Following on from my comment in the February issue of DW, I thought it worthwhile to spend a few moments contemplating just how much, or how little, companies (as well as individuals) can learn from the history books. Thankfully, major health scares as per the current coronavirus pandemic, are few and far between. Nevertheless, they do occur. And organisations might do well to spend a little time, after we have weathered the storm, thinking about the future, in terms of the human aspect of their business. Yes, AI and robots are, we hope, immune to illness, but humans are still a vital part of any organisation, and large scale illness is something that might just need a bit more planning for in the future. At least as part of a business continuity/disaster recovery plan, where the emphasis tends to be on the machines, not the humans.

Financially, there are always peaks and troughs in economies – both individual ones and the global one. It is difficult to predict these – else all economists would nearly always be in agreement – something which they rarely are! So, it’s difficult to plan for them. However, the current situation might just encourage more businesses to keep more money in reserve, ‘just in case’. And, just maybe, shareholders will not be quite so demanding for seemingly endless, increasing dividends if it leaves the company coffers bare when recession or unforeseen events threaten to derail the economic landscape.

More generally, students of history can tell anyone who cares to listen, that history really does have a habit of repeating itself. Unfortunately, there is much uncertainty as to when these repetitions might occur, but re-occur they do.

For example, there will always be threats to national, regional and global security. The 20^th century was rather volatile on this front. And when the Communist empire finally collapsed and everyone imagined eternal, global peace, along came the terrorist threat from the Middle East. More recently, cyber warfare would appear to be a growing concern.

Add to this environmental stresses – politicial commentators love to say that many of the wars of the future will be fought over water (access to it/the lack of it in certain locations); other aspects of climate change – denied by the most powerful leader in the world (!); the odd health scare; the ongoing migration crises in parts of the world, and various other events, all of which have been witnessed previously in some shape or form (yes, even climate change has had a major impact over the years), and the future is not so certain.

Of course, planning for a series of ‘might happens’ is not that easy, but maybe, just maybe, the present global crisis will serve as a timely warning for countries, corporations and individual citizens that we can no longer just keep our heads down, stick our fingers firmly in our ears (having washed them well beforehand!) and carry on regardless.

Integration challenges continue to slow down digital transformation initiatives, as many organisations don’t have a company-wide API strategy.

MuleSoft has published the findings of the 2020 Connectivity Benchmark Report on the state of IT and digital transformation. The global survey of 800 IT decision makers (ITDMs) in organisations with at least 1,000 employees revealed that more than half (59%) of organisations were unable to deliver on all their projects last year, creating a backlog for 2020.

• IT departments are having to innovate faster but with fewer resources: Half of respondents say their IT budgets will increase by less than 10% this year, despite their project workloads skyrocketing by 40% (up from 33% last year).
• IT faces the constant balancing act of keeping the lights on and innovating: Nearly 70% of IT’s time is spent running the business, instead of focusing on innovation and development.
• Organisations struggle to maintain the pace of our digital world: Nearly two-thirds (64%) of ITDMs find it difficult to introduce new technologies because of their IT infrastructures.

“Businesses are under increasing pressure to digitally transform as a failure to do so risks negatively impacting revenues.  However, traditional IT operating models are broken, forcing organisations to find new ways of accelerating project delivery and reusing integrations,” said Ian Fairclough, vice-president of services, EMEA at MuleSoft. “For organisations with hundreds of different applications, integration remains a significant challenge towards them being able to deliver the connected experiences customers strive for. This report highlights that while ITDMs recognise the value of APIs, many organisations have yet to fully realise their potential.”

Businesses are failing to capture the full value of APIs without a company-wide strategy: The vast majority of organisations understand the power and potential of APIs – 80% currently use public or private APIs. However, very few have developed a strategic approach to enabling API usage across the business.

• Company-wide API strategies are necessary to drive true value and reuse: Only 12% of organisations (9% in the UK) are mandated by leadership to abide by a company-wide API integration strategy for all projects. More than half (54%) implement APIs on a project-by-project basis or use a strategy that’s siloed to certain parts of the business.
• Businesses don’t have an easy way to share APIs: Less than half (42%) of internal software assets and components (e.g., code, APIs, best practice templates) are available for developers to reuse - this figure falls to just over a third (34%) in the UK. Most organisations (80%) also don’t have an effective way to share APIs or integrations. - in the UK this figure rises to 96%,
• Citizen integrators lack critical internal resources: 70% of ITDMs say they have a good strategy in place to enable non-technical business users to easily integrate apps and data sources with APIs. Despite this high level of confidence, 67% don’t have a team that’s dedicated to driving the sharing and reuse of APIs - in the UK only half of organisations have a dedicated team.
• New business users are emerging, amplifying the need for reuse: Outside of IT, the top three business roles with integration needs include business analysts (40%), data scientists (38%) and customer support (38%).

API reuse is directly linked to speed of innovation, operational efficiency and revenue 

By establishing API strategies that promote self-service and reuse, businesses put themselves in a much better position to innovate at speed, increase productivity and open up new revenue streams. However, only 42% of ITDMs (30% in the UK) are leveraging APIs to increase the efficiency of their application development processes.

• Most organisations are making integration harder on themselves by not designing APIs for reuse: Among organisations that leverage APIs, 52% use them as part of the development process for new projects and 52% use them to build integrations. However, less than half (46%) say their APIs are reusable, highlighting outsize opportunity for IT to be more effective in the delivery process. 
• Organisations aren’t activating API ecosystems: Only 26% of organisations are driving innovation with partner and external developer ecosystems by exposing APIs to third parties.
• When designed with intent, APIs drive business outcomes: Organisations using APIs benefit from operational improvements such as increased productivity (54%), increased innovation (47%) and greater cross-team agility for self-serve IT (46%).
• APIs are the new revenue stream: On average, nearly a third (31%) of businesses’ revenue is being generated by APIs or by API-related implementations.

“CIOs are uniquely positioned to lead their organization’s digital transformation. IT leaders across all industries must be focused on creating a new operating model that accelerates the speed of delivery, increases organizational agility and delivers innovation at scale,” said Simon Parmett, CEO, MuleSoft. “With an API-led approach, CIOs can change the clock speed of their business and emerge as the steward of a composable enterprise to democratize access to existing assets and new capabilities.”

Study highlights lack of investment in areas most likely to have a positive CX impact.

Organizations must tear down the walls between IT and the business and make more customer-centric investments if they are to improve customer experience (CX), according to new research from Pegasystems Inc.. Pega’s 2020 Global Customer Experience Study was conducted among decision makers spanning 12 countries and seven different industries by research firm Savanta.

The study highlighted four key pain points businesses must address if they are to provide a better, more personalized customer experience, successfully differentiate themselves from the competition, and improve customer satisfaction and loyalty:

• Walls between IT and the business are causing CX confusion: The research revealed thatIT is twice as likely to lead CX business initiatives than any other group. Twenty-six percent of CX projects are currently led by IT, 13% by dedicated CX functions, 12% by general management, 11% by marketing, and just 7% by retention and loyalty specialists. While IT is critical to supporting these projects, problems can arise when they are forced to make business decisions that require those in other departments – many of whom are measured by a different set of metrics – to buy in and adopt a new approach, or a new technology solution. It’s a key reason why 81% of respondents cited ‘people issues’, including a lack of skills, sponsorship, adoption, and org structure among their top four CX challenges.

• Where are all the C-level sponsors? Only 35% of businesses have a C-level sponsor for CX initiatives, and in 36% of companies these projects are led at director level or below. This can result in a lack of expertise, leadership, and awareness in the initiatives themselves and can also cause those working on them to question the organization’s commitment to CX. By contrast, the involvement of C-level sponsors can in itself break down the walls between IT and the business and accelerate change.

• Lack of investment in the most relevant channels: Sixty-eight percent of companies say their channel focus is determined by the needs of their customers, but their actions say otherwise. Respondents named email (43%) and digital ads (42%) as their top two channel investments for next year – both of which are considered to be channels with increasingly lower customer response rates. By contrast, only 28% said they were planning to invest in chatbots, and 26% planned to invest in inbound contact centers, suggesting a focus on prioritizing short-term outbound gains instead of focusing on the inbound channels customer most typically want to use to communicate.

• Reliance on outdated analytics: While analytics software evolves at lightning speed, the study found too many outdated and less effective analytics solutions still in use. For example, one quarter or more still rely on customer journey mapping (27%) or micro-segmentation (25%), while nearly one in five (19%) still perform arduous A/B testing. Even more telling is that use of customer-centric analytics that can really jumpstart CX, such as propensity modeling (37%), customer lifetime value projection (34%), or performance simulation (33%), are still far from prevalent.

Infoblox has published new research that exposes the significant threat posed by shadow IoT devices on enterprise networks. The report, titled “What’s Lurking in the Shadows 2020” surveyed 2,650 IT professionals across the US, UK, Germany, Spain, the Netherlands and UAE to understand the state of shadow IoT in modern enterprises.

Shadow IT devices are defined as IoT devices or sensors in active use within an organisation without IT’s knowledge. Shadow IoT devices can be any number of connected technologies including laptops, mobile phones, tablets, fitness trackers or smart home gadgets like voice assistants that are managed outside of the IT department. The survey found that over the past 12 months, a staggering 80% of IT professionals discovered shadow IoT devices connected to their network, and nearly one third (29%) found more than 20.

The report revealed that, in addition to the devices deployed by the IT team, organisations around the world have countless personal devices, such as personal laptops, mobile phones and fitness trackers, connecting to their network. The majority of enterprises (78%) have more than 1,000 devices connected to their corporate networks.

“The amount of shadow IoT devices lurking on networks has reached pandemic proportions, and IT leaders need to act now before the security of their business is seriously compromised,” said Malcolm Murphy, Technical Director, EMEA at Infoblox.

“Personal IoT devices are easily discoverable by cybercriminals, presenting a weak entry point into the network and posing a serious security risk to the organisation,” he added. “Without a full view of the security policies of the devices connected to their network, IT teams are fighting a losing battle to keep the ever-expanding network perimeter safe.”

Nearly nine in ten IT leaders (89%) were particularly concerned about shadow IoT devices connected to remote or branch locations of the business.

“As workforces evolve to include more remote and branch offices and enterprises continue to go through digital transformations, organisations need to focus on protecting their cloud-hosted services the same way in which they do at their main offices,” the report recommends. “If not, enterprise IT teams will be left in the dark and unable to have visibility over what’s lurking on their networks.”

To manage the security threat posed by shadow IoT devices to the network, 89% of organisations have introduced a security policy for personal IoT devices. While most respondents believe these policies to be effective, levels of confidence range significantly across regions. For example, 58%of IT professionals in the Netherlands feel their security policy for personal IoT devices is very effective, compared to just over a third (34%) of respondents in Spain.

“Whilst it’s great to see many organisations have IoT security policies in place, there’s no point in implementing policies for their own sake if you don’t know what’s really happening on your network,” Murphy said. “Gaining full visibility into connected devices, whether on premises or while roaming, as well as using intelligent systems to detect anomalous and potentially malicious communications to and from the network, can help security teams detect and stop cybercriminals in their tracks.”

Global research highlights how organisations are capitalising on emerging technologies to enhance finance and operations for competitive advantage.

Organisations that are adopting Artificial Intelligence (AI) and other emerging technologies in finance and operations are growing their annual profits 80 percent faster, according to a new study fromEnterprise Strategy Group and Oracle. The global study,Emerging Technologies: The competitive edge for finance and operations, surveyed 700 finance and operations leaders across 13 countries and found that emerging technologies – AI,Internet of Things(IoT),blockchain,digital assistants– have passed the adoption tipping point, exceed expectations, and create significant competitive advantage for organisations.

AI and Digital Assistants Improve Accuracy and Efficiency in Finance

Organisations embracing emerging technologies in finance are experiencing benefits far greater than anticipated:

• Errors in finance organisations have been reduced by 37 percent on average.
• 72 percent of organisations using AI have a better understanding of overall business performance.
• 83 percent of executives believe AI will completely automate financial close processes within the next five years.
• Digital assistants increase productivity by 36 percent and accelerate financial analysis by 38 percent.

AI, IoT, and Blockchain Drive More Responsive Supply Chains

AI, IoT, blockchain and digital assistants are helping organisations improve accuracy, speed and insight in operations and the supply chain, and respondents expect additional business value as blockchain applications become mainstream.

• Organisations using AI in their supply chains have seen order fulfillment reduction by an average of 6.7 business days.
• Applying IoT data to supply chain processes helps organisations reduce fulfillment errors by 26 percent on average.
• AI is helping organisations reduce fulfillment errors by 25 percent, stock-outs by 30 percent, and manufacturing downtime by 26 percent.
• Organisations using digital assistants in their supply chains have increased employee productivity by 28 percent and the speed of analysis by 26 percent.
• 87 percent of organisations using blockchain have achieved or exceeded ROI expectations; 82 percent expect to see significant business value within the next year.
• 78 percent of executives believe the ability to verify supply chain monitoring with blockchain will reduce incidents of fraud in their supply chain by 50 percent or more over the next five years.
• 68 percent of respondents see increased business intelligence as a key advantage of emerging technology in supply chain operations.

Emerging Tech Equals Competitive Advantage

The vast majority of organisations have now adopted emerging technologies and early adopters (those using three or more solutions) are seeing the greatest benefit and are more likely to outperform competitors.

• Emerging technologies have become mainstream and 84 percent of organisations are using at least one of these technologies (AI, IoT,blockchain,digital assistants)in production.
• 82 percent of organisations using three or more emerging technologies are ahead of competitors, compared to only 45 percent of organisations using none.
• Organisations using multiple emerging technologies are 9.5 times more likely to have market-leading financial and operational accuracy.
• Organisations are 2-3 times more likely to purchase prebuilt emerging technology solutions than build them on their own (percentage varies depending on technology solution).
• Almost all respondents (91 percent) considered SaaS applications to be a key enabler of emerging technology.

A study from Juniper Research has found that total operator-billed revenue from 5G IoT connections will reach $8 billion by 2024; rising from $525 million in 2020. This is a growth of over 1,400% over the next 5 years. The report identified the automotive and smart cities sectors as key growth drivers for 5G adoption over the next five years.

The new research, 5G Networks in IoT: Sector Analysis & Impact Assessment 2020-2025, anticipated that revenue from these 5G connections is a highly sought-after new revenue stream for operators. It forecasts that 5G IoT connections must be considered as new connections that will not cannibalise existing operator connectivity revenue from current IoT technologies.

For more insights on 5G on the IoT, download our free whitepaper: 5G ~ The 5-Year Roadmap.

5G Value-Added Services Key for Operators

The research urges operators to develop comprehensive value-added services to enable IoT service users to manage their 5G connections. It forecasts that tools, such as network slicing and multi-access edge computing solutions, will be essential to attract the highest spending IoT service users to use their 5G networks.

The research also forecasts that valued-added services will become crucial in the automotive and smart cities sectors. It also forecasts that these sectors would account for 70% of all 5G IoT connections by 2025, with higher than anticipated levels of device support for 5G radios accelerating the uptake of 5G connectivity.

Maximising the New Revenue Stream

The research claimed that the initial high pricing of 5G connectivity in the IoT sector would dissuade all but high value IoT users. It urged operators to roll out holistic network management tools that complement the enhanced capabilities of 5G networks for IoT capabilities.

Research author Andrew Knighton remarked “Management tools for the newly-enabled services are key for users managing large scale deployments. We believe that only 5% of 5G connections will be attributable to the IoT, but as these are newly enabled connections, operators must view them as essential to securing a return on their 5G investment”.

New data from Extreme Networks reveals that IoT is barreling toward the enterprise, but organisations remain highly vulnerable to IoT-based attacks.

The report, which surveyed 540 IT professionals across industries in North America, Europe, and Asia Pacific, found that 84% of organisations have IoT devices on their corporate networks. Of those organisations, 70% are aware of successful or attempted hacks, yet more than half do not use security measures beyond default passwords. The results underscore the vulnerabilities that emerge from a fast-expanding attack surface and enterprises’ uncertainty in how to best defend themselves against breaches.

Key findings include:

● Organisations lack confidence in their network security: 9 out of 10 IT professionals are not confident that their network is secured against attacks or breaches. Financial services IT professionals are the most concerned about security, with 89% saying they are not confident their networks are secured against breaches. This is followed by the healthcare industry (88% not confident), then professional services (86% not confident). Education and government are the least concerned of any sector about their network being a target for attack.

● Enterprises underestimate insider threats: 55% of IT professionals believe the main risk of breaches comes mostly from outside the organisation and over 70% believe they have complete visibility into the devices on the network. But according to Verizon’s 2019 Data Breach Investigations Report, insider and privilege misuse was the top security incident pattern of 2019, and among the top three causes of breaches.

● Europe’s IoT adoption catches up to North America: 83% of organisations in EMEA are now deploying IoT, compared to 85% in North America, which was an early adopter. Greater IoT adoption across geographies is quickly expanding the attack surface.

● Skills shortage and implementation complexity cause NAC deployments to fail: NAC is critical to protect networks from vulnerable IoT devices, yet a third of all NAC deployment projects fail. The top reasons for unsuccessful NAC implementations are a lack of qualified IT personnel (37%), too much maintenance cost/effort (29%), and implementation complexity (19%).

● SaaS-based networking adoption grows: 72% of IT professionals want network access to be controlled from the cloud. This validates 650 Group’s prediction that more than half of enterprise network systems will transition to SaaS-based networking by the end of 2023.

Extreme provides the multi-layered security capabilities that modern enterprises demand, from the wireless and IoT edge to the data centre, including role-based access control, network segmentation and isolation, application telemetry, real-time monitoring of IoT, and compliance automation. As the mass migration of business systems to the cloud continues, cloud security becomes ever more important. Extreme’s security solutions extend in lockstep with the expanding network perimeter to harden enterprises’ environments both on-premises and in the cloud.

Ivanti has released survey results highlighting the challenges faced by IT organisations when it comes to aligning their IT Service Management (ITSM) and IT Asset Management (ITAM) processes

According to data collected by Ivanti, 43% of IT professionals surveyed reported using spreadsheets as one of their resources to track IT assets. Further, 56% currently do not manage the entire asset lifecycle, risking redundant assets, potentially creating a risk, and causing unnecessary and costly purchases.

Findings from the survey demonstrate the need for greater alignment between ITSM and ITAM processes, especially when looking at the time spent reconciling inventory/assets. Nearly a quarter of respondents reported spending hours per week on this process. Another time-intensive process for IT professionals is dealing with out-of-warranty/out-of-support-policy assets, with 28% of respondents reporting they spend hours per week supporting these assets. And, when asked how often they have spent time fixing devices that were later identified to still be under warranty, 50% of respondents said “sometimes.”

“It’s clear that there is room for improvement when it comes to managing assets,” said Ian Aitchison, senior product director at Ivanti. “While IT teams are starting to better track their assets, collaborating with other teams and understanding the benefits of combining asset and service processes, time and money advantages are being lost as they don't have the data they need to effectively manage and optimise their assets and services.”

When asked about the benefits of combining ITSM and ITAM processes, the survey found that respondents expected to see:

• Better visibility of their IT estate: 63%
• Increased IT staff productivity: 59%
• Optimised costs: 54%
• Improved service delivery: 53%

Aitchison added, “When ITSM and ITAM are closely aligned and integrated, many activities and processes become more automated, efficient and responsive, with fewer things ‘falling through the cracks.’ IT teams gain more insight and are better positioned to move from reactive activities to more proactive practices, delivering higher service levels and efficiency at lower costs.”

According to the survey, IT professionals are also somewhat dissatisfied with the available asset information, or data, they have access to within their organisations. When asked if they incorporate and monitor purchase data, contracts and/or warranty data as part of their IT asset management program, 39% of respondents said yes, 42% said partially and 19% said no. This means more than 60% of IT professionals are missing key information in their IT asset management program to effectively manage their IT assets from cradle to grave.

Over 80% of C-Suite, business and IT decision makers believe that digital performance is critical to business growth.

Riverbed has published the results of a survey which found that over 70% of C-Suite decision makers believe business innovation and staff retention are driven by improved visibility into network and application performance. The survey findings unveiled in the ‘Rethink Possible: Visibility and Network Performance – The Pillars of Business Success’ report cites a positive correlation between effective technology and company health, a finding that is supported by the fact that 86% of C-Suite and IT decision makers (ITDMs), and 87% of business decision makers (BDMs), reveal that digital performance is increasingly critical to business growth.

Slow running systems and a lack of visibility

Three-quarters of all groups surveyed have felt frustrated by current network performance, with IT infrastructure being given as the key reason for the poor performance. This problem is exacerbated by a lack of full and consistent visibility, as one in three ITDMs report that they don’t have full visibility over applications, their networks and/or end-users. Furthermore, almost half of the C-Suite (49%) believe that slow running and outdated technology is directly impacting the growth of their businesses. This highlights the importance of implementing new technology to drive productivity, creativity and innovation.

Business priorities and challenges

Business priorities and challenges are evolving, so the technology they rely on needs to advance too. Over three quarters (76%) of ITDMs acknowledge that their IT infrastructure will have to change dramatically in the next five years to support new ways of doing business. A further 95% of all respondents recognise that innovation and breaking boundaries is crucial to business success, emphasising the need to embrace new technology. This may be why 80% of BDMs and 77% of the C-Suite say they believe that investing in next-generation technology is vital.

Commenting on the research findings, Colette Kitterhing, Senior Director UK&I at Riverbed Technology, said: “All leaders recognise that visibility, optimised network infrastructure, and the ability to accelerate cloud and SaaS performance are the next frontier in business success. Given this, it’s time the C-Suite, business decision leaders, and IT decision makers come together to invest in the right solutions, prioritise measurement, and place visibility and infrastructure at the top of their agenda.”

Kitterhing continued: “At Riverbed, we are helping businesses evolve their capabilities, whether it’s by monitoring networks and the apps that run on them, application performance, or updating the network infrastructure that underpins their digital services. We fundamentally believe this is the key to supporting our customers’ staff and their ambitions, driving innovation, creativity, and helping them Rethink what’s possible.”

Rethink Possible: Evolving the Digital Experience

With over 80% of all leaders (82% C-Suite, 84% BDMs and ITDMs) agreeing that businesses must rethink what’s possible to survive in today’s unpredictable world, technology needs to be an enabler in the process. Riverbed’s portfolio of next-generation solutions is giving customers across the globe the visibility, acceleration, optimization and connectivity that maximizes performance and visibility for networks and applications.

FireMon has released its 2020 State of Hybrid Cloud Security Report, the annual benchmark of the cloud security landscape.

The latest report finds that while enterprises rapidly transition to the public cloud, complexity is increasing, but visibility and team sizes are decreasing while security budgets remain flat to pose a significant obstacle to preventing data breaches. The 2020 State of Hybrid Cloud Security Report features insights from more than 500 respondents, including 14 percent from the executive ranks, detailing cloud security initiatives in the era of digital transformation.

“As companies around the world undergo digital transformations and migrate to the cloud, they need better visibility to reduce network complexity and strengthen security postures,” said Tim Woods, VP of Technology Alliances for FireMon. “It is shocking to see the lack of automation being used across the cloud security landscape, especially in light of the escalating risk around misconfigurations as enterprises cut security resources. The new State of Hybrid Cloud Security Report shows that enterprises are most concerned about these challenges, and we know that adaptive and automated security tools would be a welcomed solution for their needs.”

Cloud Adoption, Complexity and Scale Create Security Challenges

While enterprises increasingly transition to public and hybrid cloud environments, their network complexity continues to grow and create security risks. Meanwhile, they are losing the visibility needed to protect their cloud systems, which was the biggest concern cited by 18 percent of C-suite respondents, who now also require more vendors and enforcement points for effective security.

The 2020 FireMon State of Hybrid Cloud Security Report found that:

(Consider) Business acceleration outpaces effective security implementations:

Need for Security Outpaces the Need for Data Protection: Nearly 60 percent believed their cloud deployments had surpassed their ability to secure the networks in a timely manner. This number was virtually unchanged from 2019, showing no improvement against a key industry progress indicator.

Cloud Complexity Increases: The number of vendors and enforcement points needed to secure cloud networks are also increasing; 78.2 percent of respondents are using two or more enforcement points. This number increased substantially from the 59 percent using more than two enforcement points last year. Meanwhile, almost half are using two or more public cloud platforms, which further increases complexity and decreases visibility.

Shrinking Budgets and Security Teams Create Gaps in Protection

Despite increasing cyberthreats and ongoing data breaches, respondents also reported a substantial reduction in their security budgets and teams from 2019. These shrinking resources are creating gaps in public cloud and hybrid infrastructure security.

