Jeevithan Muttu, GM & SVP – Device Management at Motive, on why the operators that combine network intelligence, device awareness and entitlement orchestration into a unified, real-time control framework will be positioned to capture the opportunities these network capabilities create
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Networks today generate volumes of data never seen before. From broadband home Wi-Fi performance to IoT deployments and smart meters, battery health and application usage patterns, communications service providers (CSPs) now have unprecedented visibility into how their networks and connected devices are being used.
For many operators, valuable intelligence remains confined to dashboards and analytics platforms rather than shaping live service decisions. Turning network data into sustainable revenue calls for more than insight; it requires the ability to act instantly at the device and entitlement layer.
What’s clear is that access to information is no longer the constraint. The real constraint is execution, and the shift from understanding to action will define the next phase of telecoms monetisation.
From network data to commercial action
Over the past decade, CSPs have invested heavily in 5G, edge computing and advanced analytics. These investments have significantly increased network capability, yet monetisation has not progressed at the same pace.
The reason is structural. Networks can detect patterns, predict demand and analyse performance. However, unless that intelligence can be translated into immediate policy enforcement, defining who receives which service, under what conditions, and on which device, revenue opportunities remain theoretical.
Real-time device intelligence bridges this gap.
When operators can evaluate device capabilities, subscriber entitlements, and current network conditions simultaneously, they can execute services dynamically rather than statically.
Instead of analysing congestion after it affects customers, they can prioritise traffic in the moment. Instead of offering rigid plans, they can introduce usage-aware tiers, temporary performance boosts or device-specific service enhancements.
This is not about generating more telemetry. It is about operationalising the intelligence already available. Device intelligence is also used for effective root cause analysis in near real-time.
Turning network capabilities into revenue
Many of the capabilities required to unlock new revenue streams already exist within today’s networks. However, the ability to monetise them depends on translating network intelligence into real-time delivery.
5G network slicing enables guaranteed performance for enterprise workloads or premium consumer tiers. Edge analytics can identify latency-sensitive applications or location-specific congestion patterns. Open Gateway APIs, standardised by CAMARA, allow operators to expose network capabilities, such as quality-on-demand, device verification, and secure authentication, to third-party ecosystems.
Yet, none of these capabilities generates revenue without real-time entitlement validation and enforcement. Whether delivering a performance guaranteed SLA or temporary service upgrades, operators must be able to provision and enforce those entitlements instantly across the network, device and billing environments.
Real-time device intelligence enables:
On-demand service upgrades
Premium quality-of-service tiers
Dynamic network slicing monetisation
Usage-based or event-based pricing models
Device lifecycle management as a managed service
Secure and scalable exposure of network APIs
In each case, the differentiator is not simply the network technology. It is the orchestration layer that determines whether services can be delivered securely, consistently and at scale.
Improving service quality and reducing churn
Monetisation and customer experience are increasingly inseparable.
Subscribers expect activation, onboarding and service changes to work immediately. They expect transparency into what they are entitled to access. Even minor friction, such as failed provisioning, inconsistent access to features, or delayed upgrades, can undermine trust and increase churn.
By aligning device intelligence with real-time entitlement control, CSPs can:
Reduce activation and provisioning failures
Ensure consistent feature enablement across multiple devices
Automatically adapt services based on network conditions
Proactively manage performance before issues escalate
Minimise support calls and operational costs
For example, when an enterprise application requests a temporary upgrade via Quality-on-Demand APIs, entitlement systems can dynamically apply a gold-tiered service profile to the subscriber. Similarly, when edge analytics detect sustained congestion in a particular cell, automated policy adjustments can prioritise premium traffic to preserve the quality of experience for enterprise customers
This capability shifts operators from reactive remediation to proactive assurance, directly improving both service quality and commercial resilience.
Enabling the API economy securely
As operators participate in initiatives such as the GSMA Open Gateway APIs, exposing standardized network APIs to developers and enterprise partners, control becomes even more critical.
APIs unlock monetisation potential. Entitlement ensures that potential is realised securely.
SIM-based silent authentication, quality-on-demand and device-status APIs all rely on real-time validation. Without a robust entitlement and device management framework, operators risk inconsistent execution, revenue leakage or increased exposure to fraud.
Edge analytics may surface opportunities for contextual offers or performance guarantees. However, unless analytics outputs are directly integrated with policy enforcement mechanisms, they remain advisory rather than actionable.
Connecting data insight to automated control is what transforms network intelligence into billable services.