Budget Reductions Increase Risk: There was a 20.7 percent increase in the number of enterprises spending less than 25 percent on cloud security from 2019; 78.2 percent spend less than 25 percent on cloud security (vs. 57.5 percent in 2019). Meanwhile, 44.8 percent of this group spent less than 10 percent of their total security budget on the cloud.

Security Teams are Understaffed and Overworked: While the cyberattack surface and potential for data breaches continues to expand in the cloud, many organisations trimmed the size of their security teams – 69.5 percent had less than 10-person security teams (compare to 52 percent in 2019). The number of 5-person security teams also nearly doubled with 45.2 percent having this smaller team size versus 28.5 percent in 2019.

Lack of Automation and Third-Party Integration Fuels Misconfigurations

While cloud misconfigurations due to human-introduced errors remain the top vulnerability for data breaches, an alarming 65.4 percent of respondents are still using manual processes to manage their hybrid cloud environments. Other key automation findings included:

Misconfigurations are Biggest Security Threat: Almost a third of respondents said that misconfigurations and human-introduced errors are the biggest threat to their hybrid cloud environment. However, 73.5 percent of this group are still using manual processes to manage the security of their hybrid environments.

Better Third-Party Security Tools Integration Needed: The lack of automation and integration across disparate tools is also making it harder for resource-strapped security teams to secure hybrid environments. As such, 24.5 percent of respondents said that not having a “centralised or global view of information from their security tools” was their biggest challenge to managing multiple network security tools across their hybrid cloud.

By harnessing automated network security tools, robust API structures and public cloud integrations, enterprise can gain real-time control across all environments to minimise challenges created by manual processes, increasing complexity and reduced visibility. Automation is also the antidote to shrinking security budgets and teams by enabling organisations to maximise resources and personnel for their most strategic uses.

SolarWinds has released the findings of its “2019 Trends in Managed Services” report, showing the health of managed services and the forces shaping the market across North America and Europe.

SolarWinds MSP partners with The 2112 Group to create the annual report from findings based on data from its benchmarking tool, MSP Pulse, which provides deep insight into potential growth opportunities for IT providers and where they can excel, areas for improvement, and can also help guide how they invest in their business. The comprehensive reports show comparisons on revenue, profit, service selection, sales capacity, customer engagement, and growth potential.

“Our research has revealed that 97% of respondents offer some form of managed services—which is a clear demonstration of the managed services transformation remaking the technology channel. It’s safe to say the state of managed services is strong,” said John Pagliuca, president of SolarWinds MSP. “This latest research also underscored the major growth opportunities we’re already looking to help our MSPs leverage including automation, security, and operations. With our customer success-focused initiatives like MSP Pulse, the MSP Institute, and Empower MSP, we’re supporting partners with much more than technology. We’re working to fuel the success of our MSPs in 2020 and beyond. The future looks bright for those who want to expand their comfort zone, and we’re here to help them do just that.”

The results show that MSPs are comfortable with the security basics such as antivirus, backup, and firewalls:

For solutions in North America, respondents were most comfortable offering and using antivirus (89%), firewalls (83%), data backup and recovery (81%), and endpoint security (75%).

In Europe, respondents were most comfortable offering and using antivirus (93%), data backup and recovery (82%), firewalls (82%), and antispam (80%) as solutions.

However, MSPs have room for growth in some of the more advanced security solutions and offerings, as respondents were less confident in the more complex controls:

European and North American respondents selected the same top three solutions they were least comfortable with: biometrics, cloud access security brokers (CASBs), and digital rights management.

On the services end, European respondents were least comfortable with penetration testing (52%), auditing and compliance management (39%), and risk assessments (36%). North American respondents were least comfortable with auditing and compliance management (53%), penetration testing (47%), and security system architecture (39%).

The results also showed MSPs are starting to increase the use of automation to handle day-to-day tasks such as patch management and backup, but don’t feel comfortable with automating the advanced tasks:

Automation saves North American MSPs an average of 15.6 full-time employee hours per week and in Europe, an average of 23 full-time employee hours per week.

In North America, respondents were least comfortable automating client onboarding (44%) with identity and access management in second place (38%). In Europe, respondents were least comfortable automating SQL query workflows (57%) but shared their discomfort with automating identity and access management with their North American counterparts.

In the 2018 report, MSPs were losing customers almost as fast as they gained them, but 2019 showed an improvement in customer retention. Two of the top three reasons for losing customers stemmed from the customer rather than the service provider:

In North America, respondents pick up an average of four clients every three months while losing one in the same period.

In Europe, respondents pick up an average of three clients every two months while losing more than one on average in the same period.

Top causes of customer loss included the company either went out of business (26% in North America and 16% in Europe) or were fired by the partner (25% in North America and 16% in Europe).

Another key finding showed core business operations are still amongst the biggest growth obstacles for MSPs including lack of resources/time, sales, and marketing:

North American MSPs claimed their biggest obstacles toward growth were sales (43%), lack of resources/time (42%), and marketing (26%).

European MSPs claimed their biggest obstacles toward growth were lack of resources/time (41%), sales (32%), and security threats (32%).

Many providers claim a lack of sales and marketing expertise is a major anchor on their growth—hiring specialized staff could help close the gap or training for existing employees. The SolarWinds MSP Institute, a learning portal within the SolarWinds Customer Success Center, gives SolarWinds partners access to a variety of courses, including sales and marketing tracks, to help coach users in the management and growth of their business, and provides go-to-market strategies and advice to help educate on these issues.

“We’re so pleased to be partnering with SolarWinds MSP again on the annual Trends in Managed Services report,” stated Larry Walsh, CEO of The 2112 Group. “It gives MSPs an opportunity to see where the IT channel currently stands, where the opportunities are for growth and where they could improve as it pinpoints the crucial trends shaping the managed services market.”

A strong Q4 ensured that the four largest colocation FLAP markets of Frankfurt, London, Amsterdam and Paris reached a combined 201MW in take-up for 2019 – a new European record, according to the Q4 European Data Centres Report from CBRE, the world's leading data centre real estate advisor.

CBRE analysis shows that Frankfurt was the star performer during 2019, responsible for 45% of the 201MW. This is both the first time Frankfurt has topped the annual rankings and the first time any European market has seen 90MW of take-up in a single year. Furthermore, half of the pre-let capacity in facilities under construction also took place in Frankfurt.

CBRE expects that take-up in the core FLAP markets will remain high at 200MW for the next two years as the hyperscale companies continue to utilise wholesale colocation services in these markets.

The developer-operators, confident of prolonged strong demand, are now building at a larger scale than ever before to secure these requirements. To this end, there was 314MW of new capacity brought online in the FLAP markets during 2019, equating to a 24% market growth This new capacity would require EUR 2.2bn of capital to deliver.

Mitul Patel, Head of EMEA Data Centre Research at CBRE, commented: “The data centre market in Europe continues to grow like no other and represents one of the most exciting asset classes anywhere. The 201MW procured in 2019, as well as pre-lets and optioned capacity, is a remarkable achievement. Our expectation is that this level of activity will continue as the hyperscalers continue their accelerated procurement of data centre services.”

“As the hyperscale companies roll out their services more widely across Europe, the rate of hyperscale procurement of data centre capacity in other European cities will increase rapidly. As a result, markets such as Madrid, Milan, Warsaw and Zurich, will witness a substantial increase in colocation activity.”

The third annual Verizon Mobile Security Index takes a deep dive into the state of mobile security, looking at different types of threats and offering tips to protect your environment.

The third annual Verizon Mobile Security Index finds that a large number organizations are still compromising mobile security to get things done, which can leave entities at risk. About four out of 10 respondents (43 percent) reported their organization had sacrificed mobile security in the past year. Those that did were twice as likely to suffer a compromise.

In fact, the study found that 39 percent of respondents reported having a mobile-security-related compromise. Sixty-six percent of organizations that suffered a compromise called the impact “major,” and 55 percent said the compromise they experienced had lasting repercussions.

“In today’s world, mobile connectivity is more important than ever. Organizations of all sizes and in all industries rely on mobile devices to run much of the day to day operations, so mobile security is a priority,” said Bryan Sartin, executive director, global security services with Verizon. “The types of devices, diverse applications and further emergence of IoT devices further complicate security. Everyone has to be deliberate and diligent about mobile security to protect themselves and their customers.”

Because mobile attacks aren’t industry specific, this year’s Verizon Mobile Security Index 2020 features supplemental vertical reports in key segments including: financial services; healthcare; manufacturing; public sector; retail and small and medium business. The report also discusses the importance of mobile security in pivotal technologies like cloud and IoT and how the emergence of 5G will impact security. And with 80 percent of organizations saying that mobile will be their primary means of accessing cloud services within five years, now is the time to hone in on mobile security.

The obvious question is: what should organizations do? The report highlights users, apps, devices and networks as the four key mobile attack vectors. The report includes a number of tips on how organizations can safeguard against mobile security threats, including establishing a “security-first” focus, developing and enforcing policies and encrypting data over unsecured networks.

Survey snapshots:

• Five of six (83 percent) respondents answered 8 or higher when asked to rate how crucial mobile is to the smooth running of their business (on a 10 point scale)
• 43 percent of organizations sacrificed security, with the top reasons being time (62 percent), convenience (52 percent) and profitability targets (46 percent).
• 20 percent of organizations that suffered a mobile compromise said that a rogue or insecure Wi-Fi hotspot was involved.
• 31 percent of those surveyed admitted to having suffered a compromise involving an IoT device.
• 84 percent of organizations said that their reliance on data stored in the cloud is growing.

With a gently rising global GDP (Goldman Sachs guesses 2020 will see around 3.4% worldwide growth; Europe will be lower, at around 1.1%, with Germany, Italy and the UK shrinking, and issues in France) IT sales across Europe can be expected to grow. They have been rising based on enterprises seeing the need to drive efficiency and stay up with competition, plus the growing SMB turn to technology. There have been minor issues over confidence, particularly in the UK market, but the trend is up, especially towards more targeted solutions. It will place more of a burden on channels, however, as the requirement is for more sophisticated and integrated solutions, which in turn place more demands on the channel resources and its ability to successfully sell advanced ideas.

At the same time, customers are asking about the use of technologies such as AI, analytics, blockchain and the many varieties of security. Any supplier or channel player not able to talk convincingly about these technologies may find themselves displaced by an upstart.

This message was powerfully driven home by Microsoft research at the end of 2019. It reported that while customers had previously looked at using tools from Microsoft and others, organisations are “moving beyond adopting the latest applications and are developing their own proprietary digital capabilities to help propel success and gain a competitive advantage”.

According to a Gartner survey Operational excellence, Cost, Digital and Growth are top of mind of CIO plans in 2020, well above other concerns such as Cyber security, Innovation and Recruiting.

Your customers don’t want the same as everyone else; they want to build and develop their own solutions- their own intellectual property (IP) even. There's widespread agreement that the applied use of a “creative, entrepreneurial mindset to invent new digital capabilities using advanced technologies such as AI and IoT — will have a significant impact on global communities and organizational culture”.

Which is where managed services play their part in being scalable, with known fixed costs and in their ability to explore new technology introductions through pilots.

The global managed services market was valued at $166.8bn in 2018, and it is expected to reach $319.5bn by 2024, registering a CAGR of 11.5% during the forecast period of 2019-2024 says researcher Mordor Intelligence. The market for managed services is going to be fuelled by the increasing shortage of expertise with businesses becoming more technology oriented. Furthermore, due to rapid digitalization, the companies are required to continually innovate and upgrade their infrastructure to remain competitive.

Industry trade body CompTIA surveyed members in late 2019 and found general agreement on the areas of growth to 2022 as Managed Services, IoT, Data, security and consulting services. Managed services was out in front with some 82% agreeing that it was a growth engine. Some 35% expected it to grow significantly, half moderately and 14% to stay the same. 

But the issues of purchasing such services remains a source of difficulty for customers. As Gartner told the Managed Services Summits in London and Amsterdam last year, people are more tech savvy than ever before which means it’s not just the IT department that makes technology choices. And the internet now opens up many more sources of information and access to trusted sources (independent sites) which used to cost a fortune

But with all that complexity and wealth of information comes a problem; the buyer has to sort through so much information. But worse still the buyer still can’t find the right information to make a quick buying decision.

And so the buying cycle becomes fluid and sometimes erratic. The net result – buying takes 97% longer than planned as found in a Gartner survey last year. And what’s more Gartner surveys show that up to 59% of buyers cancelled at least one buying effort.

And the conversations are becoming more detailed and going to the core of the customer business: this emerged in a lively panel at the Managed Services and Hosting Summit Manchester on October 30 2019 gave some useful ideas on meeting current customer challenges: Nigel Church from MSP First Solution: “We’re speaking to more customers about digital transformation and enablement processes, and we have seen a shift, starting with conversations about cloud and then going onto business strategy.”

So the Managed Services Summits in Amsterdam and London this year aim to reflect the new pressures on MSPs to engage successfully with customers and offer new types of service, in security, in data, in newer technologies such as AI.

https://mshsummit.com/amsterdam/

Concerns that the assumptions underpinning organizational strategy may be outdated or misaligned to current growth objectives topped business leaders’ concerns in Gartner, Inc.’s latest Emerging Risks Monitor Report.

Gartner surveyed 136 senior executives across industries and geographies and the results showed that “strategic assumptions” had risen to the top emerging risk in the 4Q19 Emerging Risks Monitor survey, up from the third position the previous quarter (see Table 1). Last quarter’s top emerging risk, “digitalization misconceptions,” has now become an established risk after ranking on four previous emerging risk reports.

“This quarter saw a number of external risks converge in executives’ thinking, from increasing concerns about the impact of extreme weather events to trade policy,” said Matt Shinkman, vice president with Gartner’s Risk and Audit Practice. “Currently, however, business leaders are most acutely concerned with the beliefs underpinning their own strategic assumptions and the ramifications of getting them wrong.”

Table 1. Top Five Risks by Overall Risk Score: 1Q19-4Q19

Source: Gartner (February 2020)

Strategic Planning Must Account for Critical Uncertainties

The study defined a strategic assumption as a plan based on a belief that a certain set of events must occur. Gartner research has found that executives believe more than half of their time spent in strategic planning is wasted, and the quality of those plans fail to meet expectations. Incorrect strategic assumptions often result in stalled growth that can derail planned results.

“Strategic assumptions are often sound when they are first formed, but in today’s environment are more vulnerable to becoming outdated or obsolete due to a rapid increase in the pace of change,” noted Mr. Shinkman. Senior executives ranked the pace of change as a top emerging risk in the second quarter of 2019.

Organizations with a poorly formed set of strategic assumptions typically produce a high number of projects that are not aligned with their stated objectives. Moreover, time and budget for the execution of key initiatives consistently overrun planned targets.

“Risk teams should play a vital role in mitigating the impact of inaccurate strategic assumptions. A key component of clarifying strategic assumptions is discerning between likely truths and critical uncertainties,” Mr. Shinkman said. “Risk leaders should involve themselves early in the strategic planning process and add value by developing a set of criteria to stress-test assumptions and root out biases and flaws before they become cemented in a strategic plan.”

Cyber-Physical Convergence Presents New Risks

The second most cited risk was a convergence of cyber-physical risks, as previously unconnected physical assets become part of an organization’s cyber network. Nearly 90 percent of organizations with connected operational technology (OT) have already experienced a breach related to cyber-physical architecture. Despite these threats, organizations continue to move forward with integrating Internet of Things devices, smart buildings and other OT, often without dedicated security policies.

“The risks of OT are still not widely appreciated throughout most organizations,” said Mr. Shinkman. “Top risk teams partner with IT to develop dedicated strategies that account for the security deficiencies in such assets and include regular meetings to review issues such as access rights and employee training.”

Nearly 90% of organizations have a Chief Experience Officer or Chief Customer Officer

Organizations are taking customer experience (CX) more seriously by committing more resources and talent to the discipline, according to Gartner, Inc. Gartner’s 2019 Customer Experience Management Survey revealed that in 2017, more than 35% of organizations lacked a chief experience officer (CXO) or chief customer officer (CCO) or equivalents, but in 2019, only 11% and 10% lacked one or the other role, respectively (see Figure 1).

“There has been significant growth in the presence of CXOs and CCOs or equivalents in many organizations over the last two years,” said Augie Ray, VP analyst, Gartner for Marketers. “However, these roles rarely report to CMOs despite marketing taking control of more CX initiatives.”

Figure 1: Key CX Leader Roles Are More Common and Rarely Report to the CMO

Source: Gartner (February 2020)

The survey — which covered a variety of departments where CX efforts are run and supported, such as marketing, IT, customer service, operations, sales and stand-alone CX departments — found that responsibility for CX budgets and initiatives has begun to shift into the marketing department.

“As marketing continues to take on a larger role in CX, marketing leadership faces a potential challenge coordinating companywide CX,” said Ray. “CMOs and marketing leaders responsible for aspects of their organization’s CX must ensure that roles are understood, redundancy and conflict are minimized, and collaboration is prioritized.”

To do this, Gartner recommends that CMOs and marketing leaders take the following actions:

• Establish clear lines of responsibility and authority for everyone involved in CX: The presence of many departments and leaders contributing to CX demands strong cross-functional governance. To avoid different leaders managing conflicting CX strategies and programs, ensure all CX leaders meet regularly, share information, discuss common problems, evaluate duplicative vendors and efforts, and avoid confusion over accountability.

• Broaden marketing measures: For marketing to successfully take and sustain a larger role in enterprisewide CX, marketers must consider the comparative weight given to their short- and long-term metrics. Committing time and resources to CX efforts should go beyond short-term goals such as awareness, inbound traffic and conversion, but also long-term outcomes such as improved customer satisfaction, reduced churn, increased lifetime value and great referral volume. Define goals not just by internal expectations, but by how customers define success. Establish metrics and KPIs around efforts that meet customer expectations. 

Retailers to establish Robot Resource Organizations to manage nonhuman workers

Gartner, Inc. predicts that by 2025, at least two of the top 10 global retailers will establish robot resource organizations to manage nonhuman workers.

“The retail industry continues to transform through a period of unprecedented change, with customer experience as the new currency,” said Kelsie Marian, senior research director at Gartner. “The adoption of new digital technologies and the ever-changing expectations of customers continues to challenge traditional retailers, forcing them to investigate new-human hybrid operational models, including artificial intelligence (AI), automation and robotics.”

Gartner research shows that 77% of retailers plan to deploy AI by 2021, with the deployment of robotics for warehouse picking as the No. 1 use case. Warehouse picking involves smart robots working independently or alongside humans. In the future, retailers will establish units within the organization for procuring, maintaining, training, taxing, decommissioning and proper disposal of robot resources. In addition, they will create the governance required to ensure that people and robots can effectively collaborate.

Many retail workers want to use AI specifically as an on-demand or predictive assistant, meaning the robot will need to work alongside humans. “This means the robot will have to “mesh” with the human team — essentially meaning that both sides will need to learn how to “collaborate” to operate effectively together,” said Ms. Marian.

An example is an autonomous robotic kitchen assistant that learns an operator’s specific recipes and prepares them according to the wishes of the operator. The robot can work in harmony with the operators who, in turn, are having to adapt to changing consumer tastes.

Choosing the right candidate — human and machine — for the job is critical for success. A combined effort from HR, IT and the line-of-business hiring managers will be required to identify the skills needed to ensure the pair work together effectively. “Retail CIOs must provide ongoing maintenance and monitoring performance for effectiveness. If not, the team may be counterproductive and lead to a bad customer experience,” said Ms. Marian.

The introduction of AI and robotics will likely cause fear and anxiety among the workforce — particularly among part-time workers. It will be vital for retail CIOs to work with HR and business leaders to address and manage employees’ skills and concerns; and change their mindset around the development of robot resource units.

Blockchain Smart Contracts to help improve overall data uality by 50%

Gartner, Inc. predicts that by 2023, organizations using blockchain smart contracts will increase overall data quality by 50%, but reduce data availability by 30%.

“When an organization adopts blockchain smart contracts — whether externally imposed or voluntarily adopted — they benefit from the associated increase in data quality, which will increase by 50% by 2023,” said Lydia Clougherty Jones, senior research director at Gartner.

However governance frameworks for blockchain participation, or the terms and conditions within the smart contract, can dictate the availability of the data generated from the smart contract transaction, from none to limited to unlimited. “This variable could leave participants in a worse position than if they did not participate in the blockchain smart contract process. As such, an organization’s overall data asset availability would decrease by 30% by 2023,” added Ms. Clougherty Jones.

The net impact however, is a positive result for data and analytics (D&A) ROI. The impact of blockchain smart contract adoption on analytical decision making is profound. It enhances transparency, speed and granularity of decision making. It also improves the quality of decision making, as its continuous verification makes the data more accurate, reliable and trustworthy.

“Smart contracts are important and D&A leaders should focus on them because they promise a near certainty of trusted exchange. Once deployed, blockchain smart contracts are immutable and irrevocable through nonmodifiable code, which enforces a binding commitment to do or not do something in the future. Moreover, they eliminate third-party intermediaries (e.g., bankers, escrow agents, and lawyers) and their fees, as smart contracts, perform the intermediary functions automatically,” said Ms. Clougherty Jones.

Gartner analysts recommend D&A leaders pilot blockchain smart contracts now. Organizations should start deploying them to automate a simple business process, such as non-sensitive data distribution or a simple contract formation for contract performance or management purposes. Then organizations should engage with their affiliates and partners to pilot blockchain smart contracts to automate multiparty contracts within a well-defined ecosystem, such as banking and finance, real estate, insurance, utilities, and entertainment.

Privacy Compliance Technology to rely on AI

Over 40% of privacy compliance technology will rely on artificial intelligence (AI) by 2023, up from 5% today, according to Gartner, Inc.  

“Privacy laws, such as General Data Protection Regulation (GDPR), presented a compelling business case for privacy compliance and inspired many other jurisdictions worldwide to follow,” said Bart Willemsen, research vice president at Gartner.

“More than 60 jurisdictions around the world have proposed or are drafting postmodern privacy and data protection laws as a result. Canada, for example, is looking to modernize their Personal Information Protection and Electronic Documents Act (PIPEDA), in part to maintain the adequacy standing with the EU post-GDPR.” 

Privacy leaders are under pressure to ensure that all personal data processed is brought in scope and under control, which is difficult and expensive to manage without technology aid. This is where the use of AI-powered applications that reduce administrative burdens and manual workloads come in.

AI-Powered Privacy Technology Lessens Compliance Headaches

At the forefront of a positive privacy user experience (UX) is the ability of an organization to promptly handle subject rights requests (SRRs). SRRs cover a defined set of rights, where individuals have the power to make requests regarding their data and organizations must respond to them in a defined time frame.

According to the 2019 Gartner Security and Risk Survey, many organizations are not capable of delivering swift and precise answers to the SRRs they receive. Two-thirds of respondents indicated it takes them two or more weeks to respond to a single SRR. Often done manually as well, the average costs of these workflows are roughly $1,400 USD, which pile up over time.

“The speed and consistency by which AI-powered tools can help address large volumes of SRRs not only saves an organization excessive spend, but also repairs customer trust,” said Mr. Willemsen. “With the loss of customers serving as privacy leaders’ second highest concern, such tools will ensure that their privacy demands are met.”

Global Privacy Spending on Compliance Tooling Will Rise to $8 Billion Through 2022

Through 2022, privacy-driven spending on compliance tooling will rise to $8 billion worldwide. Gartner expects privacy spending to impact connected stakeholders’ purchasing strategies, including those of CIOs, CDOs and CMOs. “Today’s post-GDPR era demands a wide array of technological capabilities, well beyond the standard Excel sheets of the past,” said Mr. Willemsen.

“The privacy-driven technology market is still emerging,” said Mr. Willemsen. “What is certain is that privacy, as a conscious and deliberate discipline, will play a considerable role in how and why vendors develop their products. As AI turbocharges privacy readiness by assisting organizations in areas like SRR management and data discovery, we’ll start to see more AI capabilities offered by service providers.”

International Data Corporation (IDC) has announced the release of IDC FutureScape: Worldwide Developer and DevOps 2020 Predictions — China Implications (IDC #CHC45894020, January 2020). The new IDC paper puts forward ten developer and DevOps market predictions for 2020 with a specific focus on China, as well as actionable insights for technology buyers in the DevOps market for the next five years.

Nan Wang, Senior Market Analyst, Enterprise System and Software Research, IDC China, said that the study provides a well-rounded analysis and discussion of developer and DevOps market trends and their impact on digital transformation and business’ technology departments.

“Senior IT leaders and business executives will come away from this report with guidance for managing the implications of these technologies,” she said. “Furthermore, our market predictions will help inform their IT investment priorities and implementation strategies.”

IDC’s Top10 developer and DevOps market predictions for 2020 are as follows:

1. Enhanced AI optimizations for developers

By 2024, 56% of companies will not confine their artificial intelligence (AI)/machine learning (ML) use to application development. By then, close to 10% of AI/ML-based optimizations will focus on software development, design, quality management, security and deployment. By 2023, 70% of companies will invest in employee retraining and development, including third-party services to meet the needs of new skills and working methods brought about by AI applications.

2. Wide use of container platforms

By 2024, 70% of new application developed with programming languages will be deployed in containers for improved deployment speed, application consistency and portability.

3. Growth of part-time developers

By 2023, the number of part-time developers (including business analysts, data analysts and data scientists) in China will be double the number in2019. Specifically, part-time developers will increase from1.8 million in 2019 to 3.6 million, representing a CAGR of 12.2%. By 2020, 15% of customer experience applications will deliver hyper-personalization through reinforcement learning algorithms continuously trained on a wide range of data and innovations.

4. DevOps as a daily activity

By 2023, the number of organizations releasing codes for specific applications will increase to 30% from 3% in 2019. By 2024, at least 90% of new versions of enterprise-grade AI applications will feature embedded AI functions, though those disruptive AI-focused applications will only account for 10% of the total market.

5. Accelerating transformation of traditional applications

By 2022, the accelerating modernization of traditional applications and the development of new applications will increase the percentage of cloud-native production applications to 25%, driven by the utilization of microservices, containers and dynamic orchestration.

6. DevOps focus on business KPIs

By 2023, 40% of DevOps teams will invest in tooling and focus on business KPIs such as cost and revenue as operations will begin to play a more important role in the performance of end-to-end applications and business impact.

7. Use of related analytical tools driven by open source software

The increasing reliance of applications on open source components has driven the rapid growth of software component analysis and related tools. By 2023, software component analysis tools, which are currently only used by a minority of organizations, will be used by 45% of organizations.

8. Companies establishing their own development ecosystems

By 2023, 60% of Chinese G2000 companies will establish their own software ecosystems, and 50% will access important reusable code components from publicly accessible community code libraries.

9. Growth of open source codebases

By 2024, open source software derived from open code libraries as a percentage of all enterprise applications will double to 25%, with the remaining75% being customized according to organizations’ business models or use scenarios.

10. Recognized applicability of DevOps

By 2024, applications that completely use DevOps will account for less than 35%. Enterprises have recognized that not all applications can benefit from the complex operations spanning development and production related to continuous integration and continuous delivery (CI/CD).

As shown by the above predictions, emerging business models and frequently changing needs for services have created challenges to enterprises’ IT infrastructure. Going forward, the ability to quickly establish new business systems and continuously respond to system iterations brought about by market changes will emerge as the top priorities for IT departments

In response to these external pressures, companies have begun adopting modern methods to implement application development, packaging and testing. In this new and challenging environment, machine learning and AI are being widely used. IDC predicts that the work of future will, to a large extent, will be influenced by how developers and the DevOps community evolve in the next five years.

Worldwide ICT spending to reach $4.3 trillion

A new forecast from International Data Corporation (IDC) predicts worldwide spending on information and communications technology (ICT) will be $4.3 trillion in 2020, an increase of 3.6% over 2019. Commercial and public sector spending on information technology (hardware, software and IT services), telecommunications services, and business services will account for nearly $2.7 trillion of the total in 2020 with consumer spending making up the remainder.

"The slow economy, weak business investment, and uncertain production expectations combined with protectionist policies and geopolitical tensions — including the US-China trade war, threats of US tariffs on EU automobiles and the EU's expected response, and continued uncertainty around the Brexit deal — are still acting as inhibitors to ICT spending across regions," said Serena Da Rold, program manager in IDC's Customer Insights and Analysis group. "On the upside, our surveys indicate a strong focus on customer experience and on creating innovative products and services driving new ICT investments. Companies and organizations across industries are shifting gears in their digital transformation process, investing in cloud, mobility, the Internet of Things, artificial intelligence, robotics, and increasingly in DevOps and edge computing, to transform their business processes."

IT spending will make up more than half of all ICT spending in 2020, led by purchases of devices (mainly mobile phones and PCs) and enterprise applications. However, when combined, the three IT services categories (managed services, project-oriented services, and support services) will deliver more than $750 billion in spending this year as organizations look to accelerate their digital transformation efforts. The application development & deployment category will provide the strongest spending growth over the 2019-2023 forecast period with a five-year compound annual growth rates (CAGR) of 11.1%.

Telecommunications services will represent more than one third of all ICT spending in 2020. Mobile telecom services will be the largest category at more than $859 billion, followed by fixed telecom services. Both categories will see growth in the low single digits over the forecast period. Business services, including key horizontal business process outsourcing and business consulting, will be about half the size of the IT services market in 2020 with solid growth (8.2% CAGR) expected for business consulting.

Consumer ICT spending will grow at a much slower rate (0.7% CAGR) resulting in a gradual loss of share over the five-year forecast period. Consumer spending will be dominated by purchases of mobile telecom services (data and voice) and devices (such as smartphones, notebooks, and tablets).

Four industries – banking, discrete manufacturing, professional services, and telecommunications – will deliver 40% of all commercial ICT spending in 2020. IT services will represent a significant portion of the spending in all four industries, ranging from 50% in banking to 26% in professional services. From there, investment priorities will vary as banking and discrete manufacturing focus on applications while telecommunications and professional services invest in infrastructure. The industries that will deliver the fastest ICT spending growth over the five-year forecast are professional services (7.2% CAGR) and media (6.6% CAGR).

More than half of all commercial ICT spending in 2020 will come from very large businesses (more than 1,000 employees), while small businesses (10-99 employees) and medium businesses (100-499 employees) will account for nearly 28%. IT services will represent a significant portion of the overall spending for both market segments – 54% for very large businesses and 35% for small and medium businesses. Application and infrastructure spending will be about equal for very large businesses while small and medium businesses will invest more in applications.

"SMBs are increasingly embracing digital transformation to take advantage of both the opportunities it presents, and the disruption it can mitigate," said Shari Lava, research director, Small and Medium Business Markets. "Digitally determined SMBs, defined as those that are making investments in digital transformation related technology, are almost twice as likely to report double-digit revenue growth versus their technology indifferent peers."

$124 billion to be spent on Smart Cities initiatives in 2020

A new forecast from the International Data Corporation (IDC) Worldwide Smart Cities Spending Guide shows global spending on smart cities initiatives will total nearly $124 billion this year, an increase of 18.9% over 2019. The top 100 cities investing in smart initiatives in 2019 represented around 29% of global spending, and while growth will be sustained among the top spenders in the short term, the market is quite dispersed across midsize and small cities investing in relatively small projects.

"This new release of IDC's Worldwide Smart Cities Spending Guide brings further expansion of our forecasts into smart ecosystems with the addition of smart ports alongside smart stadiums and campus," said Serena Da Rold, program manager in IDC's Customer Insights & Analysis group. "The Spending Guide also provides spending data for more than 200 cities and shows that fewer than 80 cities are investing over $100 million per year. At the same time, around 70% of the opportunity lies within cities that are spending $1 million or less per year. There is a great opportunity for providers of smart city solutions who are able to leverage the experience gained from larger projects to offer affordable smart initiatives for small and medium sized cities."

In 2019, use cases related to resilient energy and infrastructure represented over one third of the opportunity, driven mainly by smart grids. Data-driven public safety and intelligent transportation represented around 18% and 14% of overall spending respectively.

Looking at the largest use cases, smart grids (electricity and gas combined) still attract the largest share of investments, although their relative importance will decrease over time as the market matures and other use cases become mainstream. Fixed visual surveillance, advanced public transportation, intelligent traffic management, and connected back office follow, and these five use cases together currently represent over half of the opportunity. The use cases that will see the fastest spending growth over the five-year forecast are vehicle-to-everything (V2X) connectivity, digital twin, and officer wearables.

Singapore will remain the top investor in smart cities initiatives. Tokyo will be the second largest spender in 2020, driven by investments for the Summer Olympics, followed by New York City and London. These four cities will each see smart city spending of more than $1 billion in 2020.

On a regional basis, the United States, Western Europe, and China will account for more than 70% of global smart cities spending throughout the forecast. Latin America and Japan will experience the fastest growth in smart cities spending in 2020.

"Regional and municipal governments are working hard to keep pace with technology advances and take advantage of new opportunities in the context of risk management, public expectations, and funding needs to scale initiatives," said Ruthbea Yesner, vice president of IDC Government Insights and Smart Cities and Communities. "Many are moving to incorporate Smart City use cases into budgets, or financing efforts through more traditional means. This is helping to grow investments."

Was there ever a buzzword as buzzy as “digital transformation?” It’s perfect: It sounds futuristic and exciting, but it’s also vague enough to be used for any number of different products and services. It could be a process, a technology suite, a mindset, or all of the above. But aside from the inevitable hype that accompanies every other innovation, digital transformation is not just technology for technology's sake; it will be a real game changer to your business.

Is digital transformation for you?

Digital transformation is, put simply, the use of new and fast evolving digital technology to solve a business problem. Gartner, in its glossary, describes it as “anything from IT modernization (for example, cloud computing), digital optimization, to the invention of new digital business models.” Sounds obvious that you would want it, doesn’t it?

But if you are still having doubts about whether it is quite right for you, then consider the following four questions. If you can answer ‘yes’ to at least one of these when evaluating the relevance of a new technology for your organisation, then you can be pretty confident that digital transformation is for you.

1.    Does this solution make us more efficient?

2.    Does this solution enhance the customer experience?

3.    Will this solution help attract and retain the right talent?

4.    Will this solution carry a measurable return on investment?

It is worth reiterating here that digital transformation is no longer something reserved for the future. It is the here and now. According to a Smart Insights survey, 65% of businesses already have a transformation program in place, with only 35% saying they currently have no plans to run one; which means that businesses who have not yet started their transformation are already behind two thirds of their competitors, a worrying thought in the competitive world we live in. So, undertaking a digital transformation is clearly not proceeding at the same pace everywhere, but what can it actually do?

AI

One of the biggest innovations to sit under the digital transformation umbrella is Artificial Intelligence (AI). In its most basic form, you would consider yourself to be in the presence of AI when a computer system performs tasks which would normally be performed by humans. AI has moved beyond theory and into reality so quickly that it is fast becoming a ‘must have’ rather than a ‘nice to have’. Human skills such as visual perception, psychological insights, speech recognition, decision-making, and translation between languages are now routinely being replicated by AI algorithms that provide the instructions for computer systems to follow.

And according to a recent (2018) study, which Cherwell conducted in partnership with global research firm IDG, a whopping 71 percent of IT organisations have already implemented one or more AI projects. And around one-third of those surveyed say their AI projects are already generating a return on investment, with an overwhelming majority—90 percent—anticipating that the ROI will be positive in the next five years.

Many businesses have already begun to make use of AI-enabling technologies as part of their digital transformation, with basic information capture and process automation being at the top of their ‘To Do’ list. All of this is being facilitated by integrating processes across departments.

Integration

Integrating processes is an essential building block of digital transformation because it breaks down the silos of information that commonly grow up within organisations. Integration also makes communication and collaboration between staff and customers much easier, promoting self-serve options that eliminate departmental bottlenecks.

Work process integration has also been shown to encourage higher employee engagement, satisfaction and productivity. According to a recent Lawless Research study of cross-functional processes commissioned by Cherwell Software, (‘Work Process Integration: Bad News is Good News – Small Footprint Today Signals Big Opportunities Ahead’) a variety of standard processes aren’t highly integrated, and poorly designed (i.e., non-integrated) processes reduce productivity and negatively affect the employee experience.

At least one-quarter of cross-functional team managers expressed frustration that different team members were using different apps. The issues considered most frustrating in this context were inefficiencies (cited by 43% of managers), repetitive work (40%), miscommunication (37%), errors (27%), and software incompatibility (26%). Respondents also reported that, on average, manual processes (which could be automated) consumed nearly half of their workday. 43% said that they spent at least half of their workday on manual processes. These processes included onboarding/offboarding an employee (86%), resolving customer issues (also 86%), conducting performance reviews (85%), and participating in cross-functional projects (also 85%).

Whether your ‘customers’ are inside or outside your organisation, their demands for a service that utilises data across the organisation are only increasing. ‘Inside’ customers, such as employees who use the company’s IT services and ‘Outside’ customers who buy the company’s products and services can both benefit equally from AI-enabled activities that draw from a common well of resources.

IT Support

The IT support desk is a good example of where organisations can start gathering information around the most frequent ‘pain points’ of their end users and not just react to problems after they have occurred. Proactively anticipating them in advance can prevent them happening in the first place. If they have the resources and know-how, they can find solutions and innovate. Predictive analytics can also be used for incident management, demand planning, and workflow improvement.

Your IT employees might be relying on manually - processed IT tickets for every request they receive, whereas moving to a self-service IT model would give the IT team more time to focus on transformation initiatives, while also increasing employee efficiency and effectiveness.

AI can also reduce the costs associated with high-volume, low-value service desk activities because it takes over routine and repetitive tasks. Chatbots, knowledge curation, and incident/request routing are three big categories of AI features that are already in widespread use now. AI-assisted knowledge management now includes an intelligent search function that doesn’t just rely on specific keywords, but actually understands context and meaning in a way that a human being does. It should be pointed out that while AI does change the way that IT supports staff work, it won’t replace them completely, but will add value to what they are already doing.

The use of chatbots, for example, means there is always ‘someone’ available 24/7 and 365 days a year, even if it’s not an actual human being. While many chatbots are used simply to deflect routine and repetitive queries and requests before escalating more complex interactions to a live IT support agent, virtual support assistants (VSAs) are now even capable of carrying out actions for the customer such as resetting passwords, deploying software, escalating support requests and restoring their IT services.

Customer Service

And what about customer service? Chatbots, for example, are one of a number of different engagement options that can be made available to customers. A chatbot AI can give customers the answers they need, when they want them, without having to wait on hold for ages until a human employee becomes available. This can drastically improve the time both parties spend resolving the query.

And how much better would it be if you could put customer communications, history and documents into context and use that knowledge to provide a better, faster service? Human agents draw on a wide range of skills to solve customer problems, but so too can non-human agents if they have the information available. And non-human agents have the advantage that they can read, digest and process huge amounts of data far faster than humans can. It is also worth remembering that chatbots are not the only option for customers – organisations should be able to customise the available interfaces depending on the context of the business and the customers themselves.

The benefits of getting quick and easy resolutions to problems and getting perfectly tailored offers that suit your specific requirements make it easy to see why these AI systems are becoming so popular. In the future, we can look forward to enjoying the convenience of a fully tailored, always ‘on’, user experience when at work or at home.

Constant connectivity provides opportunities for innovation and modernisation. Conversely though, it also creates cybersecurity threats that can compromise extremely sensitive information.

With the world heading swiftly into an age of ever-more-enhanced connectivity, individuals and organisations need to familiarise themselves with these developing threats and the volatile landscape, while ensuring they have a robust way to protect themselves against these threats.

Finding a Place for CSPs in a Volatile Landscape

Communications services providers (CSPs) specialising in mobile services, media, or web services live in a world of relentless innovation. A need to stay relevant forces CSPs to deliver value beyond basic connectivity. This opens lucrative new markets and opportunities for all industries.

The IoT industry will play a pivotal role in these innovations. The technology is on track to embed itself into countless aspects of our day to day lives, playing a pivotal role in the creation of smart cities and infrastructure, connected vehicles, digital healthcare, smart homes and more, at a pace that is hard to keep up with. 5G is also being rolled out at the same time as the IoT reaches its peak, ensuring substantial potential disruption.

Similarly, Over-the-Top (OTT) businesses – content providers that distribute messaging and streaming media over the internet – are booming. Years after Yahoo! Messenger and AOL’s AIM came and went, they keep finding new ways to undermine CSPs’ business models. Tencent, the parent company of Chinese messaging platform WeChat, currently has a market cap of over £300bn (compared to Verizon’s £190bn) and the meteoric rise of OTT streaming players like Netflix has been well documented.

And, of course, the counterpoint to all this innovation is that cyber attacks are more prolific than ever, displaying ever-evolving tactics as cybercriminals learn and adapt. Telecoms companies are frequently targeted because they build, control, and operate critical infrastructure that is widely used to communicate and store large amounts of sensitive data for consumers, businesses, and government. Data breaches or denial of service attacks on CSPs can reverberate far beyond the initial incident. Moreover, end user equipment – home routers, smartphones, IoT devices and more – are not entirely under CSP control. They can be easy to compromise and thus ideal targets for hackers looking to steal data.

Following a long year of social media giants battling with digital regulations, data privacy is a higher priority than ever before. Since prominent communications brands have also been implicated in major data breaches, CSPs are beginning to recognise the need to embrace trust as a competitive differentiator.

For consumers, the dramatic expansion in bandwidth and connectivity that will come with 5G technologies and emerging IoT devices will provide more options to engage with media.  It will also present new opportunities for both media providers and network operators. There’s no doubt that it’s an exciting time in the telecommunications sector.

Although CSPs are best positioned to enable these new business models, they can’t just sit back and enjoy the riches of growth. They still need to work to secure their customers from the risks inherent to the data economy.

Telecommunications Infrastructure is a Unique Access Point to National Security for Cyber Criminals

The infrastructure of telecommunications organisations is inherently more exposed to hard-hitting cyber attacks compared to other consumer-oriented organisations. Bad publicity, brand damage, and regulatory fines can cause short to medium-term damage, but an attack on a telecoms company has the potential for a much deeper impact that most other services don’t have to worry about.

Telecommunications systems are embedded so deeply within the networks of nations across the globe that their security has become paramount. They are constantly functioning as facilitators of not only financial and business transactions, but also emergency response communications, meaning that the consequences of a breach are substantial. Steps must be taken to ensure that every blunt edge in telecoms cyber security is sharpened and secured. 

Guarding Assets with Privileged Access Management

Companies today look after a whole host of information and data, much of it being confidential and of critical importance. To guard this data yet still allow certain individuals to access to it, privileged credentials exist across almost every enterprise’s IT environment. Cybercriminals know this. That is why almost all advanced attacks today gain access to a target’s most sensitive data, applications, and infrastructure by exploiting a company’s privileged credentials. Telecommunications is by no means an exception.

Despite this knowledge, organisations allow privileged access to critical assets and systems to remain unsecured and unmanaged. Assets are therefore left vulnerable to damaging cyber-attacks that could impact telecommunications companies and citizens far beyond the limits of a simple data breach.

Companies must up their game in securing, controlling, and monitoring the use of powerful privileged accounts to minimise disruptive damage to these systems.

In order to proactively reduce the risk of privileged access abuse, telecoms companies must firstly understand the most common types of attacks that exploit privileged access. They must know how an attacker thinks and behaves in each case to exploit the organisation’s vulnerabilities. They must then prioritise the most important privileged accounts, credentials, and secrets. Identifying the potential points of attack, and then focusing especially on those that could jeopardise critical infrastructure or the organisation’s most vital information, is also essential.

After understanding weaknesses and access points for attackers, telecoms companies must determine the most effective actions to close the gap in these areas. Which actions are the highest priority? What can be achieved quickly, and which actions require a long-term plan?

As an overarching rule, organisations should recognise that attackers are constantly looking for new ways to gain access, and act appropriately in response. By taking the time to plan out a strategy for managing privileged access and returning to reassess it as your organisation and the threat landscape evolve, you can develop a formidable defence.

Securing Telecommunications’ Exciting Future

As a global society, we’re moving into an era where technology is our most important asset and tool. We are innovating the very networks that this world runs on daily, creating, in turn, services that improve our standard of living exponentially. IoT devices create a network of tools versatile and agile for human needs, and 5G connectivity will provide all of it to our fingertips. But as a shepherd tends his flock at night, so must we in keeping alert and vigilant to potential threats that attempt to disrupt positive growth.

By understanding threats, proactively prioritising the weakest points in privileged access infrastructure, determining the best course of action, and striving for continuous improvement, CSPs will be able to minimise damage from cyber threats. With a solid strategy in place, the rewards of a hyper-connected world will be reaped.

In the service provider realm’s not-too-distant past, there was a distinct line in the sand.

On the one side, networking and security teams spearheaded the evolution to an NFV architecture, with a strong focus on virtualising network and security functions. Forays into the world of automation were tentative at best.

On the other side, developers enthusiastically embraced cloud platforms, DevOps methodologies and automation via CI/CD pipelines. Rapid application deployment and delivery was, and still is, the name of the game.

The edge is where they both come together, and applications can live harmoniously side-by-side with network functions.

Thanks to its distributed nature, and fuelled by the gradual global rollout of 5G, edge computing is finally starting to empower service providers to offer new solutions and services that simultaneously increase revenue streams and cut network transport costs.

Rather than transmitting data to the cloud or a central data warehouse for analysis, processing can take place at the ‘edge’ of a network, reducing network latency, increasing bandwidth and delivering significantly faster response times.

Take self-driving cars.

Hosting applications, with all their associated data, in a centralised location like a public cloud can yield end-to-end latency to the tune of tens of milliseconds. That is far too slow. You’ll get the same result if the the application stays central and the network function is moved to the edge. However, when you move the application and network function to the edge it is possible slash latency to milliseconds. Now we’re in business.

Virtualised Content Delivery Networks (CDN) are another compelling case in point.

To date, a third party CDN tended to be hosted at a peering point or in a centralised data centre. This is changing, with some canny service providers building their own edge computing based CDNs to cover local video content and IPTV services, all while saving on transit and backhaul costs.

There are different business models available to bring these kind of use cases to life.

The simplest scenario is a service provider allowing physical access to an edge compute site. Third parties bring their own hardware and manage everything. The service provider is responsible for space, power and connectivity, also known as a colocation model.

A far more interesting approach is for the service provider to offer Infrastructure as a Service (IaaS) or Platform as a Service (PaaS) options to third parties through a shared edge compute platform. Service providers can build these themselves or with specialist partners.

The power of automation

Automation is the secret ingredient to making it all work.

In the context of cloud computing, automation is critical for developers to publish new versions of code at pace and with agility. 

In the networking world of NFV, automation is key to driving a service provider’s operational costs down. Previously, network-  and service provisioning were manual, time-consuming tasks. Today, while objectives may differ, the tooling and techniques are the same – or sharable – for both network and developer teams. Applications and network functions co-exist in an edge compute environment.

So how can developers automate the deployment of applications and associated application services in the cloud?

For the purpose of this article, we’re concentrating on application services automation. It is worth noting that the steps described below can be easily integrated into popular configuration and provisioning management tools such as Ansible or Terraform, which are then further complemented by CI/CD pipeline tools such as Jenkins.

The first step is bootstrapping or introducing the virtual machine to deliver application services into the cloud of choice.

Next is onboarding, which implies introducing a basic configuration with networking and authentication parameters (e.g. IP addresses, DNS server etc.). Finally, there’s the actual deployment of application services – such as ADC or security policies – using declarative Application Programming Interfaces (API).

The last point is critical.

Imperative APIs, which most vendors have, means you tell the system what to do at every juncture. Firewalls are a good example. You’d need to create address lists and align them with firewall rules. These are then grouped together in a policy, which is then assigned to an interface. There are distinct steps and requirements for rest API calls to go through in sequence, otherwise everything fails. Contorting all of this into an automation tool is expensive and takes time.

Declarative APIs are a different beast. You can tell the system what you want, and it figures out the way ahead. With one declaration (in JSON or YAML format) you could, for instance, define all ADC and security service parameters and give it to the system with a single rest API call. In this case, the outcome is either a success (the service has been deployed) or it has failed but the overall system remains unaffected. There is no requirement for intelligence in the automation system. The intelligence stays within the systems you are configuring, which dramatically reduces automation costs.

The exact same steps can be taken to provision a virtual network function in an NFV environment. A declarative API markedly simplifies integration with end-to-end NFV orchestration tools. The orchestrator doesn’t need to know the individual steps to configure a service or network function, it simply pushes a single JSON declaration with the parameters the system needs to set up the service. Again, the intelligence on ‘how’ to configure the service stays within the system you are configuring.

Through closer alignment between networking and developer disciplines, we can now build a distributed telco cloud with a common automation framework for applications and network functions. It is agile and secure at every layer of the stack – from the central data center all the way to the far edge and can even span into the public cloud.