Unlocking innovation on top of legacy systems
There is a common perception that enabling these new revenue models requires wholesale replacement of legacy OSS/BSS systems. In reality, operators can achieve this by modernising the control layer while integrating with existing infrastructure.
A real-time entitlement and device management platform can sit between network functions, OSS/BSS systems and device ecosystems, acting as the policy decision and enforcement point.
By taking this approach, CSPs gain the ability to launch new commercial models without extensive rearchitecture, stay aligned with evolving GSMA standards and OEM requirements, maintain compliance across multiple markets, reduce engineering overhead and accelerate time to market.
Turning intelligence into action
The telecoms industry has already made substantial infrastructure investments. 5G is deployed. Edge computing is expanding. Device ecosystems are evolving rapidly. Data volumes continue to grow.
Digital-native connectivity providers can introduce new offers in a matter of days. Enterprise customers increasingly expect on-demand, configurable services. Developers engaging with network APIs demand predictable, automated access to capabilities.
However, only the operators that combine network intelligence, device awareness and entitlement orchestration into a unified, real-time control framework will be positioned to capture the opportunities these network capabilities create. Those that continue to treat entitlement and device management as background utilities risk remaining infrastructure providers in a market increasingly defined by service agility. The opportunity to unlock new revenue models is already embedded within today’s networks. The differentiator will not be who collects the most data, but who can convert intelligence into action first
Now the hype is settling, 2026 is the year organisations must prove AI will deliver ROI. Matt Fuller, Co-founder and Vice President of AI/ML Products at Starburst, explains why measurement is only meaningful if organisations embed AI into core business processes and workflows.
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Over the past 18 months, many organisations have experimented with AI through pilots and isolated initiatives. While those experiments have generated excitement and much speculation, they have also created a measurement challenge. The question is no longer whether AI works, but whether it will deliver the measurable return on the significant investments companies have made in data infrastructure, skills and systems.
Today, AI’s impact is often assessed through activity metrics – usage rates, prompts generated, or time saved – rather than whether it improves business performance. Yet, activity does not equal impact. If organisations want to measure AI’s real contribution, they must anchor it to business outcomes that define competitive position: win rate, customer retention, time-to-market, risk exposure, cost per case, or revenue growth. Therefore, measuring AI meaningfully requires a shift in thinking – from treating AI as a standalone tool to embedding it directly into how the business operates.
Redesigning workflows for the AI era
The main reason AI initiatives fail to scale and, subsequently, prove business value is that they are introduced into existing processes. AI tends to be added as a “bolt-on” step in a legacy workflow rather than being part of the redesigned process.
To unlock value, organisations must therefore rethink and redesign end-to-end workflows around AI rather than inserting it after the fact. I believe that the real opportunity lies in improving decision-making across the whole process – from data architecture to insight generation to operation execution.
When AI is embedded in business operations from the start, its impact becomes measurable through outcomes such as faster product launches, improved forecasting accuracy, reduced operational losses, or stronger customer retention. However, redesigning workflows in this way quickly exposes another challenge: ensuring reliable access to trusted enterprise data.
Building the data foundation for scalable AI
As I’ve outlined, AI pilots often rely on fragmented, project-based datasets, where the data often sit outside of the organisation’s data architecture. While those datasets may be useful for experimentation, they rarely provide the reliability or governance required for enterprise-wide deployment, compromising scalability.
To embed AI into core business processes, organisations must therefore adopt a ‘data product’ mindset. This means creating curated, domain-owned datasets with clear ownership, quality standards and built-in governance. The data products then become reusable assets capable of supporting analytics, operational systems and AI models across the organisation.
It is only when AI operates on trusted data products, instead of one-off extracts, that it can be reliably integrated into operational workflows, scaled across the enterprise and measured meaningfully. At that point, AI stops being an experimental capability and becomes a business asset.
But getting to that point requires another shift to happen. Data must be treated as a governed, enterprise-wide strategic asset rather than a by-product of IT systems or a collection of disconnected silos. The shift here isn’t about better AI models; it’s about building a unified data foundation that enables AI to drive durable competitive advantage.
Scaling up security in the AI era
As organisations start embedding AI into decision-making processes, data governance becomes critical. Scaling AI without robust upstream governance structures introduces significant legal, operational and reputational risks. Before AI influences business-critical decisions, it is imperative that organisations can ensure clear data ownership, enforceable access controls, lineage visibility, and compliance with regional and regulatory requirements.
Importantly, governance cannot simply exist as policy documentation. It must be technically enforced across the entire data estate so that organisations can confidently scale AI while maintaining control over how and what data is accessed and used. Without that level of governance, AI risks amplifying existing data fragmentation and compliance challenges rather than delivering enterprise value.
People first, every time
AI adoption also raises the important question of the relationship between human expertise and machine intelligence. Much of an organisation’s competitive advantage exists not in data, but in tribal knowledge held by humans – judgment, context and business understanding built over years of activity.
AI systems are only as strong as the data and the context they are given. While organisations should progressively codify that institutional knowledge into governed data products, metadata and documented business rules, we are still far from a point where AI can fully replicate that depth of human expertise.
Today, AI is enhancing human capabilities rather than replacing them – surfacing insights, accelerating analysis and providing decision support – while experts remain in the loop to validate, refine, apply judgment and ensure decisions reflect the broader business context.
As institutional knowledge becomes more structured and accessible, AI’s role will naturally expand. But in the near term, as I’ve outlined, the most successful organisations will be those that use AI to augment their workforce rather than attempt to automate expertise prematurely.
From experimentation to enterprise value
Organisations are now moving beyond AI experimentation, shifting the focus from novelty to measurable business impact. Achieving true AI business value requires embedding AI into how the business actually operates – supported by trusted data foundations, strong governance and workflows redesigned around better decision-making. It is my view, then, that AI should ultimately be judged not by how often it’s used, but by whether it delivers meaningful business results.
ByMatt Fuller, Co-founder and Vice President of AI/ML Products at Starburst.
Cirata CEO Stephen Kelly tells us why having fully centralised data is the key to creating seamless AI access for companies.
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MIT reported that up to 95% of enterprise AI projects fail. Businesses are racing to adopt the technology and rushing execution in the process, significantly eroding ROI and potentially leaving thousands of businesses at risk of regulatory exposure.
Despite the urgency, there lies an invisible burden which is undermining progress. It is called AI debt and it may be the single greatest obstacle standing between AI ambitions and commercial reality.
AI debt is the accumulated result of incomplete digital transformation. It is every decision which meant legacy infrastructure was never fully retired, or the fragmented data siloes that were never unified. Any new platforms layered on top of such large and complex data sets that haven’t been organised properly, are creating complexity rather than clarity.
On their own, these shortcuts may have seemed pragmatic but collectively, they are now stalling innovation.
The risk of unfinished business
Recent analysis from McKinsey highlights the scale of the missed opportunity. Despite AI tools becoming commonplace today, 63% of organisations reported that they are still experimenting or piloting early-stage AI projects. This shows that most organisations are yet to embed AI deeply enough into their workflows and processes to capture its full value, estimated globally at between $2.6 trillion and $4.4 trillion.
The number one reason for this is years of bolt-on systems that have created tangled IT estates that slow decision-making and make rapid innovation nearly impossible. Running legacy and modern environments side by side inflates maintenance costs and introduces operational confusion which can result in failed implementations. These poorly executed migrations waste capital and introduce security and compliance risk, particularly under regulations such as General Data Protection Regulation (GDPR) and Digital Operational Resilience Act (DORA).
These consequences are measurable. Projects are delayed by months, which has a knock-on effect for budgets. Overall, the whole process inevitably grinds to a standstill because the data required to fuel AI models remains locked away in silos. Estimates suggest that between 50 and 70 per cent of enterprise data remains unconnected and inaccessible for advanced analytics.
The rise of AI debt
The push towards autonomous systems capable of independent decision-making is amplifying this risk of failure. While a majority of organisations plan to deploy AI agents in the near term, only a fraction have centralised their data or ensured their infrastructure can handle the projected surge in workloads.
The statistics are sobering. A recent report from Cisco found that fewer than one in five companies have fully centralised their data for seamless AI access. Over 60 per cent expect workloads to increase by more than 30 per cent within the next few years. Less than a third feel fully prepared to secure agentic AI systems against emerging threats.
Even the most digitally advanced firms are grappling with spiralling compute costs and persistent talent shortages in cybersecurity and AI engineering. In the same way that technical debt slowed software development in the 1990s and 2000s, AI infrastructure debt threatens to stall the current wave of transformation before it delivers meaningful returns.
At its core, AI debt is a data problem. AI systems amplify whatever they are trained on. If the data is incomplete or contextually degraded, the outputs will be flawed, often in ways that appear plausible but lack integrity. This phenomenon, sometimes described as AI slop, is not merely a technical nuisance but a commercial and reputational risk.