Industry-wide, we expect common automation frameworks that enable the deployment of applications and their services, as well as network functions, to become the norm in the coming years – particularly as 5G rollout continues worldwide. The pressure is building for service providers to unify siloes, get agile and start living more on the edge.

‘Any sufficiently advanced technology is indistinguishable from magic’ - Sir Arthur Charles Clarke’s Third Law (1973) goes a long way to explaining the state of AI in business at the moment. 

AI certainly qualifies as an advanced technology (even though it’s been around since 1956*) and, without meaning to be rude, for most people it is indistinguishable from magic – most of us have no idea how it actually works, and many have over-inflated expectations of what it can actually do (thanks Elon).  This makes it hard to understand its strengths and limitations, where it makes sense to deploy it, as well as makes it hard to differentiate AI from complex software. This results in highly-varied degrees of success in the use of AI within businesses.

To give an example of why understanding the limitations of AI is essential to successfully using it, consider the following example: for several years, Amazon was using AI to help filter applicant CVs and identify suitable candidates, essentially letting the machine objectively decide who to take forward.  However, the AI was trained on historical CVs that were mostly males, and so it was found to be rejecting female CVs. Not so objective after all. It is remarkable that Amazon, one of the leading AI players, had not considered the inherent bias of input to the model sooner.

Now, it may seem boring to talk about understanding and limitations when everyone is hyping up the potential for AI, but, as the Amazon example shows, it is of fundamental importance.  Even more boring still, and what many companies really struggle with when it comes to deploying AI, is access to data - the first step in any AI project is to ensure data availability, cleanliness, and integrity, without which the benefits of AI will never be truly realised. It is no mean feat to get this right, and it should come as no surprise that the companies achieving the greatest success in AI are those that are, in relative terms, young – for example, Facebook, Google, Amazon, Uber, and Tesla.

In summary, many companies, even those using it, are unclear what AI is and how it works, and many don’t have the data foundations on which to exploit it to its fullest.  That said, its popularity will continue to grow, and its use will no doubt continue to increase over the coming years, but the companies that will achieve the greatest successes are those that have understood its limitations and exploited what it is great at – learning from huge amounts of data and helping humans to do better jobs, not replacing them entirely.

*The term Artificial Intelligence was coined back in 1956, but it’s only relatively recently that we’ve been accumulating the mass data sets and developed the computational horsepower needed to use AI (consider that the most recent iPhones have over 100,000 times the processing power of the computer used for the Apollo 11 moon landing).

Machine Learning in Finance

Business changes all the time, but advances in today’s technologies have accelerated the pace of change. Machine learning analyses historical data and behaviours to predict patterns and make decisions. It has proved hugely successful in retail for its ability to tailor products and services to customers. Unsurprisingly, retail banking and machine learning are also a perfect combination. Thanks to machine learning, functions such as fraud detection and credit scoring are now automated. Banks also leverage machine learning and predictive analytics to offer their customers a much more personalised user experience, recommend new products, and animate chatbots that help with routine transactions such as account checking and paying bills.

Machine learning is also disrupting the insurance sector. As more connected devices provide deeper insights into customer behaviours, insurers are enabled to set premiums and make payout decisions based on data. Insurtech firms are shaking things up by harnessing new technologies to develop enhanced solutions for customers. The potential for change is huge and, according to McKinsey, “the [insurance] industry is on the verge of a seismic, tech-driven shift.”

Financial Trading

Few industries have as much historical and structured data than the financial services industry, making it the perfect playing field for machine learning technologies. Investment banks were pioneers of AI technologies, using machine learning since as early as the 1980s. Nowadays, traders and fund managers rely on AI-driven market analysis to make investment decisions that are paving the way for fintech companies to develop new digital solutions for financial trading. AI-driven solutions such as stock-ranking based on pattern matching and deep learning for formulating investment strategies are just some of the innovations available on the market today.     

Despite these technological advances, the concept of machine learning replacing human interaction for financial trading is not a done deal. While Index and quantitative investing account for over half of all equity trading, recent poor performance has exposed weaknesses in the pattern matching model on which investing strategies are based and demonstrates that, no matter how fancy the math, computers are still no replacement for the human mind when it comes to capturing the nuances of financial markets. At least, not yet. 

Data Analytics for Security and Compliance

Managing enormous volumes of data make compliance and security two of the biggest challenges for financial organisations. It is no longer enough to protect your network perimeter from attack, as the exponential growth of data and an increase in legitimate access to that data increases the likelihood of a breach on the inside.  Additionally, banks are storing large volumes of data across hybrid and multi-cloud environments that provide even more opportunity for cybercriminals to get their hands on valuable assets. In short, the same data that brings new opportunities for business growth increases the security risk for financial firms.

Data analytics using machine learning has been transformational in helping firms overcome these challenges as it picks up on unusual user behaviour to detect suspicious activity and minimise the risk of fraud, money laundering, or a breach. Similarly, data analytics technologies can be applied to compliance activities such as database auditing processes, reducing the need for human intervention and thereby easing the burden for compliance managers.

Looking Ahead

As the financial services industry continues to leverage machine learning and predictive analytics, the volume of data these firms generate and store is ballooning. Protecting that data, other sensitive assets, and business operations will only become more challenging. Firms will have to adopt new security technologies that can mitigate their security and compliance risk.

The increasing adoption of DevOps is down to several factors, for instance, it allows organisations to capture all processes in an auditable and replicable way. Additionally, it adapts quickly, which makes the cost of change low, allows businesses to add cross-functionality collaborations, which often involves different teams working together, and results in working at a much higher speed. Since its introduction, DevOps has also served to highlight that organisations need to be more agile and has inspired many to do so.

As DevOps has matured and become more mainstream, there has been a gradual evolution of the approach. With a similar evolution taking place in the cloud world, more intelligent tools have started to become available too. Consequently, developers are now able to follow up DevOps processes with more discipline and more efficiently, with the approach showing the potential to revolutionise enterprise.

Presenting DevSecOps

Among the most significant developments in the evolution of DevOps so far has been the emergence of DevSecOps – the practice of integrating security into development. Historically, the issue of security had been largely overlooked in terms of DevOps due to the inclusion of security during development hindering speed. Instead, security was commonly retrofitted after a build. However, as developers and organisations have begun to realise that this approach not only makes the process more difficult but also isn’t the most security-conscious method, some have started to integrate security into DevOps from the outset. This DevSecOps approach allows developers to alleviate any security issues at the time of development, rather than retrospectively.

Beyond this, DevSecOps is also helping businesses to break down siloes by encouraging greater collaboration across teams to ensure that security experts are involved and knowledge is being shared.

As more organisations adopt a DevSecOps approach, they should adopt the following two initiatives:

Detailed review processes

A vital principle of DevSecOps is to continually review security. This means compliance monitoring for PCI and GDPR, determining what the process is if security senses a threat and deciding how the business will assess if code is susceptible to a certain vulnerability. In order to do this successfully, it’s important for an organisation to establish a review process from the moment it thinks about architecting a new solution. From here, it can move to ongoing monitoring and management of security as the code progresses through every stage, from the developer desk to the building of the solution and the testing of it. It’s also crucial to ensure developers are given training and are taught to be aware of security throughout the development journey.

A culture of collaboration

Enterprises must ensure they have the right mindset and embrace a collaborative culture which recognises that different expertise from across the business is required for DevSecOps to be effective. Traditionally, developers have been focused solely on aspects such as logic and algorithms, with security factoring only as an afterthought. However, by adopting a culture that encourages collaboration at the start of every build, organisations will be able to create secure, stable, resilient solutions which will pay dividends.

The next phase of DevOps

The advent of DevSecOps signals a continued evolution of DevOps, and while there is no guarantee regarding where the approach might go next, there are two main theories regarding its future:

NoOps

NoOps is the idea that solutions will feature everything they are required to, such as code standards, security, libraries and legislation protocols, from the outset and everything will be completely automated. Technically, as everything would be automated within the software provisioning pipeline, there would be no need for manual, human-based operations, instead, they will be required to merely monitor and raise questions as they verify the software. As everything would automatically meet a certain standard, this could potentially guarantee a higher level of security and resilience.

MLOps

Rather than DevOps disappearing completely, different types of Ops may emerge. Ops could be augmented by machine learning (ML), or MLOps could be developed to form a machine learning-driven operation that would be able to certify the standards that organisations want software to be written with and even flag issues with it.

The evolution of DevOps is likely to continue as organisations become more familiar with it and technology continues to advance at pace. In time, this will result in DevOps beginning to encompass new technologies, such as ML, and all of the requirements of development being brought together. Ultimately, this will be extremely beneficial, encouraging collaboration across departments and ensuring that new solutions meet required standards and security from the outset.

According to a Gartner survey[1], by 2022, more than 75 percent of companies will be running containerised applications in production – a sweeping uptake from the fewer than 30 percent that do so today. While containers can help teams gain scalability, flexibility and ultimately, delivery speed, they also create a lot of complexity as applications and associated infrastructure becomes more distributed. It should also be noted that if an organisation uses containers, it will also likely use an orchestration tool for the deployment and management of containers, such as Kubernetes. As such, it is important that DevOps teams have monitoring in place to increase visibility, being able to seamlessly spot performance or availability issues that originate in containers and traditional infrastructure architecture, in order to prevent business problems. To ensure this, there are a few pivotal rules to follow to maximise container investment.

Monitor apps alongside infrastructure

For traditional infrastructure, the ideal practice is to monitor server performance metrics and the health of the application running on it, including trace or calls made to other components. However, the work of IT teams is frustrated by the exponential layers of complexity added by Kubernetes. The IT team must undertake the daunting task of not only monitoring the server and application, but also the health of the containers, pods, nodes and the Kubernetes control plane itself.

To maximise the return on investment (ROI) of container investments, the monitoring of the Kubernetes control pane and master components is highly important. Unhealthy components lead to issues in the scheduling of workloads and this can directly impact scalability and flexibility benefits, plus the running of business applications. When these applications fail, it can put serious strain on an organisation’s service level agreement (SLAs), customer commitments and the overall brand.

Beyond the monitoring of components, a keen eye must be kept on the overall business service being delivered. This multi-leveled monitoring necessitates a tool that is not just capable of monitoring containerised applications and all elements of infrastructure, but can easily roll up information to an overall service view. This ensures that DevOps and IT support teams have holistic context into how components link together and how any emerging issue impacts related processes. 

Monitor at the service level

The underlying infrastructure of applications is ever-growing in complexity. This being the case, it is important that an organisation’s IT team prioritises the applications and services critical to business functionality. To help guarantee a clear perspective of networking infrastructure, IT teams must not be too focused upon the individual container view – after all, if one specific container has raised an alert, this does not necessarily mean multiple other containers are failing. It may be that, despite the alert, business services have not been negatively impacted.

An effective method to maximise ROI by staying focused upon what is integral to business functionality is by identifying and monitoring key performance indicators (KPIs) across different containers. This will provide an overall service or application level view and a telling perspective of how your applications are performing.

Automated monitoring saves time

When using Kubernetes, containerised workloads are scheduled across nodes in the cluster so that resources are used efficiently to meet workload requirements. The process of manually adding containers and pods to and from monitoring is time consuming, inefficient and, simply put, unrealistic. The oft recycled truism ‘time equals money’ is certainly correct when an organisation’s IT team is stuck with a monitoring solution that requires manual changes. In this scenario, teams are faced with numerous tasks such as adding monitoring agents, configuring metrics to be collected, and even specifying when alerts should be triggered by changing thresholds in order so that they reflect the needs of the business service.

Underlining the need for automation, is the fact that the resources themselves can be short-lived to begin with. Sysdig’s 2018 Docker report[2] demonstrated the ephemeral natures of containers by finding that 95 percent of containers live less than a week, 85 percent live less than a day and 74 percent live less than an hour, while 27 percent live between five and 10 minutes, and 11 percent live less than 10 seconds.

These fleeting lifespans are not necessarily a drawback – that is why companies choose to implement containers. However, to maximise ROI, it is imperative that IT teams automate container monitoring, including automatically adding and deleting cluster resources to be monitored, in order to reduce manual effort involved.

A unified view of monitored resources is essential  

Companies that use Kubernetes are often complex in infrastructure – they may operate both in the cloud and on-premise, while using containers as a unifying layer to standardise application management and deployment. To effectively manage such labyrinthine systems, it is essential to have unified visibility of business services and their underlying infrastructure components, including containers and traditional components. But more importantly, this visibility must provide automatic context, e.g. if an issue in a container starts to arise, then the impact on other pertinent infrastructure components and the overall business service is made known.

It is a fact of modern IT infrastructure that diverse environments are connected, so without a unified view, it can be immensely challenging to troubleshoot issues that transcend environments. Given this complexity, monitoring tools must go beyond only monitoring what has happened, to understanding why – a unified intelligence approach that helps users remain proactive when encountering these challenges.

Effective monitoring ensures ROI on container investments

Containerised applications can be a useful tool in the employ of IT teams, offering scalability, flexibility and delivery speeds. However, their utility is often matched by the complexity that they, and the Kubernetes orchestration tool, bring to infrastructure. To maximise ROI, effective monitoring is all but essential. The ideal means of doing this is by following these few pivotal rules, and when these are followed, an organisation can truly make the most out of containerised applications. 

“We’ve heard a lot about how artificial intelligence (AI) will transform the back office function within businesses, but very little about its transformational impact on the front-end; the marketing, sales and customer success functions where there are opportunities to help drive revenue. This is changing though. Many businesses are already starting to augment their sales and marketing teams with Intelligent Virtual Assistants (IVAs) that allow them to scale their business and accelerate revenue across the customer journey.

“One example is CenturyLink, the third largest telecom company in the US. The company started using our AI-powered sales assistant to drive warms leads through their sales funnel. To begin with, our AI assistant was tasked with reaching out to smaller prospects that did not have a designated sales rep. The AI assistant was able to contact these prospects and make many feel like they had a personal relationship with CenturyLink.

“The AI Assistants that they named ‘Angie’ and ‘Ashley' immediately went to work setting up 300 calls for four sales reps. which quickly became a case of allowing them to follow up with 800 telemarketing leads per week because the quality was so good. CenturyLink is now using Conversica to contact 90,000 prospects every quarter.

“Using Intelligent Virtual Assistants to augment their workforce has been a game-changer for their business. IVAs identify and deliver hundreds of qualified leads each week who are truly ready to engage with salespeople. For CenturyLink, this has resulted in increased excitement among their sales team, which in turn has led to a 20-1 ROI on a monthly basis.

“This steady approach is often required as it helps sales teams, who can often be hesitant of the idea of technology augmenting their work, to steadily become accustomed to the idea. It also allows businesses to test the personalised approach to AI in sales in a safe environment. As more businesses become more digitally adept, expect intelligent automation to transform the sales and marketing process in these ways.”

"Whether it is data visualisation (Descriptive Analytics), finding correlation in a massive amount of data (Diagnostic Analytics) or making predictions (Predictive Analytics), AI can augment humans in what they do. However, there is some anxiety about algorithms or machines being able to give instructions based on specific events and data (Prescriptive Analytics) or even to execute those instructions with little or no human intervention (Automated Decisions): will they take over and fire us all?

The reality is very different, and we are very far from such a scenario if it will ever happen. There are three practical examples of the use of AI in today’s businesses.

• Predictive Analytics can be useful in sales and marketing. AI can predict which product an existing customer is likely to want next, or the tell-tale signs that a client is about to leave 

• Robotic Process Automation: Within any business, there are likely to be routine, repeatable and mind-numbing tasks — such as processing orders or re-keying invoices. With AI, many of these operational processes can be automated and streamlined, saving hours for your staff.

• Cognitive Interaction: Web chatbots have come a long way. Advanced AI can personalise the experience, calling up account details, while also reading the emotion of the customer. For example, if the customer is starting type ‘angry’ words, the chatbot can prompt a member of your human team to join the conversation and save the day.

We all experienced, even unknowingly, some of this, e.g. when we see an online shop proposing other products while we are browsing their catalogue, or when we interact on an online chat. However, at the moment, mainly the larger business have managed to make some use of AI.

Smaller businesses might still feel a bit intimidated by technology, as they look at it as a mere technology, rather than as a tool to improve the business.

It’s important to stress that AI is about augmenting what your staff do — and freeing up their time for better things — not replacing them. Your goal is to solve business issues with the right solutions. That requires business acumen as the starting point.

Think about the core business challenge, not technology. The tail shouldn’t wag the dog!

So, if you are an SME, how do you want to use AI?

Rather than taking a ‘big bang’ approach, split your challenge into bite-sized chunks. Use AI to solve little bottlenecks first or to glean specific insights that inform your decision-making.

You might discover that the software you need exists already. If not, then think creatively. For example, you could team up with a local university or college and work collaboratively, providing some modest financial incentives and prizes. Smart minds like a challenge and may relish the opportunity to solve a real-life business headache.

This way, you’ll solve the business issue, break the ice with AI, have new tools that sharpen your competitive edge."

It has long been held that the world is flat when it comes to technological progress connecting users at an ever-increasing pace.  The sewing machine took 1119 years to spread beyond Europe whereas WhatsApp messaging took only 6 years to reach 700 million followers.   

It is evident that digital advances move at pace and these days digital has become synonymous with Artificial Intelligence from Alexa to Siri to Eggplant. 

Some people are nervous about adopting AI-powered technologies, but our experience is that if vendors can clearly explain how algorithms work and show how AI is driving business outcomes, enterprises are hungry to adopt.In a recent Eggplant global survey, 98% of respondents are looking to attain 50% automation of their software development cycle and will likely use AI to get there. 

AI is vastly impacting customer experience and how users interact with organisations. Smart companies are using it to personalise, analyse, monitor and test across any platform, simulating real user interactions and determining how technical factors are affecting business outcomes.  Today personalisation is at the cosmetic and content level, but we now have the technology to personalise the entire digital experience at a much deeper level. 

Ultimately, businesses working with AI are increasing competitive advantage, improving efficiency and creating amazing digital experiences - all of which lead to positive business outcomes.

Today in so many industries, from manufacturing and life sciences to financial services and retail, we rely on algorithms to conduct large-scale machine learning analysis. They are hugely effective for problem-solving and beneficial for augmenting human expertise within an organisation. But they are now under the spotlight for many reasons – and regulation is on the horizon, with Gartner projecting four of the G7 countries will establish dedicated associations to oversee AI and ML design by 2023. It remains vital that we understand their reasoning and decision-making process at every step.

Algorithms need to be fully transparent in their decisions, easily validated and monitored by a human expert. Machine learning tools must introduce this full accountability to evolve beyond unexplainable ‘black box’ solutions and eliminate the easy excuse of “the algorithm made me do it”!

The need to put bias in its place

Bias can be introduced into the machine learning process as early as the initial data upload and review stages. There are hundreds of parameters to take into consideration during data preparation, so it can often be difficult to strike a balance between removing bias and retaining useful data.

Gender for example might be a useful parameter when looking to identify specific disease risks or health threats, but using gender in many other scenarios is completely unacceptable if it risks introducing bias and, in turn, discrimination. Machine learning models will inevitably exploit any parameters – such as gender – in data sets they have access to, so it is vital for users to understand the steps taken for a model to reach a specific conclusion.

Lifting the curtain on machine learning

Removing the complexity of the data science procedure will help users discover and address bias faster – and better understand the expected accuracy and outcomes of deploying a particular model.

Machine learning tools with built-in explainability allow users to demonstrate the reasoning behind applying ML to a tackle a specific problem, and ultimately justify the outcome. First steps towards this explainability would be features in the ML tool to enable the visual inspection of data – with the platform alerting users to potential bias during preparation – and metrics on model accuracy and health, including the ability to visualise what the model is doing.

Beyond this, ML platforms can take transparency further by introducing full user visibility, tracking each step through a consistent audit trail. This records how and when data sets have been imported, prepared and manipulated during the data science process. It also helps ensure compliance with national and industry regulations – such as the European Union’s GDPR ‘right to explanation’ clause – and helps effectively demonstrate transparency to consumers.

Providing humans with the tools to make breakthrough discoveries

There is a further advantage here of allowing users to quickly replicate the same preparation and deployment steps, guaranteeing the same results from the same data – particularly vital for achieving time efficiencies on repetitive tasks. We find for example in the Life Sciences sector, users are particularly keen on replicability and visibility for ML where it becomes an important facility in areas such as clinical trials and drug discovery.

Models need to be held accountable…

There are so many different model types that it can be a challenge to select and deploy the best model for a task. Deep neural network models, for example, are inherently less transparent than probabilistic methods, which typically operate in a more ‘honest’ and transparent manner.

Here’s where many machine learning tools fall short. They’re fully automated with no opportunity to review and select the most appropriate model. This may help users rapidly prepare data and deploy a machine learning model, but it provides little to no prospect of visual inspection to identify data and model issues.

An effective ML platform must be able to help identify and advise on resolving possible bias in a model during the preparation stage, and provide support through to creation – where it will visualise what the chosen model is doing and provide accuracy metrics – and then on to deployment, where it will evaluate model certainty and provide alerts when a model requires retraining.

…and subject to testing procedures

Building greater visibility into data preparation and model deployment, we should look towards ML platforms that incorporate testing features, where users can test a new data set and receive best scores of the model performance. This helps identify bias and make changes to the model accordingly.

During model deployment, the most effective platforms will also extract extra features from data that are otherwise difficult to identify and help the user understand what is going on with the data at a granular level, beyond the most obvious insights.

The end goal is to put power directly into the hands of the users, enabling them to actively explore, visualise and manipulate data at each step, rather than simply delegating to an ML tool and risking the introduction of bias.

Industry leaders can drive the ethics debate forward

The introduction of explainability and enhanced governance into ML platforms is an important step towards ethical machine learning deployments, but we can and should go further.

Researchers and solution vendors hold a responsibility as ML educators to inform users of the use and abuses of bias in machine learning. We need to encourage businesses in this field to set up dedicated education programmes on machine learning including specific modules that cover ethics and bias, explaining how users can identify and in turn tackle or outright avoid the dangers.

Raising awareness in this manner will be a key step towards establishing trust for AI and ML in sensitive deployments such as medical diagnoses, financial decision-making and criminal sentencing.

Time to break open the black boxes

AI and machine learning offer truly limitless potential to transform the way we work, learn and tackle problems across a range of industries—but ensuring these operations are conducted in an open and unbiased manner is paramount to winning and retaining both consumer and corporate trust in these applications.

The end goal is truly humble, honest algorithms that work for us and enable us to make unbiased, categorical predictions and consistently provide context, explainability and accuracy insights.

Recent research shows that 84% of CEOs agree that AI-based decisions must be explainable in order to be trusted. The time is ripe to embrace AI and ML solutions with baked in transparency.

Against this backdrop, it’s critical that businesses help their employees to use data properly. However, without effective information governance, even data-savvy employees struggle to derive true value from their enterprise’s content, spending as much as 36% of their time trying to search for information, and only in half of the cases achieve success (says IDC). Therefore, a solid foundation of a data-driven culture includes rethinking the ways an organisation deals with information, as well as implementing the right technologies.

The root causes of poor information governance

Today, organisations generate unprecedented quantities of data, the major part of which is unstructured, including complex data, such as PDFs, pictures, and other documents. Using traditional manual methods for data management has become inefficient and as a result, organisations are flooded with information. A recent report by The Compliance, Governance and Oversight Counsel found that 60% of corporate data has no “business, legal or regulatory value.” Such information is commonly disorganised, often old or irrelevant, requiring high IT labour and storage costs due to its large volume.

Moreover, many organisations store their content in siloed systems, each having its own search functions. Predominantly keyword-based search has proven to be ineffective due to the complexity of modern enterprise content. For example, when an average employee searches for a document in a corporate storage system, he or she usually utilises fragmented information such as brand or practice names, rather than using the exact file name. Keyword-based search will reveal all possible documents that contain these words, yet the majority of which will be irrelevant to the request. As a result, the user will have to spend a significant time filtering out unnecessary information. In fact, a recent survey has found that 82% of employees believe that poor information management adversely impacts their productivity. Some organisations even classify documents manually in order to meet this challenge. However, this is time-consuming, and introduces the risk of human error – after all, tagging documents incorrectly is worse than not tagging them at all. Generating partial information in search results leads to inconsistent business practices, which might negatively impact strategic initiatives.

Information governance best practices

How can organisations help their employees derive more value from enterprise content? Ideally, employees should be able to quickly search for necessary data, as well as for information that is relevant to their request, and share it amongst colleagues and teams. This assumes that every employee can apply existing corporate knowledge to his/her task, rather than creating an asset from scratch. For that, every piece of content needs to be indexed and managed thoroughly. Such an approach allows an organisation to take maximum advantage of its content as it endorses employees’ efforts to share "best practices" and encourages connected thinking, which in turn creates new business opportunities.

A robust information governance strategy enables an organisation to remove the roadblock caused by mountains of disorganised data and the limitations of keyword-based data exploration, and to accelerate data literacy across the organisation. The latter leads to benefits such as improved decision-making, increased employee productivity and makes organisations more competitive.

Here are the best practices for improving information governance:

1. Set priorities and design the structure

Once an organisation has decided to enhance information governance, it is recommended to appoint a dedicated team. The project team should start with asking such questions as: “How should data be organised so that employees can effectively use it?”; “Should it be searchable depending on business practices or sectors?”; or “How many languages does an organisation operate with?”. To answer these questions, the team should arrange meetings with subject matter experts. This will help them to thoroughly transform this knowledge into structured taxonomies.

2. Enforce machine learning to overcome the limitations of a keyword-based search

Smart search should be aware of the context of the specific business environment and the user’s goals. Such context is derived from the metadata that describes what this document is about. To enable the search system to leverage metadata, it is necessary to apply to each document complex tags. While this task is virtually impossible for a human to complete due to its labour intensity and high probability of errors, automated data classification easily copes with it. Then, machine learning analyses an organisation’s content enterprise-wide, adapts to its specific environment and derives the terms and taxonomies that best describe the business context. Smart search uses these taxonomies to provide business users with accurate search results. Moreover, such search uses not only terms based on traditional single keywords, but also “compound terms” – that is, short phrases that have very specific meaning in the organisation-specific context. If the user wishes to improve their search results even more, he or she will be able to use refiners that allow them to narrow down the search results. With such an intelligent search system, the organisation can ensure employees can quickly access the information necessary to achieve their business goals as well as to define duplicate and irrelevant data to dispose of it.

3. Educate employees

Employees are often resistant to change, since getting used to new technologies might take them additional time. Therefore, the project team should explain the value of information governance to every employee. Moreover, senior management should take a leadership-driven approach and encourage mid-level managers to explain how the established information governance contributes to their everyday work and opens up new business opportunities for an organisation.

Taking such an approach to information governance, empowered by machine learning, helps organisations to build a data-driven culture as it enables them not only to organise their data, but also to change the way of thinking inside the organisation, encouraging employees to keep, share and reuse good practice knowledge. A positive outcome of robust information governance on business is the ability to leverage corporate expertise to provide better service and long-lasting value to its customers. Moreover, with such governance in place, organisations can easily identify and delete duplicate, old and irrelevant data, thus, cleaning up storage and cutting down costs.

Automation has been a hot topic in the customer service industry for some time now, with excitement growing around the potential for AI to revolutionise the way businesses interact with their customers and, in theory, cut costs.

However, as seen with the offshoring boom, viewing customer service as merely a cost to be optimised can have a devastating effect on customer satisfaction levels. So, what role should AI play in the industry going forward, and how can businesses avoid repeating past mistakes?

The rise of the ‘super-agent’

As AI technology becomes more accessible, greater numbers of businesses will undoubtedly choose to start automating some of the more basic, transactional customer-facing tasks previously undertaken by agents.

However, rather than replacing the need for live agents, the rise of AI has in fact increased demand for human interaction from customers, making the role of live agents all the more important.

Thanks to AI, agents are increasingly dealing exclusively with more complex, emotive enquiries that require them to engage with customers in a sensitive, personal way that is not yet possible for technology.

Forward-thinking businesses have identified this as an opportunity to differentiate themselves from their competitors, and have begun providing their agents with more extensive, specialised training, with an emphasis on emotional intelligence and soft skills in addition to product knowledge.

These so-called ‘super-agents’ are customer service experts, capable of building a genuine rapport with customers and providing a consistently excellent experience thanks to their empathy, authenticity and effectiveness.

Agent-facing solutions

While customer-facing technologies such as chatbots are the first to come to mind when thinking about AI in customer service, AI will actually prove just as, if not more, useful behind the scenes.

AI is capable of interrogating large volumes of data extremely quickly, something that is already being put to good use in other sectors, such as law, with growing numbers of law firms developing their own in-house AI systems to automate laborious research tasks.

Customer support leaders should look to emulate this, in order to improve agents’ access to customer data. For example, AI is capable of immediately gathering all relevant data from a company’s CRM system, speeding up the process and arming the agent with the tools they need at the beginning of the call to effectively and sensitively handle the query.

Importantly, this can include information about previous interactions with a customer across all channels, such as social media. This is a really effective way of minimising customer frustrations as a result of having to re-explain certain issues, and can flag certain customers as high priority if they have reached out across multiple channels already.

It’s important that businesses do not simply view AI as the latest cost-cutting tool, as this will inevitably lead to unhappy, dissatisfied customers. However, implemented correctly, AI will help to pave the way for more highly skilled, compassionate agents who, thanks to clever use of technology, are able to provide the best possible level of service to every customer.

We live in an era where “narrow intelligence” (artificial intelligence focused only very specific tasks) can be used to great benefit. Cyber security teams have been using machine learning for years to better detect malware, categorise content and detect anomalies in traffic or behaviour, but a significant amount of human interaction is needed to build and maintain these solutions. This is not a bad thing – the current state of AI is such that as a technology it can be a powerful ally, but is far from being self-sufficient.

In addition to being a powerful tool for defenders, such as handling enormous amounts of data that would be impossible to do manually, AI can also be used by attackers. Although not yet very often seen, the emergence of open source tools offering capabilities ranging from creating perfect impersonations of voices to utilising state of the art reinforcement learning methods to improve the efficiency of attacks means they will soon become more prevalent.

To prepare themselves, defenders must continue to ensure they are able to respond to threats rapidly enough. For example, when a multi-agent attack bot launches and completes a data extraction from an initial breach within milliseconds, there is just no way for human defenders to keep up without the help of AI.

But we must remember that AI is not infallible. The more we use solutions that learn from data, the more we create new types of attack surfaces. AI is not inherently good or bad, but it also is not inherently secure – when building AI solutions we must take steps to not only ensure we protect our AI from intentional manipulation, but also unintentional, undesired behaviour such as bias or having blind spots where we did not have training data. These issues can be addressed, but only when we become aware of them and act when building the systems.

One could say that in general humankind currently looks at AI from the perspective of replicating human intelligence – a viewpoint that is severely limiting the potential of machine intelligence. Computers are not like people, they have very different strengths – and when building solutions that utilise machine intelligence, we should use the strengths of  computers and not try to just replicate how we think. Just as humankind first tried to fly by mounting wings on our arms and flapping them wildly but ended up building airplanes and rockets, true machine intelligence will most likely take a very different form than what most expect – something we for one at F-Secure are exploring in our Project Blackfin for example. This does not mean general intelligence or robot overlords will be taking over the world, but I do believe we are nearing the next era of AI solutions and it will be very exciting to see!

Data

Historically, you distributed software to the consumers and customers, but you didn't get the insight regarding how they leverage your software - which features are in use and those that are not. When providing your software as a service, you also have lots of data and insights that can help you improve your service.. You can therefore use this information to provide insight to your customers, you are better able to understand usage patterns and, ultimately, be able to use this data to give intelligent feedback. The SaaS era coincides with the almost ubiquitous concept of Big Data. And SaaS, which is able to leverage AI techniques, has a distinct advantage over the software provision days of old - the software provider now has access to aggregated data from different customers, which he can leverage to build a better service.

Companies now hold significant volumes of data from customers all in one place. AI enables a more automated means of mass data processing. Gartner defines big data by not only Volume, but also by its variety and velocity. Variety refers to the different media we use to represent data (beyond simple figures) and velocity is determined by the speed at which data is collected, analyzed and implemented. The ultimate reality is that IT teams are dealing with increasing amounts of data and a variety of tools to monitor that data - which can mean significant delays in identifying and solving issues. And with the whole area of IT operations being challenged by this rapid data growth (that must be captured, analysed and acted on), many businesses are turning to AI solutions to help prevent, identify and resolve any potential outages more quickly.

Marketing is particularly well placed to leverage AI techniques. The data SaaS companies collect need to be relevant and recent. The more up to date the data is the more efficiently it can be implemented. Large corporations can access data collected through loyalty programs and cross-promotional activities, and smaller businesses can acquire data through customer surveys, online tracking or by competitor analysis. AI solutions can certainly be a golden opportunity for businesses to broaden their perspective on potential customers.

Automation

For B2B customer-centric businesses, AI allows more functions which may previously have had a manual component to be automated - for example, it enables them to automate many customer experience processes, such as training and onboarding, marketing campaigns and ongoing customer service. AI essentially aggregates large quantities of data for example, customer data and filters it into automatic processes. Customer service AI platforms like chatbots, which respond to and troubleshoot customer inquiries automatically, enable customer service departments to take on additional inquiries. That’s great news for revenue retention and churn reduction, as customers tend to show a heightened interest in a purchase, following a positive customer service experience. Likewise, negative customer service experience is a good way to get rid of customers. Supplementing AI technology with your customer service team can target the seamless cross-section between convenience, problem-solving and human experience - a typical example here could include using machine learning to automate aspects of customer service (especially self-serve).

The main UX challenge of SaaS is remoteness. Artificial Intelligence can help to alleviate that sense of remoteness whilst delivering a more satisfying experience to the customer. There has been a lot of scaremongering with respect to machines taking jobs from humans, and that AI will bring about automation in practically all walks of working life - however, the more likely scenario is that AI will deliver most value when it is deployed in conjunction with human beings. SaaS can, and should, manage those interactions which can be handled automatically ( classic Saas) and those which require human intervention.  AI-augmented human interactions can drive SaaS interactions too.

Personalisation

Natural language processing and machine learning allow SaaS companies to massively boost personalisation. And, let’s face it, consumers have become much more demanding,  wanting personal experiences that are tailored to their specific needs. AI can help here by providing analysis of your users’ previous actions, giving you actionable insights into their preferences and interests. This allows you to configure user interfaces providing that all-important tailored experience. Remember, If they don’t receive it from your business, they’ll go elsewhere to find it.

The Customer

The Wellcome Sanger Institute is one of the world’s leading research facilities focused on genomic discovery.

Based at the Wellcome Genome Campus, Cambridge and established in 1993, the Institute conducts key research into improving the outcomes of human health using data derived from genomic sequencing, particularly in the areas of cancer, malaria and other pathogens. It also works closely with a wide range of collaborators – both internationally and across the Campus – to share the results of its research with the wider scientific community.

The Challenges

At the core of Sanger’s technical infrastructure are its DNA sequencing machines; a fleet of complex and advanced scientific instruments which generate vast quantities of data that must then be analysed within their on-premise data centre.

The nature of genomic research, a cutting-edge and evolving area of science, means that the demand for data-processing capacity is only likely to increase over time – genomic data is soon set to become the biggest source of data on the planet.

In fact, as much genomic data has been produced in the last eighteen months as the previous eighteen years, but as an organisation concerned primarily with science, budgets for supporting infrastructure, including IT, will always be under pressure.

Simon Binley, Data Centre Manager at Sanger states: “It’s hard to predict what level of data centre capacity we’ll need over the next five to ten years, but all the trends point towards the requirement for greater IT capability; the more processing power you have the greater the scientific capability. Every year, the sequencers generate data more quickly as the technology improves. IT has to keep up with that demand.”

By way of illustration of the pace of change, as part of an international effort, Sanger took 13 years to map the first human genome. Today the same task can be achieved in less than an hour. As data is gathered more quickly and in greater quantities, scientists will turn to examining previously unexplored phenomena and interactions revealed as new information is produced.

“The human body is made up of trillions of cells,” says Binley. “As we sequence those cells we will also be gathering more genomic data via a greater number of sequencing machines. Advances in today’s technology also mean we are gathering data more quickly than ever before. This requires more power availability, greater storage, faster connectivity and higher levels of local compute.”

This requirement for local processing to be physically close-by the sequencing equipment where data is being generated is an archetypal example of an ‘edge computing’ deployment. “Proximity to the sequencing equipment is a primary consideration for the data centre,” continued Binley. “The bandwidth and latency requirements for the high volume and velocity of genomic data makes cloud services unsuitable. As such, no other edge data centre is as important to discoveries about human life as the one at Sanger.”

Another key considerisation is the DNA sequencing equipment on which the scientific effort depends, which must be protected by uninterruptible power supply (UPS) systems at all time. Downtime within this distributed IT environment would require the chemicals used in the research process to be replaced at significant cost, in addition to lost time and data. Ongoing monitoring of UPS battery health is therefore essential to ensure runtime is available and makes a major contribution towards the Wellcome Sanger Institute avoiding outages in both the data sequencing and research efforts.

Operational efficiency is the remaining key concern for Binley and the data centre. Simply put, any money saved on IT and physical infrastructure, either in terms of capital expenditure (CapEx) or operating costs, means greater funding is available for key scientific research. Cooling, as ever, is a major point of focus when addressing operating efficiency.

The Solution - An upgrade delivered via partnership

As part of its on-going demand for greater processing power, Sanger recently made operational a fourth data hall within its existing data centre facility. Comprising more than 400 racks and consuming 4MW of power, the Institute is now the largest genomic research data centre in Europe.

Additionally, the DNA sequencing equipment on which the scientific effort depends are protected by individual APC by Schneider Electric Smart-UPS™ uninterruptible power supply (UPS) systems. Ensuring resiliency within this distributed IT environment is essential, ensuring that the Wellcome Sanger Institute avoids outages and any losses of sequencing or research data.

As part of the upgrade, Sanger worked directly with EfficiencyIT (EiT), a specialist in data centre design and build, and UK Elite Partner to Schneider Electric. Having worked closely with the Institute for a number of years, whilst forming a long-term, strategic partnership with Schneider Electric, EiT were able to recommend the best course of action in terms of monitoring for this data-driven and distributed IT environment, ensuring that its scientific research would continue without interruption from downtime or from unexpected outages.

EiT specified Schneider Electric EcoStruxure IT Expert™; a cloud-based Data Centre Infrastructure Management (DCIM) software platform, which enables the user to manage all of the key infrastructure assets and improve the overall efficiency of the data centre.

Installed by EiT’s software experts, the solution provides the Wellcome Sanger Institute with insight into the operation of all key infrastructure assets in the data centre, including APC by Schneider Electric NetShelter™ racks, APC rack-metered power distribution units (PDUs), Smart-UPS™ uninterruptible power supplies (UPS) and cooling equipment.

 “Partnership is absolutely crucial for today’s data centre environments.” Nick Ewing, Managing Director, EiT, said: “By listening closely to the Wellcome Sanger Institutes’s challenges and understanding the paramount importance of their work, we were able to recommend the most cost-effective and innovative solution to support their business objectives. As such, they selected EcoStruxure, which gives them greater visibility into the IT and power infrastructure, whilst ensuring they can drive both operational and energy efficiency.”

EcoStruxure IT Expert™ is highly scalable and quick to deploy and install, allowing thousands of devices to be connected and discovered in less than 30 minutes. The software is completely vendor-neutral, allowing the user to manage all of the critical infrastructure assets on their network within a single solution, leveraging Data Analytics that enable smarter real-time decision making and ensure that any unexpected issues are identified and quickly resolved.

The platform, where data from assets is stored, pooled and analysed, is highly cyber secure and GDPR compliant, allowing users to view all of their assets at any time, from anywhere and on any device. Additionally, security, software and patch updates are automatically issued via the Schneider Electric Cloud, which is hosted on Microsoft Azure to ensure customer information is kept safe and secure.  

Previously, the Sanger facility had not utilised a unified management platform for its hardware assets. Such systems as were in place had grown organically as the data centre grew. According to Simon Binley: “It was perhaps natural that the focus was on the IT equipment housed within the data centre and not necessarily on the power train. The EcoStruxure solution unifies those two important data centre elements, giving us greater visibility of the entire estate.”

EcoStruxure IT Expert replaces that disjointed system with a single management console that allows a much more comprehensive and integrated view of the operation of the entire facility. “Visibility of power utilisation and assets throughout the data centre is the first step to build a complete understanding of what the facility looks like,” says Binley. “From that we can make an informed decision on how to make it more efficient. Having the ability to see and manage more than 400 racks of critical IT equipment, and all the components contained within each, gives us a significant advantage and enables a massive improvement to the operation of the data centre.” 

The Benefits and Results

EcoStruxure IT Expert was chosen, says Binley, because it was considered best in class for the Wellcome Sanger Institute’s requirements after examining several competing products. It was well suited to the existing infrastructure in the data centre, most of which has been provided by Schneider Electric, but because of its standards-based open-platform architecture, it can simply and easily integrate with hardware from other vendors.

As well as improving the operation of the data centre itself, EcoStruxure IT’s management capabilities can also be extended to other distributed assets throughout the Institute and the broader Wellcome Genome Campus, including is sequencers. Several other communications equipment rooms distributed throughout the Campus are also made visible within EcoStruxure IT’s single pane of glass.

Another benefit of EcoStruxure IT is its cloud-based architecture, which enables the Institute to outsource some of its critical IT requirement to third-party colocation providers and still manage the infrastructure internally. The solution provides the institute with complete visibility of IT health, ensuring that both the data centre manager and any accompanying maintenance or service providers, such as EiT, are instantly notified via email and the EcoStruxure IT Expert™ smartphone application should a power outage occur. In this way it allows all key stakeholders to have visibility, whilst enabling rapid collaboration to help quickly resolve critical IT issues.

The payback for installing EcoStruxure IT will be seen through reduced operating costs, especially in terms of a reduction in energy consumption. “From a power and energy perspective, we expect to save between 5 and 10% over the first two years in the data centre itself,” says Binley. “We can do that by raising the room temperature in the data halls, currently set at 19C to 21C. For a 4MW facility, that represents a significant cost saving thanks to a reduced cooling effort required, and as the product matures, we would look to increase that even further.”

The Institute has ambitious targets for improving the Power Usage Effectiveness (PUE) rating for the data centre from between 1.6 and 1.8 where it is now, to around 1.4, which will yield even further cost savings. “Any improvement in PUE and energy efficiency automatically reduces electrical spend and allows us to repurpose investment into other areas,” says Binley. “That means more equipment, more scientists and more benefit to humanity, which is the primary focus for the Wellcome Sanger Institute.”

AI will shape the future Artificial Intelligence will become a leading technology in the future, from helping doctors find cures for some of the world’s most resilient diseases, to training, upskilling and assisting workforces.

We will see more time consuming and repetitive tasks become streamlined by AI - from the basics, such as setting up impactful PPC or social media advertising campaigns, to the complex such as building new websites and content to feed the algorithms. This will see the role of the marketeer shift completely away from set up and administration, to focus more on higher level strategy and creative thinking: how to engage with consumers, clients and customers in a way that is truly personalised and authentic. Brain power will replace tech ability - the human mind's aptitude for thinking flexibly and responsively will be key for successful marketers.

Of course, the alleviation of admin extends to the management of resource and skills too. Take agency management for example – rather than spending time drafting briefs, checking in on deadlines and chasing down suppliers, marketers will be able to rely on AI to manage these processes for them.

AI will grow from strength to strength, but also it has its draw backs – it isn’t as simple as machines replacing humans. Future businesses will need to invest in data scientists and engineers who can build AI and ML models to do the jobs required. Even then, we need huge amounts of clean data for AI to function effectively. If the initial data has any bias, the algorithms that AI use can become systemically prejudiced or inaccurate, damaging the brand and costing businesses money. Marketers need to be wary of these issues when embracing the technology.

Other emerging trends must also be considered as part of the wider marketing and business approach. Take analogue as an example. We are seeing analogue coming back into fashion, and this links back to consumers desire to feel something tangible and they want to feel as if products are genuine. The sweet spot will be balancing tech demands, with the want and need for personalised and authentic products and services.

This desire for authenticity will see the rise of the co-bot; humans working in partnership with AI to produce quality services and products. AI will streamline processes and help with automation, whilst humans use their emotional intelligence to create meaningful products and experiences. This will see the budgets available for creativity in marketing increase, as manual tasks which would have been completed by entry-level team members are instead carried out using AI, leaving more budget for creating added value to clients and customers.

In the first instance, the greatest challenge when embracing new technologies for marketers will be the implementation of machine learning models. This will be essential to ensure that the work this new technology completes is accurate. As such there will be a huge need for data scientists and engineers to create machine learning models that are effective.

The hard work for marketers will then become the conceptual part of the job. Whilst AI will take care of defining audiences and setting up ads, marketers will be required for building creative, immersive and experiential campaigns that work across a plethora of personalised audience types. In the future the marketing objective will be to ensure a business stands out from the crowd and offers truly engaging services and products for clients.

Many large enterprises worry that Artificial Intelligence (AI) is too complex, time intensive and costly to implement and deliver business value. Just finding the right AI use case, some believe, is a daunting prospect, and failure to meet deployment deadlines means delayed ROI. These are valid concerns for business decision-makers. However, some of these perceptions are far less valid, and such concerns are no match for today’s AI.

As more AI use cases emerge, the hybrid workforce (human and AI collaboration) takes shape across various industries. 2020 is poised to be the year people realise AI is not going to take their jobs but will make their jobs less mundane and more satisfying. This is through the rise of Digital Employees – which represent the next great chapter in human-machine collaboration. By combining an intuitive conversational interface with back-end integrations into enterprise systems, these solutions empower all users — regardless of technical proficiency — to easily access information and services.

This trend will put pressure on enterprises to demonstrate that they’re ready to offer reskilling and new job opportunities for employees impacted by AI implementations. Employees, meanwhile, can stay ahead of any AI-enabled changes in their companies by identifying how they can adapt and change their roles and skills for the Future of Work.

Staff who traditionally spend more time improving existing processes than handling day-to-day tasks will be happy to learn they’ll also have more interesting work to do. Not every task will be able to be fully automated. Digital Employees can learn enough of a process to help automate bulk requests, but it will still need to ask for human assistance if and where automation takes a process as far as it can go.

Most enterprise AI deployments previously required hours of meetings and a broad team of deployment specialists to get a use case up and running. Now, the process is as simple as downloading a game onto your iPhone. You’re now able to log onto a marketplace where Digital Employees are arranged according to skills. You select the ones you need for your business and “interview” the AI-powered worker before cloud-sourcing your new employee. If the employee meets your company’s standards, the solution will self-configure and integrate to your company’s cloud-based servers and systems — meaning ROI comes in days and weeks, not months or years.

Most businesses are aware of the benefits and opportunities of AI. However, the best way for businesses to facilitate positive experiences is to clearly communicate how AI will change worker roles in the short- and long-term. By making it clear that you’re not hiring Digital Employees to remove a workforce, but rather to augment it, you’ll immediately address your workers’ biggest fears.

A current joke among data scientists is “If It's built in Python it’s ML (machine learning), if it’s built in PowerPoint it’s AI.” Businesses have been talking a big game about industry advances through increasing automation, but the reality is that AI is still far more notional than functional. In fact recent articles have shown that one of the most widely hyped advantages of AI, increasing personalization and targeted advertising, barely performs better than the non-targeted kind and that in general marketing spend confuses correlation with causation: punters didn’t buy because we gave them a coupon or showed them a specific ad, they got a coupon or saw a specific ad because they were already likely to buy. 

Beyond that, we’ve seen endless stories about AI discriminating against black patients when allocating health care resources, against the disabled in screening job applicants by video interview, and against women in determining credit limits even for married couples with identical credit scores and other financial information. Nobody wants to be next in the headlines for this kind of discrimination, yet businesses and governments seem endlessly seduced by the promise of AI, despite extremely dubious rates of success and the immense cost of building and maintaining these systems in a market where data scientists command immense salaries. 

So where is it working? There are some real success stories in reducing time to completion and freeing up humans from repetitive tasks in various industries and predicting failure points in complex systems. Examples include predictive maintenance for manufacturing and field equipment, predicting load balancing for complex systems like telecommunications infrastructure, improvements to transcription and translation software, and automating process flows like billing and payments, producing documents, and search and categorisation for massive amounts of paperwork. In other words, it works best in environments where there is low risk of human rights violations because it’s working with abstract concepts like inventory or infrastructure rather than making decisions about people. 

As discussed in my recent position paper Stemming Sinister Tides: Sustainable Digital Ethics for Evolution, AI does have the potential to actually mitigate bias in many of the disastrous examples above, but at the moment it continues to replicate existing societal biases because that’s all we have to train it on. In a way, many of these scandals are simply pointing out in procedural terms the biases that we humans have introduced against one another. It’s up to everyone involved in designing and building AI, from business leaders to software engineers, not to accept this state of affairs and allow it to be encoded into the fabric of our digital lives, to shrug our shoulders and say that the AI is only doing what we tell it to, but instead to push for a better world for all, online and off. 