This occurs when organisations migrate or modernise without preserving metadata, lineage and governance and so its meaning is lost along with trust. In regulated industries, that erosion has legal implications. In competitive markets, it has revenue implications.
The path forward requires paying down the debt.
Paying off AI debt
At Cirata, we always advise that the best way to eliminate AI debt is to address fragmentation at its source. This means employing a system that can create a unified, interoperable data foundation that supports AI at scale. By decoupling data orchestration from underlying infrastructure, organisations can move, replicate and integrate data seamlessly across on-premises, hybrid and multi-cloud environments without disrupting production systems.
This approach delivers several strategic advantages. Automated data flows across clouds and platforms ensure models are trained and updated with the latest data. Businesses should always look to leverage open standards, such as Apache Iceberg, to prevent vendor lock-in and preserve long-term flexibility.
IT leaders should explore solutions to help them make sense of their data. By centralising governance and eliminating brittle integrations, organisations can feel confident they’ll be on the right side of AI project success. Most importantly, they can break the cycle of making short-term compromises that accumulate into long-term risk.
No algorithm will compensate for structural weakness
The promise of AI remains immense. Autonomous systems and generative models will continue to reshape industries. But no algorithm can compensate for a weak foundation. Just as a building requires structural integrity before additional floors are added, AI requires a unified and trusted data infrastructure before it can deliver sustained value. The organisations that thrive in 2026 and beyond will not be those that launched the most pilots. They will be those that had the discipline to eliminate their AI debt first.
Russell Gammon, Chief Innovation Officer at Alphatax, on why tax needs to operate on a shared foundation where information can flow and be updated consistently
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The modern CFO has more responsibilities than ever before, with a 2024 study finding that over 80% had taken on additional demands in the previous two years. As the report points out, “CFO is not a finance role. It is a strategic business role whose mandate is finance.”
With multiple priorities competing for time and attention, tax sits at the centre of many of the metrics CFOs are accountable for, from cash flow and risk exposure at the operational end of the scale to corporate reputation at the strategic level. Tax functions are under growing pressure to meet these expectations, with workloads increasing even as resources and budgets in many organisations remain flat or decline.
Under these conditions, tax teams are forced into a reactive, deadline-led operating model, and it’s understandable that work is completed in cycles rather than as part of an ongoing, connected process that continuously inputs into business decisions. The problem this creates, however, is that it perpetuates a clear disconnect between the undeniable importance of tax and the role it can and should play in day-to-day financial management.
A Taxing Set of Problems
It’s a situation that has evolved over a considerable period of time. For instance, most CFOs progress through finance-led career paths, with limited exposure to the depth and breadth of tax as a defined discipline.
In addition, tax is highly specialised and fragmented. It covers multiple complex, often interrelated requirements, each with its own processes and requiring access to specific subject matter experts. Even within tax teams, knowledge is distributed across individuals.
Tax also operates in a context where interpretation is often required, and clear-cut answers are not always immediately available. Bring these issues together, and it’s hardly surprising that many organisations view tax as a risk-sensitive function that must exist in its own bubble, rather than as one integrated into broader financial strategy or front and centre for the CFO.
The many and varied supporting technologies used across the tax function inevitably reflect this fragmentation. A large, expanding list of multiple-point solutions is used to address specific requirements rather than to provide a unified view of the tax function. This means tax data is almost inevitably spread across disparate systems and teams, and as any tax professional knows, the potential for inconsistencies and errors is ever-present.
Much of the work required to manage this complexity remains manual, and even where integration exists, insights are often buried within compliance outputs rather than being fed back into planning or decision-making.
Empowering the CFO
For CFOs, this situation almost inevitably draws them towards retrospective processes and analysis, when what they need is forward-looking insight. In practical terms, this reinforces a cycle of reactive intervention and increases the likelihood that opportunities to improve tax outcomes are missed.
This is increasingly at odds with the role CFOs are expected to fulfil, particularly given the emphasis on agile decision-making and the high levels of accountability that come with the job. How, for example, can a CFO be expected to make the best decisions about business expansion or investment when they do not have a clear, connected view of the organisation’s tax position?
Addressing these challenges should start with a commitment to break down the process, expertise and information silos that have historically defined the tax function; away from managing tax as a series of separate activities and towards a more integrated approach.
Tax needs to operate on a shared foundation where information can flow between different areas and be updated consistently. CFOs should be empowered with a coherent view of the organisation’s tax position and what is happening at any given point in time, without relying on the need for remedial work or retrospective reporting.