With all the noise currently around digital transformation projects and new technologies many CIOs are likely to be considering their next strategic IT choices. However, despite considerable outlay on technologies that promise to digitise and optimise core business processes, we still see many organisations drowning in complexity caused by ill-advised technology investments that do little to solve their fundamental business challenges.

We’ve seen businesses experience rapid growth through expansion or acquisition with multiple reporting systems that they are yet to consolidate; or those running disparate accounting systems put in place to manage different countries’ import/export requirements or tax regimes. These organisations ultimately have little visibility of their organisation-wide operations and are therefore unable to make effective decisions or even tactical ones. And for all those businesses where visibility is obscured by rapid expansion, we see just as many whose plans are constrained by legacy business systems or outmoded processes.

The right technology choices can provide businesses with significant advantages, but before leaping in and making a significant investment, CIOs need to fully and rigorously evaluate their organisational challenges and consider where new technologies can add value.

Getting it right from the outset is essential. According to a data from management consultancy, McKinsey, in spite of the growing number of digital transformation projects underway in businesses across the globe, the success rate of these projects is disappointingly low. Only 14% of business executives claimed that their digital transformation projects have delivered on sustained performance improvements, just 3% reported successfully sustaining business change.

The sad truth is that many businesses have been putting resource, energy and millions of pounds into new technologies − without really considering how they will resolve their business-wide operational challenges. Limited time, resource and budgets, and a failure to grasp the challenges that come with business change, are also key contributors to IT project failures.

Identifying the challenge and solution

To avoid a costly mistake, and before an expensive IT buying decision is made, CIOs need to assess how innovative solutions can help them solve common issues such as business-wide visibility on operations, scaling for rapid growth, or the eradication of inefficient, ineffective and costly processes.

CIOs or business managers are right to see technology as the answer to many of these issues, but it’s how and where it is deployed that matters. The real business value of any IT investment lies in the enhanced and optimal operation of its processes and business owners need to build a clearer understanding of exactly how their organisations operate.

The starting point for any business looking to make a change needs to be an evaluation of their current processes: consider if they are optimal and if not, how to re-engineer them for better performance.

The areas for improvement highlighted by this evaluation process immediately pinpoint where new technologies can be applied to successfully deliver significant business value. These new-found insights enable businesses to use technology and improvement processes to gain better visibility of their operations, allowing them to make informed decisions, boost responsiveness to customers and drive profitability.

The focus needs to be on getting to the root of the issues that need addressing within your business and finding a technology partner who can advise on how to solve your specific challenges such as making process efficiencies; or even opening up revenue streams in new territories.

Two examples show the benefits from organisations taking this deeper approach to identifying and evaluating their strategic challenges. One UK firm with multiple overseas subsidiaries identified the need to consolidate disparate accounting systems and invested in a new single platform; this has not only reduced resources needed for financial reporting but also delivered better visibility of operations and much improved insights to management based on data. In the second case, through a deeper examination of core business processes and its customer needs, a fast-growing business realised that replacing its current systems with a single system would enable it to rapidly set up an overseas sales operation that achieved easier compliance with local sales taxes and import controls while delivering better customer service and improved overall management information.

Without documenting core processes and building a clear picture of operational needs and areas for improvement, today’s IT innovations are no more likely to help a business achieve key goals than their current technology set up. Business owners or managers need to be wary of the hype around digital transformation and engage with experts who can help them review, document and realise true visibility of their business operations and identify where technology can be applied for improvement.

A decision on new technologies should not be taken lightly, and certainly not based on the promise of business transformation: understanding your processes and operational inefficiencies and applying technology to improve and transform these is the real key to effective decision making around IT and ensuring the investment delivers as promised.

Artificial intelligence (AI) is a very popular buzzword these days. Generally, it refers to the use of computers and computer aided systems to help people make decisions, or make decisions for them. It usually relies on large volumes of data or sophisticated models to help understand the best ways to make sense of all the information and draw intelligence.

In trade finance, AI is particularly helpful in analysing quantitative data; there are usually a large number of repetitive small transactions. The repetitive nature of trade finance means that there is a lot of non-traditional data at our disposal.

This means AI-driven models can be very efficient for data analysis and revealing intelligence relating to small companies that traditional trade analysis tools cannot cover.

AI-driven supply chain analysis for SMEs

Trade finance as a business is only beginning to be digitalised and inefficiencies therefore remain, but that is changing very rapidly. A group of institutions, including banks, pension funds, institutional investors, funders and trade associations, came together earlier this year to form the Trade Finance Distribution Initiative (TFD Initiative).

An important component of the Initiative is bringing new levels of transparency to a market that is opaque and paper-based. This allows institutions to identify attractive financing opportunities where it previously may not have been possible.

Beneficiaries of this include micro, small and medium-sized enterprises (MSMEs) and corporations based in emerging markets, where credit providers simply don’t have enough data, accounting or trade information to make sound decisions.

Tradeteq’s AI technology creates credit scoring models to analyse the history of a company and their transactions. It uses vast amounts of public and non-public data, including data on each company in the supply chain as well as each receivable. From this, we can create a sophisticated evidence-based, credit scoring model.

Tradeteq also allows a company to receive early warning signs when a supplier or counterparty is in distress or at risk of not fulfilling credit or trade requirements. Tradeteq’s algorithms predict the effect on each business to ensure the risk of interrupted trade flow is minimised.

For larger international banks, this can create more trust between them and smaller corporations, including those further down the supply chain where trade finance banks typically cannot provide coverage.

Overcoming the data challenges

Even though the process seems simple, substantial challenges remain due to the availability and reliability of the data. The traditional approach to credit scoring relies heavily on company accounts and the data within them, which can be out-of-date. This is a major barrier and prevents many small companies from accessing trade finance.

A lot of financial organisations are gradually collecting their historical data and merging it with current operations to create a single source. I don’t think that this has been very successful so far, but we welcome the strong effort to put data into a reasonably uniform format.

The second challenge is the legal aspect of accessing the data once it is available and retrievable. When looking at the cross-jurisdictional legal issues, there are still things that need to be resolved in order to ensure compliance with all local and international laws.

The third challenge, which probably the easiest to resolve, is modelling accurately when the data has been validated. This is where AI can become particularly useful.

Closing the trade finance gap for MSMEs

Once the data is available in its entirety, counterparties or funders can use AI to observe patterns in a small company’s trade and payment history, including non-payments, then look at the history of comparable companies in the supply chain to identify and assess undue risks.

As it stands, this cannot be done efficiently, at speed or at scale. AI can help funders make more confident trade predictions for companies, opening up trade finance access for many small companies who would have otherwise not had that access and reduce the trade finance gap.

Following the recent resurgence of the market and the development of advanced AI solutions, organisations are faced with a unique opportunity to reimagine the way they manage their physical assets.

However, despite this revolution, it seems that many major oil and gas companies are not making the most out of these new advances in AI, nor the multiple sources of data available to them across their operations.

Although organisations often collect vast amounts of data, it’s clear that in the oil and gas industry at least, many remain challenged to be able to use it. To put it into context, one industry study found that less than 1% of the data being generated by 30,000 sensors on an offshore oil rig was actually used for decision making[1].

Our own research revealed that 71% of oil and gas asset managers still rely on just a single source of data to analyse their asset performance and risk management. This is a missed opportunity to maximise production, reduce waste, eliminate unnecessary downtime, and reduce the risk of a containment or safety incident.

The way companies with big infrastructure (e.g. chemicals, oil and gas, power, transport) typically inspect their assets to avoid failures and breakdowns has, until recently, followed a time-based approach. However, thanks to AI, 3D digital twins and machine learning, actionable insights can now be derived from multiple sources of data with speed.

Using AI in this way has been proven to lead to cost savings of 10% to 40%, yet only 18% of companies have adopted this approach according to those we surveyed. The slow pace of adoption, in spite of the efficiencies these innovations bring, sits somewhat at odds with the commonly held view that industry needs to pursue operational excellence to maintain growth.

If organisations can begin to draw on multiple sources of data, they’ll be able to derive previously unprecedented levels of insight from their assets. In time this would allow businesses to reinvigorate their maintenance approach, improving productivity and uptime.

While the majority of organisations are yet to transform their asset performance management systems, interest in emerging technologies is growing rapidly. Our research revealed the greatest interest is in big data (25%) and AI and machine learning (23%), while many are also excited by the possibility of 3D digital twins (19%).

Anecdotally, however, many organisations are reluctant to adopt new systems due to the required training and skill sets they can demand. Yet, there are now platforms available, including Lloyd Register’s AllAssets, that have been built with ease of implementation in mind, allowing oil and gas companies to rapidly modernise their approach to managing physical assets.

Advanced technology solutions are giving organisations a unique opportunity to reimagine their asset environment. It’s our view that it is time for organisations to move on from their legacy systems and enjoy a new age of operational success.

As cloud computing continues to grow, cloud management systems keep on generating huge amounts of data. To provide the best possible customer experience and to manage cloud costs effectively, in-depth analysis of this data needs to take place. Yet, clear insights cannot be drawn from these huge volumes of data using traditional rule-based systems. This is where AI is being utilised to analyse these enormous data sets in order to help enterprises get valuable and in-depth insights about their systems. 

Used in this way, AI facilitates better alerting, stronger monitoring of availability and greater identification of the root cause of failure events. AI systems can predict outages, help provide proactive infrastructure management, and ensure better service availability.

Another area where AI is having an impact is in IT service desks. Unlike cloud management where the bulk of data is machine generated, most service desk data is generated by people. The manual processing of this data can slow down productivity, especially when it comes to responding to service desk tickets. Enterprises are benefitting from a powerful form of AI called natural language processing (NLP). When used in chatbots, NLP is boosting service desk productivity and response times.

NLP has the ability to recognise and understand the context of each service desk ticket as they are submitted. It can then automatically assign the ticket to the most suitable technician for resolving the ticket based on similar interactions that have taken place. This not only accelerates ticket response and resolution times but makes it easier for service desk agents who can focus their energies on other tasks. 

As AI becomes increasingly important across different aspects of managing IT infrastructure, enterprises must work to adapt to the natural changes that occur when implementing any new technology. While AI is enabling decision automation across the stack, it is important that businesses ensure their existing process hierarchy also factors in the decisions reached from the AI system. 

Decision-making has traditionally been a question of yes or no for enterprises. Yet AI has introduced an element of probability to decision-making processes. Today, many enterprise monitoring systems issue alerts when there is trouble with a particular server but an AI-powered monitoring system will advise that there will be a 60 percent chance a particular service will fail in the next hour. The impact of these advisories can be factored into the organisation's IT workflows.

Ensuring successful adoption of AI across the enterprise comes down to designing and modifying existing hierarchies to make them flexible enough to accommodate probabilistic decisions across various workflows in the organisation. Enterprises adopting AI will need to be ready to quickly adjust existing workflows in order to reap its benefits. 

To the average person in the street, data migration may well seem like an irrelevant cog in the corporate world; something they don’t need to know about, let alone concern themselves with. That is, until that seemingly meaningless piece of IT jargon starts to cause personal pain. That’s exactly what happened to TSB customers in 2018, when a massive scale migration of customer data from an old system to a new one went awry. This initially gave customers access to other people’s confidential account information, then it resulted in the being locked out of their accounts for days, and in some cases weeks, while the problem was fixed.

Of course, the pain felt by those individual customers is just one page in a catalogue of errors but the business consequences for TSB has been much greater. Immediately after, the regulators came calling, staff were put under immense pressure, and the negative brand impact across social media left a lasting bad taste. Since the failed data migration, the bank has witnessed management changes, a legal challenge, further IT issues and, in December 2019 just 18 months after the event, it announced 82 branch closures. Whether these more recent challenges are related to the very public data error or not, it is fair to assume it will have been a contributory factor. According to Sky News, the debacle “sparked hundreds of thousands of customer complaints and led to TSB reporting a pre-tax loss of more than £105m last year.”

Large corporates are challenged by their need to move data from one system to another more than ever before due to legacy system upgrades or mergers, as in the case of TSB for example. This is exacerbated by the fact that technology adoption is advancing exponentially, requiring critical data to be moved more frequently. The stakes are rising and so are the risks. And, it’s often the crucial job of getting data from A to B where errors occur.

Looking at all the data migration projects Qbase has been engaged in over the past five years, in 63% of cases we were brought in due to a failed implementation. What this suggests is that roughly three out four software implementations fail to plan properly for data migration and suffer as a result. An interesting correlation is that according to Merkle Group, 63% of all CRM platform adoptions fail at the first attempt.

Data migration is a strategic consideration for any software implementation and should be part of the initial planning process. There are four main reasons where things go wrong.

Leaving it in the hands of the SI

We find that around four out of every five new software implementations involve an external Systems Integrator (or Solution Implementor). And, in most cases, the data migration process has been allocated to the list of SI responsibilities. While SIs are platform experts, they often lack the knowledge to successfully manage a data migration. In situations where an SI will be handling the migration, its crucial to ensure their methodology is completely watertight and that data isn’t treated as something that should merely be uploaded to see what happens.

Inadequate project scoping

Poor scoping crops up time and time again as the reason behind failed projects. In our experience it is usually a case of the requirements not being thought through and laid out in full, or misinterpretation of what those requirements mean from an implementation perspective. The crux of the issue is usually that either initial analysis of the situation, or the process of engaging stakeholders properly in order to get the facts straight from the beginning, has failed.

Setting unrealistic expectations

Effective data migration can take the same amount of time as the actual platform development yet, in many cases, it’s often viewed as a quick switch. Anyone that says otherwise is either trying to grease the wheels of a deal or doesn’t know what migration involves. Third party consultants and internal IT teams need to set expectations with the project sponsors and the board. It may make for a longer implementation timescale, but its likely that this time will be far shorter than the one faced if the project fails and has to be put back on the rails.

Failure to overcome resistance

Resistance is an inevitable part of change, especially when people believe the change is not in their best interest. Unless those resistors are tackled, the ability to migrate data effectively will always be flawed. The best way to illustrate this is through example. The last failed migration Qbase was brought in to was floundering because the SI has let the client dictate that their new platform should follow the same processes as the old ones they had used for years. They wanted the benefits of the new system but with the comfort blanket of the processes they’d spent years fine tuning. This led to the new application being forced to carry out the old processes, compromising the data model and leading to the patching of data and processes. Resistance must be addressed at the scoping stage.

As far as data migration is concerned, the failure to plan is tantamount to planning to fail. TSB is just one high profile casualty of this, but experience shows that around three quarters of all IT projects have issues resulting from poorly planned and handled migration. Organisations need to balance their focus on the platform and the data.

Artificial Intelligence (AI) is already embedded in the business world, just as it is already embedded within our daily lives. Different people choose to define AI in different ways, but broadly we can consider AI as machines acting with some form of intelligence. Capabilities such as machine learning, natural language processing, image recognition, optimisation, problem solving etc. are all forms of AI that we can see commonly in action today as we use our smartphone’s and increasingly connected digital ecosystem. They guide us to our next destination, recommend a song we might enjoy, or use facial recognition to grant us access to our phone or computer.

AI in the enterprise is similarly becoming embedded within business systems, processes and productivity applications. Often this will not be in the form of a project to “implement AI”, but through the continual process of business improvement across all areas of the business, or driven by a need to transform the business in order to adapt and compete in a rapidly changing world. Some of the key areas AI is playing a defining role in the enterprise today include:

1. Automation - AI is increasingly allowing us to automate tasks that previously required human intervention or specialist knowledge. The field of Robotic Process Automation allows us to integrate intelligent bots, or agents into business processes to specific tasks such a processing customer service requests, or performing system monitoring and alerting. Automation can include relatively simple discrete tasks, through to much more complex areas such as financial decision making, or legal advice.
2. Optimisation - Machine learning is being used to assist in solving complex optimisation problems such as intelligent scheduling and routing, optimising asset utilisation, predictive maintenance, pricing and risk management. Increasingly these capabilities are built into key business systems, but many organisations also have built or are beginning to build dedicated Analytics, Machine learning or AI teams to develop models or algorithms to support business decision making in these areas.
3. Personalisation – In many respects, advertising, marketing and SEO have led the use of AI in organisations. The creation of tailored experiences for customers and staff are increasingly becoming the norm, and AI allows organisations to do this at scale. As systems learn more about our behaviours, patterns and preferences they are able to tailor offers, information or even system behaviour, allowing us to be more productive.

Together with ASO, the organisers of the Tour de France, NTT has been working to revolutionise the fan experience, and engage an increasing digital audience. Using AI together with IOT, we are able to make race predictions, provide live data driven insights, and provide services to fans by the roadside such as the estimated time of arrival of the peloton. All these contribute to the ability of ASO to provide new digital services to their fans.

Another great example of AI in action is in the rapidly evolving space of cyber security. Using AI filtering through vast quantities of data we are able to assess and respond to potential cyber threats in real time, something that humans simply could not do.

Are the majority of companies comfortable with the technology and how it can be used, and already using it? Or, are many still unclear as to exactly what it is and what it offers to the business?

I believe almost every enterprise is now using some form of AI in their day to day business operations as it becomes embedded within the tools and technologies that we all use daily. The vast majority of organisations would also recognise the importance of AI and its impact on their future business strategies, however there are a much smaller, but growing number of organisations that are actively working to develop strategic AI capabilities.

There are many challenges in developing working AI solutions. Access to the required skills and expertise is a key barrier. It is a complex field that requires a multi-skilled cross functional team with the right business, technology and data scientist skills some of which are difficult to find in the market. 

Availability and access to the required data can also be a challenge. Organisations that have a clear data strategy and solid data platforms and pipelines will be much better positioned to delivery business benefits with AI, allowing it to be fully integrated into business systems and processes. The biggest challenge for most organisations is in developing the culture of using AI and the insights it can produce to actually transform the business. Businesses are used to using financial data to drive strategic decisions, but are not yet used to using the operational, organisational and customer data to rethink how their business operates. Entrenched business models, organisational hierarchies and business processes can be hard to change.

We would argue that it is important to get started on your AI journey, building skills and experience, and testing new ideas. Increasingly technology vendors including the major cloud platforms are providing tools and cloud services that do make this process simpler. Selecting real, but achievable use cases, and starting with the simpler supervised learning techniques that are easier to evaluate and explain before moving on to more complex techniques such as unsupervised learning. 

The opportunity to automate manual tasks and gain new insights to support business decision-making is set to have a hugely positive impact on organisations. These newly formed insights will play a crucial role in changing the clock speed of business and freeing up workforces to focus on higher value activities.

While many organisations are still in the early adoption phase, we’re already seeing some impressive results. In the insurance industry, for example, one company is now using AI capabilities to discern which of its customers are most likely to renew their premiums, before automatically contacting them via SMS. This company has witnessed a 60 percent engagement rate and 30 percent renewal rate from that text message bot campaign alone. 

Despite these promising early successes, there are challenges to overcome before AI reaches its full potential. According to research conducted by CompTIA, only 29% of businesses are regularly using AI. This low rate of deployment indicates companies are struggling to get to grips with AI, and whilst there are many ethical considerations, connectivity challenges are also undoubtedly a factor. Many organisations approach AI with the unrealistic expectation that they’ll be able to just plug it in and it will begin delivering the desired benefits. However, the siloed data stores and lack of integration between enterprise applications that exists in many organisations severely hinders AI’s ability to influence the digital ecosystem around it, rendering it an expensive yet fundamentally limited tool.

Getting the most value out of AI is about more than just knowing the right questions to ask, it’s about being able to connect AI engines to the right sources of data. To do so, organisations need a much more fluid approach to connectivity; one that allows them to decouple very complex systems and turn their technology components into flexible building blocks. They ultimately need to become more composable, so that AI can be plugged in and out of any data source or capability that can provide or consume the intelligence it creates. 

This can best be achieved with an API strategy orchestrated in an application network, which allows organisations to easily connect any application, data source or device via a central nervous system of sorts, where data can flow freely. This central nervous system, or application network, is how the ‘brain’ of AI can plug into a business’ digital ecosystem to consume its data and then provide valuable insights and actions.

“Nuxeo recently launched research with UK Financial Services (FS) workers which included questions about the value and impact of artificial intelligence. Although two-thirds said AI could transform the sector and more than half stated that the creative use of AI made organizations more attractive employers, a full 58% also said their firms lack sufficient AI talent and expertise.”

“There are very similar challenges with AI in the wider business world. Despite appreciating the potential of advanced technologies like AI - and in some cases already using them in small pockets of the organisation - businesses are generally not well positioned to fully leverage all of the benefits AI can deliver.” 

“For all the hype around AI, and the general excitement about its potential, it has arguably added the most value so far to businesses in perhaps less glamourous, but still highly valuable, ways. Some of the most widely used AI applications tend to centre around improving search outcomes and automating routine back-office processes. Using AI for such purposes is not a bad thing – it adds value, addresses specific operational pain points and helps improve productivity by freeing up time spent on back-office tasks to spend more productively elsewhere.”

“But AI can deliver much more than this. Our survey revealed that transforming customer service delivery is a key focus for AI ambitions, and AI can also be used to extract insight from customer data, information that can be used to deepen customer understanding, mitigate business risk, enhance the customer experience and, ultimately, drive digital transformation.”

“In this era of content and data, AI can play a key role in delivering more value from both existing and new information. Given that each organisation’s content and data requirements are unique, AI can predict, classify and enrich content to surface the information that is most important to a business. Beyond utilizing common, but generic AI services, most organisations will benefit from developing their own, custom and business-specific AI models that will better serve their unique business needs and data requirements.”

“13% of our research respondents believe their organisation’s inability to adopt AI quickly enough is one of the main challenges it faces in 2020, so it’s something that needs to be addressed sooner rather than later. People can also be resistant to change so it’s important not to jump in feet first with AI. A gradual migration path from the ‘old’ world to the ‘new’ is the best way forward: a managed modernisation journey, which helps address the most acute pain points and delivers quick wins from AI, without incurring new risk or disrupting the organisation with too rapid a change. AI can be genuinely transformative, but a thoughtful approach to modernisation will produce better results over time.”

This year’s Data Centre World marks a big moment for Riello UPS?

Well, Data Centre World is always our biggest and best show of the year, but 2020 is shaping up to be that little bit extra special. The event coincides with the official UK launch of our new and improved Multi Power modular range.

We’ve expanded the series with two new power modules designed to meet the needs of smaller data centres. They’re both 2U in height and offer 15 kW or 25 kW of power. These new modules complement the 42 kW module that’s proved one of our best-selling products in recent years.

And to house these two new power modules, we’ve also introduced a dedicated new cabinet (PWC X) ideal for space-restricted installations. It measures 60 cm wide and 120 cm high, compared to the 2-metre tall chassis we use with the 42 kW modules.

What’s the thinking behind these new power modules and a smaller cabinet?

Modular UPS systems are increasingly the go-to option for high-end data centres thanks to their combination of performance, scalability and efficiency.

Our extension to the Multi Power range enables smaller data centres and similar mission-critical applications to enjoy these benefits too.

The new cabinet holds up to five of the modules. That’s up to 60 kW N+1 if you’re using the 15 kW modules, or 100 kW N+1 for the 25 kW versions, in a single frame. You can also parallel up to four cabinets together for extra power.

There’s still the 42 kW modules for higher-power installations. The standard Multi Power cabinet can take seven modules, so that’s 252 kW N+1. And again, parallel up to four cabinets to deliver maximum power of 1,008 kW N+1 in a single UPS system.

Efficiency and reducing energy consumption are always hot topics – does the new Multi Power tackle this?

One of the major benefits of modular UPS is the inherent scalability they offer data centre operators. This has the knock-on benefit of cutting energy waste.

Not too many years ago, UPSs tended to be oversized at initial installation to allow for future expansion, which meant they often ran inefficiently on low loads.

Modular systems enable the UPS to closely mirror the load requirements at installation. Then when data centres need more power or battery autonomy, operators can simply plug in extra power modules or add cabinets in parallel.

With the potential for more than 1 MW of power plus redundancy in a single modular UPS system, such “pay as you grow” scalability future-proofs your power protection needs without wasting floor space and electricity, while it’ll also cut down on unnecessary air conditioning costs.

Thanks to its high-performing, specially designed components, the Multi Power delivers 96.5% operating efficiency in double conversion online mode. But it also features a special mode that provides high efficiency (up to 95%) even when running loads as low as 20-25%.

Energy Saving Mode sees the UPS run in online mode with the inverter continuing to support the load, so protection isn’t compromised. But depending on the load level, the Multi Power’s microprocessors only activate the necessary number of modules to power the load.

All the other power modules are idle with the inverter closed and charger switched off. This saves unnecessary power use and maximises system efficiency.

Inactive power modules can remain in this energy-saving status for a maximum of 15 hours. Then it swaps with one of the other active modules. This makes sure the power modules share the load reasonably equally, which ensures components age at a similar rate.

Data centres can be large-scale power users. Does the industry need to do more to cut its power consumption?

It would be unfair to accuse data centres of not taking their environmental responsibilities seriously. The industry's made great efforts to improve the efficiency of cooling, air conditioning and power systems, to name just three.

But the demand for data centre processing is constantly growing. All that processing and storage doesn’t come for free. It needs lots of electricity.

There’s also a growing feeling that the much-vaunted benefits of Moore’s Law are starting to level out. So much so that a recent Uptime Institute report reveals nearly two-thirds of data centre IT power (65%) carries out just 7% of the actual processing. This is because of ageing equipment and efficiency improvements plateauing.

We also can’t afford to ignore another major trend. The way our electricity network generates power is going through rapid change. 2019 was the first year where more electricity came from zero-carbon sources like renewables than fossil fuels such as coal and gas (48.5% versus 43%).

With challenging net-zero targets to meet, this development is surely irreversible. But such reliance on unpredictable wind and solar means National Grid has to be smarter in the way it balances supply with demand and maintains a stable frequency. 

Demand side response (DSR), which incentivises electricity users to store power and shift usage from busy to off-peak periods, will become increasingly influential.

We believe this provides data centre operators, many of whom currently have annual electricity bills upwards of £1 million, with an opportunity to rethink the role of their backup power.

All data centres deploy uninterruptible power supplies to protect against downtime and ensure a constant flow of clean electricity.

But in reality, how often are they really needed? More often than not, a UPS can be an essential but ultimately underutilised and expensive asset.

That’s why we’re working on an exciting pilot project that turns a UPS into a ‘Virtual Power Plant’ that takes part in demand side response without impacting on overall resilience.

Can you explain more about this ‘Virtual Power Plant’ concept?

We’re working in partnership with the energy trading arm of RWE, the biggest electricity supplier in Germany. Our Master+ solution involves one of our UPSs fitted with a special rectifier to enable bidirectional flow of electricity to and from the network.

This smart grid-ready UPS system is backed by premium lead-acid or lithium-ion batteries, along with RWE’s intuitive battery monitoring and communications software. This is crucial as it allows for real-time analysis and two-way communication with the grid.

We divide the UPS’s battery capacity into two distinct roles. There’s a part that purely provides emergency standby power in case of any disruption to the mains. But there’s also a ‘commercial’ section which stores electricity and feeds into the DSR scheme Firm Frequency Response, which helps maintain grid frequency within the required safe range of 49.5-50.5 Hz.

So you get the traditional safety net that a UPS offers plus the opportunity to reduce network charges and sell any surplus energy back to the grid.

If there is a power cut, any energy still stored in the ‘commercial’ part of the batteries automatically tops up the main backup to lengthen runtime.

Riello UPS is exhibiting at the upcoming Data Centre World event on stand D920. The team will be on hand to discuss its award-winning range of uninterruptible power supplies and industry-leading maintenance support. This year’s show coincides with the official UK launch of its new and improved Multi Power modular UPS range.

Also at the show, Riello UPS’s Technical Services Manager Jason Yates and RWE’s Dario Hernandez will be leading a special seminar showcasing its Master+ smart grid-ready UPS and energy storage solution. Join Jason and Dario in the Facilities and Critical Equipment arena from 10:45-11:10 on Wednesday 11 March.

With only 37% of global enterprises having implemented AI in some form (according to Gartner), there remains some way for businesses to go in their adoption. However, this figure is a 12% growth on the previous year, highlighting that AI is becoming increasingly embedded into organisation’s approaches. For the role of cyber-security and in particular, identity management, this is a positive sign of things to come.

Today’s wealth of data and computing power enables security providers to create tools to mitigate and prevent identity and privacy attacks. At present, many businesses remain at risk due to poor password security, software vulnerabilities, human error and the abuse of access and privileges. Verizon’s 2019 report found that compromised and weak credentials are cited as the cause for more than 80% of data breaches.

AI, on the other hand, is a step in the right direction of better security, by enabling the creation of predictive models that differentiate between ‘good,’ ‘bad,’ and ‘normal’ behaviour. This makes these models intelligent enough to proactively halt bot attacks and identify malicious activity. Specifically, pattern recognition, thanks to AI, is able to detect threats and viruses faster than previously imagined, enhancing businesses defences. As data volumes grow for businesses, the speed of intelligent predictions will only grow as AI models begin tapping into this wealth of intel.  

In the case of biometric identity, AI is being widely used within facial recognition, fingerprints and iris scans. It is becoming more widespread on personal and work devices, while enterprises are also deploying their own biometric security measures. However, trust in biometrics is still holding its deployment back. Okta’s Passwordless Future Report found that 86% of the respondents had reservations about sharing their biometric data, showing that work needs to be done to address how comfortable employees feel about its use and more transparency when it comes to how secure biometric data is.

In the current security landscape, security teams are often under-budget, meaning many organisations have few security analysts on hand to verify and contain threats, leaving them open to longer dwell times and large-scale breaches. Many teams do not have AI specialists in-house, leading to teams outsourcing AI specialists to bring in the specialism they need. In light of this, the arrival of productised security automation and orchestration tools cannot happen soon enough.

“It is fair to say that artificial intelligence (AI) is everywhere. Newspapers and magazines are littered with articles about the latest advancements and new projects being launched because of AI and machine learning (ML) technology. In the last few years it seems like all of the necessary ingredients – powerful, affordable computer technologies, advanced algorithms, and the huge amounts of data required – have come together. We’re even at the point of mutual acceptance for this technology from consumers, businesses, and regulators alike. It has been speculated that over the next few decades, AI could be the biggest commercial driver for companies and even entire nations.

“However, with any new technology, the adoption must be thoughtful both in how it is designed and how it is used. Organisations also need to make sure that they have the people to manage it, which can often be an afterthought in the rush to achieve the promised benefits. Before jumping on the bandwagon, it is worth taking a step back, looking more closely at where AI blind spots might develop, and what can be done to counteract them.

“AI maturity and usage has grown exponentially in the last year. However, considerable hurdles remain that keep it from reaching critical mass. To ensure that AI and ML are both represented by the masses and that they can be used in a safe way, organisations need to adopt certain best practices.

“One of these is making sure technologists who build AI models reflect the broader population. Both from a data set and developer perspective this can be difficult, especially in the technology’s infancy. This means it is vital that developers are aware of the issues that are relevant to the diverse set of users expected to interact with these systems. If we want to create AI technologies that work for everyone – they need to be representative of all races and genders.

“As machine learning inevitably becomes more widespread, it will become even more important for companies to adopt and excel in this technology. The rise of machine learning, AI, and data-driven decision-making means that data risks extend much further beyond data breaches, and now include deletion and alteration. For certain applications, data integrity may end up eclipsing data confidentiality.

“We will need to learn to live with the premise that there will not always be a perfect answer when using AIs. These AI systems learn from the data that are fed into them, so this is subjective to each use case. There may never be a time where we get the right results. With no precise check lists to overcome these situations, we must learn to adapt and provide new training and education platforms. These new training platforms will be vital to allow AI to become representative of all races and gender over the next few years. The talent pool is only set to grow, yet – the challenge remains to ensure it becomes even more diverse.

“As AI and ML become increasingly automated, it’s essential that organisations invest the necessary time and resources to get security and ethics right. To do this, enterprises need the right talent and the best data. Closing the skills gap and taking another look at data quality should be their top priorities in the coming year.”

Businesses around the world and in every sector will be completely revolutionised by Artificial Intelligence (AI) within the next five years - or so we keep hearing.

Realistically, however, AI has the same rate of development and growth as any other advanced technology. It is, unfortunately, constrained by the people researching, funding and implementing the technology to iron out issues in the field.

Perhaps more so than other technologies, AI relies on a large amount of people using it and feeding it vital data so that it can learn and progress. This means that its growth is indeed exponential - i.e. the more AI applications being used in real-world scenarios, the faster AI can progress. But the reality is that real-world limitations such as concerns over the power of AI and data privacy are still pervasive.

Real progress

These concerns also mean that AI’s progress in the business world often has two sides. Significant advances in AI’s ability to manage human tasks, for example, can be seen as both a positive and a negative depending on your perspective.

An AI application that gives employees in-call guidance on customer service, care or sales calls in enterprises, for example, might be seen as a troubling development by some people. Although, this is very much in use in businesses already, and AI’s ability to recognise (but not replicate) human emotions allows call operators to navigate sensitive conversations more smoothly, whilst also providing training for customer care best practice more quickly.

Great strides

The more AI programmes can analyse the data of enterprises, connected devices or internet sources, the more they can learn about business processes and consumer habits, thus improving operational efficiency and aiding sales and marketing initiatives. These benefits can only be felt if businesses embrace the technology wholeheartedly and understand the value, as well as the limitations, of AI in a business context.

Appreciating that AI must be fed a large amount of appropriate data, for example, is crucial to getting real benefits from the technology. Training an AI on every piece of information in a company or restricting the amount of data available to an AI, will not yield useful results.

Curiosity conquers all

Many companies are now overcoming fears about AI and recognising that while it is a powerful technology, it is only as powerful as we can train it to be as humans. This means there is still a long way to go before the majority of companies are comfortable with AI and understand it sufficiently to see real business benefits.

As AI becomes far more commonplace in a business context, as well as in our daily lives, fears and misunderstandings about the technology will surely be overcome by curiosity. As proven success stories begin to make the headlines - instead of speculation about the risks of self-driving cars - the real-world benefits of AI may live up to the hype after all.

Cloud-native app development allows organisations to build and run new apps based on cloud principles. This approach helps organisations build and run flexible, scalable and fault tolerant apps which can be deployed anywhere. Going cloud-native allows an organisation to iteratively transition their old services towards containerised, service-based, and API-driven platforms overseen by DevOps automation.            

Cloud-native development is about responding to change with speed, elasticity, and agility. This response is achieved through more frequent deployments that significantly reduce the lead time to react to change. 

Cloud-native app development is a model that applies across development and deployment of a product or a service. This requires a transition to many new practices, with one of the most challenging transitions to pull off being to build a DevOps culture.

DevOps

A DevOps culture is one where there exists a set of practices and processes that unify development and operations. Developers and operations have a shared understanding and responsibility for their work, which results in a far more integrated model to creating and deploying applications.

Evolving a DevOps culture and practices can be difficult for an organisation. Changing what your personnel do on a day-to-day basis is, by its very nature, going to be disruptive for an organisation. This is especially the case when one is combining the culture, procedures, and workflow of two separate departments into one new one with a different structure, mission, and set of processes.

Despite the time and effort taken to adopt DevOps, IT leaders know that the long-term benefit of making the transition far outweighs the initial difficulties of the transition. This evolution will inevitably take time, but once complete, the result will be shorter development times and applications that are more flexible for your organisation.

Evolving Modular Architecture

Another key part of cloud-native development is adopting a modular architecture, which sees the functions of applications broken down into independent modules. There is no single path to a more modular architecture. One path is through adopting what is called a microservices-styled architecture, which sees applications broken down into their smallest parts, independently of one another. This is well-suited for cloud-native apps as they share the same principles of elasticity and modularity.

However, these architectures are difficult to implement and maintain. Most microservices success stories see a monolith broken down into a microservices architecture. This is because analysing the existing monolith could help developers to see how the ‘parts’ of an application work together, which helps inform where the boundaries of microservices should be.

Those existing monolithic applications cannot simply be thrown away, since many of them have been dutifully and efficiently running a function for an organisation for many years. The focus should be on iterating towards cloud-native principles in these applications, rather than forcing them to comply with a standard and potentially identifying what parts of the monolith can be exposed via APIs. After all that, one can begin to break monolithic applications down into microservices or miniservices.

Automation and Self-Service Infrastructure

Manual IT tasks are a great source of lost time for development and operations teams. Automating away this work can free up time and resources among your team, meaning software is developed and deployed far quicker. Hence, embracing IT management, automation and tools that create procedures to replace labour-intensive manual processes is an essential part of a cloud-native strategy.

A similar idea runs through pushing for reusability when undertaking cloud-native application development. Recreating the same capabilities again and again – such as caching services, workflow engines, or integration connectors – can be a big waste of time. Modern middleware leverages containers and microservices and developers should take advantage of it as the capabilities it provides have already been optimised and integrated into the underlying container-based infrastructure. 

This philosophy extends to the infrastructure you use. Self-service and on-demand infrastructure helps teams quickly create consistent environments, which allow developers to focus on building applications without the time usually spent preparing infrastructure for projects. Containers and container orchestration technology help simplify access to the underlying infrastructure of an application, and provide life-cycle management for applications across different environments – whether they be private clouds, public clouds, or data centres.

Incremental Steps

Transitioning to a cloud-native development strategy isn’t something that can be done overnight. Rather, it should be seen as a gradual process and also as a learning experience. With few exceptions, such as startups, most organisations have established intricate IT environments that use on-premise applications, along with cloud platforms and services. It’s also not realistic for most organisations to unify all their systems and platforms into a  single architecture, at least not all at once. While you can immediately start using a cloud-native platform for your greenfield applications, transitioning your existing applications is a long process.

The key to such a successful cloud-native transition is to go about it with small and incremental steps. Begin by migrating monoliths and applications to the cloud first, either on-premise or off-premise. Then, begin to containerise workloads and establish container orchestration platforms, including monoliths. Then, look at those monoliths and assess which can be broken down into microservices or serverless function calls. 

As you run more services, monoliths, or third-party applications on the cloud platform, begin to take advantage of cloud services like integration, business process automation, and API management. These will take advantage of the opportunities afforded by the cloud platform.

After that, it’s a matter of continually learning and improving. As your team becomes ever-more familiar with the changes involved with going cloud native, you can adjust your processes to help improve the efficiency of your developers and software alike.

Advancing technologies can automate the process of harnessing increasingly bigger and richer data sets, to unearth actionable insights and underpin growth. The key to not only implementing meaningful service transformation – but accelerating it – is to future-proof business models by understanding the underlying data and applying analytics.

The shifting landscape – adapt or lose ground

European companies are ramping up efforts to adapt to the new digital age, or lose out to more agile players. The European Commission’s investment proposal, unveiled in 2018, featured €2.7 billion specifically to support a thriving business landscape by boosting the region’s supercomputing and data processing capabilities.

However, despite Europe accelerating at a faster pace than the US on service transformation, we are still lagging by over a year in terms of embracing technology-led transformation. AI and, more importantly, machine learning and deep learning techniques, are evolving into the mainstream. They are now becoming foundational to the process of mining data to set the framework for service transformation and deliver measurable outcomes.

There is encouraging momentum in the analytics space, with UK businesses projected to spend £24 billion on advanced analytics this year. That’s double the investment figure of just three years ago. However, to achieve transformational success, this investment must be accompanied by a more fundamental attitude shift and greater data literacy.

Transformation starts from within

Data analytics is a powerful intelligence tool that draws a holistic map of an organisation to uncover in-depth insights into the effectiveness of your current offering. Secondly, it allows you to become more targeted to your business objectives and the needs of your customer base, facilitating quicker, more accurate decision-making. Let’s explore further why data should inform each component of service transformation…

Driving operational efficiencies and agility by streamlining processes. Service transformation entails working simultaneously from two sides to shrink the core. The first part is moving away from legacy systems to cut down on inefficiencies and time wasted on manual effort. Through analytics you can also identify the applications that are best suited to automation and modernisation, taking a business priority based view. This sets the groundwork and generates the budget for the other half of the equation – introducing next-generation systems that are better architected and on the cloud.

Improving the bottom line. Data unlocks the knowledge required to pin-point exactly where to direct your attention to add value. This means you can act fast to seize opportunities and get the most bang for your buck with investments. Take the UK accountancy firm BDO, for instance, which is directing more investment into robotics and data analytics so that employees can provide an even higher level of specialised consultancy on complex queries. This is an effective way to differentiate yourself and maximise the bottom line. 

Elevating customer experience and service. Organisations cater to an increasingly disparate range of end users who want greater personalisation. Hence, an enhanced customer journey should be a top priority in service transformation. Consider the likes of Lemonade, the US player now breaking the mould in European insurtech by utilising machine learning to analyse behavioural and historical data to give customers a seamless, customised experience.

Improving security and risk management. Data analytics enables you to mitigate the risks that you can realistically manage, and foresee how macro developments could affect your business. Helped by advancing machine learning and deep learning techniques, companies can now generate data and use predictive analytics to identify risks and take timely preventative action.

Addressing the principal challenges

Most businesses already collect and store enormous amounts of data but the majority are still not unlocking its full benefits through strategic analysis. What’s stopping them?

Lack of understanding. Many don’t know where to start with service transformation or analytics, let alone both. The difficulty is not so much in adopting the latest tools but in deciding the questions that you want to answer, and the minimum viable data that you need to do so. Work backwards to define your commercial and operational performance goals – from there, the right questions and metrics become apparent.

Overcoming data silos. Data that is hard to access, fragmented or stored in formats that are incompatible with each other poses the chief hurdle to becoming data-driven. You must first engender collaboration and transparency across departments and stop duplicating efforts. Instead of restricting responsibility to the leadership team, encourage organisation-wide commitment and education. Without this, you compromise your ability to deliver a high-quality service to end users.

Legacy systems. Last year, it was reported that the Bank of England spends 33.6% more on IT than any other department in the public sector, largely because it has not updated its systems. Many businesses worry that replacing outmoded systems can be cumbersome, expensive or disruptive. Actually, it costs more to remain stationary than to embark on service transformation. Over time, legacy platforms will not be supported by newer systems, will pose a higher security breach risk, and will not offer the flexibility and scalability to accommodate expansion. Keep in mind that current analytics technology does not necessarily entail complicated or lengthy installation.

European data regulation. More stringent personal privacy laws are shining a spotlight on data management policies. Most notably, the EU’s GDPR and the UK’s Data Protection Act 2018, both of which carry substantial cost and reputational consequences for non-compliance. Of particular significance to your data strategy are the requirements for data minimisation and concerns around automated profiling. Rather than hindering service transformation, this offers a rare chance to review your data and how you process it. Use this to cultivate best practice and customer trust.

Cybersecurity. A recent survey reveals that UK chief executives see cybersecurity as one of their top concerns for the year ahead. With the issue of migrating existing data to the cloud, use of data from both internal and external sources, and the prevalence of IoT, the risk factor climbs and threatens your transformation progress. There’s much debate surrounding public vs private cloud (look at the Capital One breach, partly blamed on public cloud adoption). Hybrid-cloud can offer the best of both worlds, with the upside of cloud while storing your most sensitive data elsewhere. Outsmarting increasingly sophisticated hackers requires real time analytics; the self-learning and pattern recognition properties of AI can spot suspicious activity and minimise damage.

Looking ahead

There is excellent scope for service transformation to accelerate in the European landscape thanks to rapidly maturing technologies. However, this bright future relies on a greater understanding of how to make your data work for you. The opportunities and challenges outlined above are interconnected, emphasising that service transformation is not a linear programme and requires a multi-layered approach. They say “knowledge is power”. The knowledge that you need to build a strategy to triumph in a tough and crowded marketplace is already embedded in your data.

You can’t walk ten feet without bumping into something ‘AI’ today. Everything seems to be AI-powered in the technology world, but with the possible exception of Boston Dynamics’ robot dog, none of these developments (thankfully) look like the autonomous robots that Star Wars and The Terminator promised us.

This is largely because what we define as artificial intelligence today is actually still the evolution of what we called big data and analytics a few years ago. Machines are getting smarter, but they aren’t ‘intelligent’. They’re better at thinking in a programmed fashion, but they’re still unable to do it independently.

However, this doesn’t mean that today’s concept of AI is wrong – it’s simply a misnomer. It’s more accurate to talk about ‘augmented intelligence’ rather than ‘artificial intelligence’, and it’s human intelligence that’s being augmented. Sifting through reams and reams of numbers, figures and data generally isn’t our strong point – but computers can handle it easily.

According to Gartner, Augmented Intelligence is all about a machine’s ability to “enhance cognitive performance. This includes learning, decision making, and new experience”. It’s all about assisting people, not replacing them. It’s not about entirely simulating ‘independent intelligence’, but rather helping people to make decisions by providing better data analytics.  

One of the most interesting applications of this is in the sales industry.

Consider this: during the last two decades, the internet has been democratising information. For a buyer, this means that information needed for purchasing products and services can be found online. In fact, Forrester estimates that 61% of business customers prefer to do all of their product research online, and 67% prefer not to interact with a salesperson for information at all!

This is bad news for salespeople, with many customers not even giving them the chance to provide information. Similarly, when customers do get in touch, staff often find themselves facing well-informed customers who are already familiar with many of the facts and figures that salespeople have up their sleeves.

This is exactly where ‘augmented intelligence’ comes in. Rapid analytics and insight tools can examine the content that prospects are looking at and analyse patterns. Senior buyers will need a different ‘pattern’ of content to junior buyers. Similarly, buyers in the financial services industry may be more limited in their ability to flex, because of industry regulations such as Sarbanes Oxley, which means that sellers will have to provide greater content about their own organisation, in addition to compelling sales content. 

While an experienced salesperson will often have an unconscious understanding of these trends and needs, augmented intelligence systems can analyse them effortlessly and provide content in advance, based on reliable data. This can take a lot of the intensive brainwork away from the process, but can also help to bring new salespeople up to speed quicker.

Ultimately, this kind of AI support helps salespeople provide a better experience for buyers, allowing them to do what they do best – sell!

To thrive in today’s technology-driven business landscape, organisations must embrace digital transformation. However, these initiatives often lead to increased IT and infrastructure complexity. Combined with expectations for perfect performance and faster service delivery, IT teams find themselves under immense pressure. Fortunately, disruptive new technologies, such as AI, can lend a helping hand.

Microsoft research indicates that businesses who have deployed AI technology are outperforming other organisations by 5% on productivity, performance, and business outcomes. This increase in throughput not only has a positive impact on the bottom line, but it also improves morale as the workforce can participate in more rewarding and creative tasks that truly impact the business.

For organisations to effectively manage increasing infrastructure complexity amidst budget cuts and a growing skills gap, they must embrace technology to solve the IT conundrum and usher in the next phase of digital transformation for the IT department itself. Introducing AI to IT operations offers tremendous potential for under-resourced IT teams to regain control and truly do more with less, while improving across a wide variety of KPIs.

Artificial intelligence for IT operations (AIOps) applies machine learning (ML) and advanced analytics to gather and analyse immense volumes of data and quickly identify existing or potential network performance issues, spot anomalies, and pinpoint the root cause. Going one step further and combining AIOps with intelligent, cross-domain automation allows IT operations to automatically predict, prevent and fix issues autonomously, improving performance and service quality in addition to cost savings and greater efficiency.

Despite the clear benefits of AIOps to businesses, some people are fearful of its adoption. Employees may worry that AI will make their roles redundant, but this is far from the truth. With AIOps and automation taking care of many of the repetitive and menial operational tasks, IT employees have room to redevelop their roles into something more transformative and fulfilling, driving innovation and growth. For businesses to truly achieve digital transformation success, employees across the enterprise must embrace AI and its myriad applications and champion the benefits as the way forward.

The most successful companies of the last decade have all now leveraged AI in order to streamline business processes and operations, power their decision making, and deliver next generation insights about their employees and customers. However whilst there is some broad familiarity with what AI is, business are struggling to overcome some key barriers to entry.

One of the major reasons that companies are not comfortable initiating AI projects is the growing AI skills gap. Recent advancements have put a premium on talent skilled in data and the techniques and systems used to acquire, integrate, prepare, manage, and interpret data -- a skill set with more demand than current supply. In order to address the AI skills gap, businesses must first learn to cultivate collaborative relationships with the academic community as opposed to treating it like a transactional, one-stop-shop for talented new hires. The data-capable talent of the future can benefit from this symbiotic business-academic partnership – aspiring data scientists can deploy their learned knowledge in a real world setting, gaining experience that they otherwise wouldn’t get in an academic setting and, in turn, businesses gain access to the top tier of technological talent.

In addition to closer academic collaboration, companies must cultivate a data culture, with employee upskilling and retraining offered where needed, where every employee within the organisation understands the company’s data strategy, the value of data to their organisation’s success, and their role in using data to achieve their day-to-day work goals. Until businesses address the data and AI skills gap, they will struggle to truly harness the power of this next generation technology.

Another key barrier to AI adoption for businesses is data availability, which often severely delays and even halts enterprise AI projects. Siloed or disconnected data can mean that machine learning engines do not have the much needed raw source information they need to thrive. To avoid this, businesses must ensure they equip themselves with the tools to properly manage, move, and integrate data. Similarly, data that is of poor or inconsistent quality can also be damaging to a business as recommendations may be made based on faulty evidence. Lastly, technology aside, individual departments are sometimes averse to sharing data with other teams throughout the organisation -- this reluctance must be overcome if companies are to leverage all of their data and finally achieve a single source of the truth.

Due to these significant hurdles to AI adoption, many businesses have turned to third-party SaaS software that has machine learning at its core. This helps to align process and operational optimisation with companies who have the expertise to weave AI technology into the services that they offer. Today, companies can partner with a range of SaaS providers to marry AI with a variety of business processes, from HR to finance to customer experience. This is likely to be the way the majority of enterprises fast-track their engagement with AI and use it to further their businesses objectives.

We depend more and more on data, computing power and connectivity. Data centers are at the heart of our connected world and it has never been more important that they are built and run reliably and efficiently to maximize value for customers and data users.

By 2022, the colocation market is expected to exceed USD 60 billion, with demand for cloud capacity and repositioning of IT expenditure being the key drivers of growth in the market. (1)

North America, Europe and Asia continue to be the strongest players in the retail and wholesale colocation segment. But emerging markets such as India are now offering viable and alternative locations for providers as space, rising costs and reliable power are at a premium in developed markets.

And the pace of change is not going to slow down – in fact, it is going to accelerate as more and more enterprises chose retail and wholesale colocation providers to reduce the overall cost of IT and move from on premise data centers to rent space, racks and cages, or entire rooms and facilities for their IT equipment.

With such rising demand for colocation data centers, builders and system integrators need to enable simpler and faster deployments and expansions.

Quicker and leaner lead times from enterprise contracts mean that even if a colocation data center wanted to expand its current operations by approximately 30MW load capacity, it would need to guarantee that the facility will be ready on time and within budget.

So, how do colocation data centers guarantee timeliness and budget control without compromising on quality standards and security protocols? To stay ahead of the curve, operators need to review alternative options in the design, build and management of colocation projects.

The Need for Speed

Data center owners are now looking to achieve build cycles of between six to nine months for new builds, compared to traditional cycles of 12 to 18 months. As such, modular and scalable builds help owners to achieve aggressive time to market goals more efficiently.

With complicated data center projects that have short delivery timeframes, a pre-engineered product package or prefabricated skid solution, built in an offsite facility can present a viable alternative to traditional brick and mortar construction.

Modular system solutions which feature prefabricated eHouses and skid-mounted power substations including switchgear, transformers, ancillaries and other electrification components in one, offer flexibility, a higher level of safety and integration of intelligent technology, and importantly greater power reliability.

Modular data centers offer a more compact, timely, scalable and convenient method of deploying data capacity and a standardized power infrastructure to where it is needed.

Testing, Testing

Data center modularization also offers significant benefits in terms of build and testing as much of the build and pre-commissioning work is taken away from a construction site. Costly delays are prevented through parallel stage builds instead of sequential build procedures seen on construction sites.

Projects can be fast tracked, and risks are further mitigated with offsite testing being undertaken in a controlled environment thereby protecting the system integrity and providing peace of mind for operators. This type of approach offers the same level of rigor, consistency and quality as achieved in a traditional data center build, with program timelines being unaffected by onsite testing and engineering works.

Building Blocks to Success

Colocation companies looking to generate return on investment quickly, should also consider pre-engineered building block solution architecture.

These building block systems leverage the benefits of modular solutions together with proven pre-engineered designs to reduce project cycle times, meeting the need for speed and reliability of today’s colocation market.

This is a shift from a traditional “system plus system” approach to an optimized design which provides better economic value through higher utilization of assets while maintaining the required system reliability. The designs have been proven and developed from complex custom solution architectures previously delivered and deployed.

The proven design reduces engineering time by up to 80 percent, and standard designs deliver manufacturing efficiencies which reduce testing and manufacturing time as well as late delivery risk.

GIGA Data Centers

ABB recently deployed a block system approach to a data center in Mooresville, NC for GIGA Data Centers, to develop the critical power distribution design and help GIGA reduce total cost of ownership and speed to market.

Based in the US, GIGA Data Centers aims to use modular data center technology to bring hyperscale efficiency to the colocation market. They believe companies seeking data center colocation should have access to the same high levels of efficiency and flexibility at a competitive cost.

ABB and GIGA jointly developed the system design specifications as well as the sequence of operations for input and output switchboards. The solution comprised of low-voltage switchboards, dry type transformers and Uninterrupted Power Supplies (UPS) to support the IT server and network equipment infrastructure. Such an approach created a more compact and efficient data center that met all GIGA’s requirements and delivered outstanding power distribution and protection performance.

Shaping the future

There is no doubt that the need for speed will continue to shape colocation data center development as demand for enterprise data and services grows. Emerging technologies like autonomous vehicles, “massive” 5G and smart cities will drive the need for colocation at the “edge” and for near real time performance and further efficiency. 

Partnering with experienced contractors is important to evaluate current and future business needs and specify the high-quality procedure with the same high level of build skills across the data and smart power architecture. This together with incorporating digitalization can deliver safe, robust and modular data centers which flex and grow with demand, while also delivering faster return on investment for colocation data centers.

To read the whitepaper in full visit: https://campaign.abb.com/l/501021/2020-01-22/qsmskw

1)Data Center Construction Market Due to Grow with a CAGR of Almost 9% During the Forecast Period, 2019–2023 – ResearchAndMarkets.com.” Business Wire, Research and Markets, 10 Oct. 2019, Stack, Tim, and Ian Griffin. https://www.businesswire.com/news/home/20191010005553/en/Data-Center-Construction-Market-Due-Grow-CAGR

With its ability to foster near real-time speeds and capability of having the data and processing close to the server, Edge will further enhance the applications used in industries like healthcare and manufacturing, improving the way they operate and function.  

Edge has the unique ability to push applications, data and services away from centralised nodes to the logical extremes of a network. While some organisations are struggling even to understand the concept and its applications, others are steaming ahead in terms of implementing the newest technology by transforming business processes and decisions. By 2022, it is thought that the Edge market will grow to $6.73 Billion.

Many drivers are contributing to the rise of the Edge, from a growing load on cloud infrastructure to an increase in the number of different applications. The growth of the Edge market is encouraged by the dawn of ubiquitous connectivity delivered by emerging standards, such as Wi-Fi 6, 5G or long-range/low energy networks and the availability of a wide range of applications. It’s clear that this technology is here to stay and is set to have a dramatic impact on all industries.

So, with all of this technology available and the rise of 5G connectivity, what are the future uses of Edge Computing and what will it mean for the workplace?

Autonomous vehicles 

Perhaps the most obvious application of Edge Computing is autonomous vehicles. Generating a huge quantity of data, connected autonomous vehicles (CAV) are a prime example of how applications may benefit from the Edge data being processed closer to where it’s created, such as the motor, generator, pump and sensors. The need for constant connection and reduced latency is apparent, imagine what would happen if a CAV lost connection whilst on the highway. According to Gartner, by 2020 50% of motor vehicle manufacturers will apply advanced analytics to CAV data to identify and correct defects.

Manufacturing 
Edge Computing can improve existing manufacturing processes by making them more intelligent and autonomous while providing responsiveness and agility. It can increase reliability and provide real-time insights while minimising failure and costs for storage and bandwidth, Edge can improve the efficiency and use of IoT in manufacturing.

Healthcare 
From gathering and analysing local patients data, to patients wearing devices that can diagnose certain conditions, healthcare is embracing technology enabled by Edge Computing. As resources become more stretched providers are looking to technology to deliver services to patients. For example, wearable devices that diagnose or monitor conditions can reduce the number of GP appointments required and given that appointments are often in short supply, this is invaluable.

Printing
By using Edge Computing for print jobs, organisations can securely solve issues of lag and bandwidth cost while ensuring continuous business-critical operations. Latency is eliminated as printing is processed on-site, with total bandwidth reduced. Print jobs are processed locally with all data remaining on the company’s network so only metadata is sent over the network. With continuous connectivity, there is no risk of down time, data loss or printing errors.

These are just a few examples of the applications of Edge Computing and as the technology continues to rise in the coming years, we will see more inventive applications of the technology and see many processes improving and speeding up as a result.  

The future of the Edge will come with many apps, but with printing used across industries such as healthcare, manufacturing and education, the impact of this change will indeed be killer. Are you ready to take your print management to the Edge?

Organisations are increasingly aware of the importance of AI, however there is still a universal lack of understanding of how exactly AI deployment can benefit a company’s operations and business processes. Just 23% of businesses have incorporated AI into processes and product offerings with the majority failing to capitalise on the opportunities afforded to them by AI and machine learning technology.

Despite this lack of deployment, AI is not something organisations should neglect. AI exists and flourishes due to its symbiotic relationship with big data, whereby the former can require large amounts of data to build its intelligence. But the opposite is also true. With such vast volumes of data being created each minute, AI is a great enabler to sift through data and extract the most compelling insights to maximise business value.

A big sticking point in the wider rollout of AI initiatives within business is often due to a lack of strategic direction in terms of its deployment. The first step is to identify what problems within the company AI could solve or support. Once this has been established then strategic leads can formulate a set of metrics to determine what needs to be achieved.

Furthermore, companies must gather and utilise high-quality, accurate data to enjoy the full benefit of AI. Without sufficient volumes of quality data as input, AI algorithms will inevitably be unable to learn and make predictions. The more precise the data, the more accurate the predictions. Data Scientists typically attribute 80% of their effort in sourcing and deriving the input data for AI projects. Gathering, cleaning and maintaining data centrally for AI initiatives can significantly increase the productivity of AI initiatives, by providing a quality and catalogued source of approved data for use in AI models.  

Adopting the cloud is also another major factor towards successfully utilising AI and machine learning. Cloud technology is fundamental to the advancement of AI because it enables businesses to ingest the vast amounts of raw data being produced, and which will only increase as more companies begin capitalising on real-time data. Building AI models can require huge computational processing for both data preparation and training, for which the Clouds elastic and pay-per-use economic model is ideally suited.  By accessing this compute and data effectively, AI is able to thrive and deliver on its true cognitive promise. It’s no surprise that 49% of companies that have deployed AI today are using a cloud-based service.

Despite the fact many companies are not properly deploying AI technology, success stories within the business world are tangible. Monzo is using AI to deliver on its customer-led approach by allowing customers to discover places in their location based on financial transactions. AI software also streamlines its support service network by developing a system that recommends to its customer support agents how best to respond to certain customer queries.

The retail sector has been a major beneficiary of AI as it has used the technology to completely reimagine the business-to-consumer relationship. Major UK retailers, such as River Island, are now implementing AI at the heart of their operations. The fashion retailer is successfully using AI software to improve its stock management and ability to forecast sales figures in order to anticipate and fulfil changes in demand. By using AI, retailers can personalise the shopping experience for their consumers by better understanding consumer trends, either when shopping online or in-store, providing an optimal multi-channel experience.  

Just like the retail industry has achieved, organisations across all sectors will find true value when harnessing the power of AI in order to unlock unrivalled experiences and knowledge about its customers. Data is now plentiful and without the right skills and AI tools in place, organisations are going to be left with vast volumes of untapped data, which ultimately hold a treasure trove of new insights.

Hackers are gaining ground, according to many executives responsible for cyber security, and organisations are leaving themselves open to attack. As businesses increasingly adopt and expand their digital transformation strategies, this is resulting in them sharing huge quantities of data with external organisations and integrating new technologies and Internet of Things (IoT) devices into their core operations. As a result, this has increased the target surface area for hackers to aim for as they attempt to break in and steal that precious data.

Constantly under threat, security professionals are increasingly bogged down in detection and response processes that are too slow and not fit for purpose, putting a strain on human resources. In this constant game of cat-and-mouse, defenders are looking to Artificial Intelligence (AI) and Machine Learning (ML) algorithms to help them get ahead of attackers. Whilst the number of cyber-criminals continues to rapidly escalate, AI augments the role of security professionals by identifying malicious activities within massive data sets and enables businesses to scale their teams more effectively.

As a second line of defence, ML can be used to combat hackers in two ways. Firstly, unsupervised ML algorithms are able to find anomalies within more generic data sets, helping security experts to identify new threats, while supervised algorithms are fed ‘trainer’ data, which enables the AI to become more efficient at detecting specific cyber-attacks over time. However, it’s clear that AI and ML are not going to solve the problem by themselves, and businesses shouldn’t see these as the ‘silver bullet’ to be left to their own devices. Automation should be used to aid, rather than replace human intelligence. Humans ultimately have the expertise to add context to the data that AI and ML process, helping us decipher what is and isn’t malicious. Sometimes anomalies may be just that – anomalous.

AI won’t provide an immediate and significant winning advantage for defenders but refining ML algorithms to react with automatic or semi-automatic responses to specific threats will bolster human teams facing a scarcity of resources and expertise. We’re currently experiencing a massive skills shortage within the technology and cyber security industries – a situation that’s only going to get worse as more digital processes demand constant expert attention. So, any chance to automate must be seized by businesses to help scale teams efficiently while providing further layers of security and protection.

However, the road to a digital-first workforce isn’t an easy one and although digital transformation efforts are underway in most organisations, many common challenges remain. The top barriers range from insufficient budgets and cybersecurity concerns to legacy systems and the adoption of new technologies. Mobility and the rise of mobile working, which go hand in hand with digital transformation, also pose a challenge. 

Despite these potential challenges, for many businesses – whether they operate in retail, finance or manufacturing industries – it’s a matter of transform or be left behind. Tackling the hurdles to digital transformation and embracing digital change has never been more important. So, how can CTOs and IT departments overcome these common challenges and successfully transform their business into a smarter organisation?

Overcoming a lack of budget

Digital transformation obviously doesn’t come without extra investment. It’s therefore unsurprising that a common roadblock on a business’ digital transformation journey is often a lack of funds. For businesses who are lucky enough to receive greater IT budgets, much of the attention is placed on emerging technologies like AI and IoT. However, digital transformation isn’t just about shiny new technologies that transform business and operational models, it’s about change taking place at all levels – right down to the technologies that employees are using on a day-to-day basis.

Hardware remains an integral part of any IT strategy and therefore should be part of any organisation’s IT modernisation efforts. Completely overhauling an employee device strategy with new devices is an expensive and time-intensive process. Not to mention managing the day-to-day device lifecycle management which adds significant complexity.

To overcome this many businesses are considering other purchasing options, such as PC-as-a-Service (PCaaS), as part of their digital transformation strategy. PCaaS, which can encompass everything from mobile devices to desktop PCs, is an Opex (operational expenditure) subscription-based model which often includes services such as purchasing, configurating, managing, refreshing and retiring devices. This means an organisation pays a monthly rate for the use of a vendor’s devices and additional services, rather than buying hardware outright (referred to as Capex). Businesses can benefit from updated technology, whilst being able to amortise device costs over time, as well as scaling up and down depending on the need.

Surmounting security concerns

The PCaaS model also provides a potential solution to another common barrier to digital transformation – security. PCaaS can encompass data backup, recovery and remote wiping, giving a business the peace of mind that if the device is stolen, damaged or suffers a cyber-attack that valuable corporate data can be both wiped or recovered.

As more companies embrace digital transformation and with it mobile working strategies, this raises concerns over keeping sensitive data safe. In fact, cybersecurity concerns are a primary worry for business leaders, hindering potential organisational transformation. In a recent survey, 60% of respondents said that exposure of customer data was the largest security concern, closely followed by cybercriminal sophistication (56%) and increased threat surface (53%).

Overcoming the growing sophistication of cybercriminals is no easy task. However, as with any IT project, it’s fundamental that businesses consider security from day zero and equip themselves with the right protective technologies to ease concerns. Devices which include advanced biometric features and hardware-based credential storage capabilities provide a strong first line of defence.

Other security measures such as zero client solutions go even further and help nullify data-related threats by withdrawing sensitive data from the device itself. With information stored away on a central, cloud-based system, these solutions protect against unsolicited access to information if a device is lost or stolen. This is especially useful for mobile workers looking to access data outside of the office or on the move.

Leaving legacy systems behind

A new wave of technologies such as AR, 5G and IoT have been the catalyst for business-wide digital transformation efforts. However, reliance on legacy systems is posing a major bottleneck for many organisations looking to build a digital-first business. The problem is that whilst these new technologies are new and exciting, they create vast amounts of data and many organisations are struggling to efficiently scale their IT capabilities to cope with this increase in demand.

So, what’s the answer for organisations who don’t have the resources to simply ‘rip and replace’ infrastructure? Edge computing solutions enable organisations to resolve this challenge, while at the same time creating new methods of gathering, analysing and redistributing data and derived intelligence. Processing data at the edge reduces strain on the cloud so users can be more selective of the data they send to the network core.

Digital transformation will undoubtedly remain a major part of business conversations for many years to come. Whilst it seems we’re on the way to a digital-first future, not all organisations have arrived in the digital age and there are still several challenges to overcome. With digital-first companies expected to be 64% more likely to meet their business goals, the benefits for those embracing digital transformation are clearer than ever – they’ll just need to overcome a few stumbling blocks along the way.

Retailers need to adapt to rapid changes in consumer attitudes and the marketplace. Every aspect of retail operations has the potential to make or break the customer experience. AI technologies are breaking down barriers and making it imperative for retailers to adopt a globally competitive retail business model. The power of machine learning has made it possible for businesses to continuously scrutinise customer behaviour data and generate alerts when the time is right for the next best action.

FMCG (Fast Moving Consumer Goods) companies are already rich with data. Successful transitioning to AI can be achieved through a gradual approach, the right combination of people, processes and tools. By upscaling AI efforts, companies can glean valuable and actionable business insights. With data-driven intelligence at their disposal, organisations can strengthen four key pillars crucial to delivering a next-level retail experience— the ability to understand their customers, smart merchandising, hyper-relevant marketing and optimised operations- that centres around the products and services their customers crave.

1.  Customer lifetime management

Derive factors that influence purchase behaviour

Purchase decisions are now influenced by a variety of factors that were not envisaged. Customer segmentation based on behaviour now takes in account not just frequency-recency-monetary but also diversity of purchases, potential to experiment with a new product, seriousness, profitability (amount bought/amount returned) and other such variables. These aspects map differently to different product categories.

AI can help in bringing clarity in what really drives experience. It can also help determine the production quantity and potential consumers.

Track and prevent customer attrition

Customer behaviour can be tracked to glean insights into their loyalty. Creating customer analytical records and detailed journey maps help in identifying which customers have deviated from their usual behaviour and what should be the next best action.

Customer look-alike mapping aids in quickly gauging the time to action and in identifying the action that is most likely to achieve the objective.

Drive higher revenues per customer by running targeted cross-sell and up-sell

Customers are willing to share their data if they enjoy a higher quality shopping experience. By offering differentiated shopping journey, retailers and brands can demonstrate that their relationship is authentic.

Past purchase behaviour of similar customers can be combined with product features and pricing to derive a propensity model for future needs.

2. Merchandising

Pricing plans and promotions to optimise certain KPI

To remain profitable, optimal pricing is everything. There are inherent trade-offs between balancing growth in volume vs driving return on investment. A combination of metrics has to be modelled so that it can be achieved in unison.

Being able to simulate scenarios and measure probable impact of business decisions can be achieved through detailed modelling of available data and algorithms that learn from that data.

Creating customer-centric assortments

Good assortments which give ‘instant gratification’ to customers are at the heart of retail. They yield better sell-through, lower chances of mark downs and help optimise the inventory store by store. Understanding customer demographics, behaviours, tastes and millions of transactions requires a mix of data analytics and machine learning.

Edge sensors can be deployed to gather in-store data points such as duration for which a customer looked at a certain item, and if the item was picked up from the shelf. Scalable AI solutions can anticipate customer behaviour and replicate market conditions to run complex analysis on large datasets to help decision-making.

AI can help retailers deliver outstanding customer experience and meet their financial goals.

Location Scoring

Retailers need to see, at an aggregate level, who is moving around in cities and where are they going, so that they can market products and services more effectively to consumers, and also identify the prime locations for new stores.  Retailers are also tapping into location data to find out more about the potential customers that are walking by their storefronts every day.

AI leverages open data sources to analyse the interplay between demographic factors of a certain location and the success of their operations in that area.

3. Marketing

Market Mix Modelling (MMM)

An effective MMM takes in account the marketing spending across product base and their impact on the revenue. With a sharp increase in customer touch points, there is a corresponding rise in the data that needs to be analysed. Without ironing out the collinearities and correlations in the data, such analysis can become inadequate.

Powerful AI algorithms can aid ‘what-if’ analysis and highlight correlations in the data.

Personalised Assistance in Real-time

According to a Salesforce report, 70% of consumers say a company’s understanding of their individual needs influences their loyalty. Retailers are now equipped with the next level of personalisation where they are able to provide just-in-time assistance and proactive care.

AI can identify potential customers based on purchase history to promote and share focussed offerings in a brand. When such customers come in vicinity of the store, a message with an offer can be sent on a mobile phone.

Improving Adoption of Loyalty Programs

Loyalty programs seek to go beyond registration and occasional cashing in offers. Inactive memberships can result in relationship breach. Demographic look-alike mapping, targeted efforts can be made to convert this into an active membership by sending right offers at the right time.

4. Operations

Supply Chain

Retailers can now obtain a holistic view of their supply chain covering raw material acquisition, production and last-mile delivery by adopting intelligent, data-driven processes. This perspective is essential not only for stock forecasting and managing customer expectations but also for a shared common understanding among all the members of the chain.

Advanced analytics can predict demand spikes, identify bottlenecks and reduce supply shortages for dozens of products.

Invoice Processing

Digital processing of invoices requires extracting textual information from images of invoices. Formats of invoices may vary depending on issuer of the invoice.

Cloud-based, OCR enabled service can help in expediting information extraction and improve SLA compliance significantly.

Source: https://www.nature.com/

A Three-pillar Strategy

Economy, Environment and Social are the three pillars around which the (r)evolution of the IT sector will be built.

Too often, TCO and ROI are the primary elements for Data Centre managers to determine their success. It’d be advisable to start considering Data Centres as a sort of orchestra, where all the elements dialogue with each other contributing to the execution. In this way, adopting energy efficiency solutions (software and hardware design, liquid cooling solutions, smart buildings, circular economy, etc.) would lead to substantial savings on the electric and water consumption. These savings could then be used to invest on more IT equipment, that would translate into more IT Hardware density and, consequently, improved performance.

Last but not least, diminishing the electric and water consumption, and implementing circular economy best practices^[1], etc. would minimise the impact on the environment with a clear positive image return for any company.

A Green Immersion Cooling Data Centre

Submer is proud to announce the installation - at BitNAP Data Centre - of 10 SmartPod units delivering up to 500kW of heat dissipation. This is the first sustainable Liquid Immersion Cooling cluster in Spain to rely on renewable energies as a secondary cooling system, being BitNAP part of the district heating and cooling (DHC) system operated by Ecoenergies. The smart thermal energy network designed by Ecoenergies produces - among other things - cold water used by BitNAP as a secondary cooling loop. This smart grid - powered by a biomass plant that uses residues from Barcelona’s parks and gardens, gas and electricity - reduces the fossil fuel energy consumption by 67,000 MWh and the CO2 emissions by 13,412 T per year. The installation at BitNAP is the perfect example of the three-pillar strategy applied to the Data Centre industry. This is the first of many examples to come of real symbiosis between Data Centres, renewable energies, smart technology and a truly viable industrial application.

Paving the Way Towards Next Generation Data Centres

Submer Technologies was founded in 2015 by Daniel Pope and Pol Valls to answer a precise need and intuition: the necessity to tackle the Data Centre business from a new angle, creating highly efficient solutions to pave the way towards next generation Data Centres.

In the last 10 years, we have witnessed a major evolution in the Data Centre industry, from ICT equipment rooms to cloud Data Centres. In the next 10 years, we believe we will be witness to a revolution.

Submer is changing how Data Centres and supercomputers are being built from the ground up, to be as efficient as possible and to have little or positive impact on the environment around them (reducing their footprint and their consumption of precious resources such as water).

We believe that “innovation” doesn’t have to contradict “sustainability”. We believe there is a better way, a smarter way to stay cool. And we also believe that DCA represents the perfect “agora” where to share our ideas, our concerns, our hopes about next generation Data Centres and a privileged spot for keeping media, governments and public informed of the vital role of Data Centres in the society of the future.