With better, integrated visibility at their disposal, the CFO can shift their focus towards strategic planning and opportunities, while the tax team can also address potential risks before they escalate.
Data Insights
A key part of this approach is making better use of the data generated through compliance processes, treating it not simply as an output but as a source of insight that can also be fed back into planning and forecasting.
Clearly, technology has an important role to play in facilitating this transformation, particularly in reducing the manual effort associated with routine tasks and improving the accessibility of data across the organisation. Don’t forget, the objective is not to remove human judgment from tax, but to ensure that specialists can focus their time on supporting the wider business.
In this context, the tax function takes on a different, more strategic role, with CFOs empowered to draw it into their decision-making processes rather than engaging with it only at the point of reporting, as so many still do today.
Craig Gravina, CTO at Semarchy, on why successful AI at scale requites the transparency and trust only proper governance foundations can deliver
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Across the globe, organisations are aggressively approving AI budgets, hiring teams, and deploying models, yet the promised returns remain elusive for many. AI investments continue to grow, but measurable business value is not keeping pace with the level of spend.
The numbers tell a striking story. Semarchy’s 2026 research of C-suite executives reveals that 99% of UK organisations claim AI readiness – with 69% saying they’re completely ready. Yet 56% making significant investments cite data management as their top challenge, while only 33% are prioritizing it for investment. This 23-point gap reveals the fundamental issue: organisations are building AI applications whilst underfunding the data infrastructure those applications require.
Globally, fewer than 2% of enterprises successfully avoid data quality problems, even though 74% planned to increase AI spending in 2025.
This isn’t a failure of technology talent or algorithms; it’s a governance breakdown. The path to faster AI ROI doesn’t run through better models, but through better governance and transparency, embedded at the center of every AI initiative from the outset.
The Hidden Cost of Ungoverned AI
When AI operates without governance, the primary risk shifts from technical to reputational. Whereas technical failures are recoverable, reputational damage is far harder to restore. Consider consequential AI decisions: credit denials, medical recommendations, hiring shortlists. When these go wrong publicly, the fallout extends far beyond the technical team. When regulators or customers ask how they make their decisions, organisations without governance have no credible answers.
The regulatory pressure only amplifies this risk. Global frameworks such as the EU AI Act, emerging UK regulations, and sector-specific rules increasingly require explainability and traceability. Companies that cannot show how AI reached its conclusions risk fines, restrictions, and forced remediation.
Public misfires, such as biased outputs or visible system breakdowns like Grok AI’s high-profile failure on X last year, can rapidly erode customer trust and brand equity. For consumer tech brands especially, reputational cost can exceed technical cost exponentially. Internally, unexplained or unreliable AI outputs erode confidence, resulting in disengaged business users, stalled initiatives, and data leaders losing credibility.
These aren’t exceptional cases. They’re the predictable result of deploying AI without governance infrastructure.
The Governance Misconception
Many organisations still treat AI governance as a compliance checkpoint, bolted on just before deployment to satisfy legal or risk requirements. This narrow interpretation is exactly what makes governance feel like an obstacle. Organisations feel forced into a false choice: move fast and bypass governance or be compliant and accept delays.
In this scenario, projects slow to a crawl. Data teams spend months preparing ‘clean’ datasets specifically for AI, only to sit in approval queues. By the time governance clears, business conditions have changed or stakeholders have moved on to new priorities.
The answer isn’t less governance; it’s different governance. Governance should be intrinsic to how data is defined, managed, and delivered. When governance “travels with the data,” models consume information that already carries quality checks, lineage, access controls, and semantic context already present. AI initiatives don’t wait for approval gates – they consume governed data from the start. There’s nothing to retrofit or remediate later, because the controls were in place from day one.
This shift from governance-as-gate to governance-as-infrastructure is the key divide between organisations that struggle to realise AI ROI and those that achieve it.
Transparency as a Strategic Asset
Leadership often frames transparency as a compliance requirement, but in AI, it’s just as critical for development speed. When data lineage is clear, AI teams don’t have to spend weeks figuring out where data came from, how it was transformed, or whether they can trust it. They can focus on building and refining models instead of investigating data provenance, shifting effort from detective work to value creation.
Transparency also removes technical friction. With explicit semantic context, models interpret data correctly without layers of custom preprocessing or extensive feature engineering. Consistent access controls across data sources prevent last minute security reviews from stalling projects at critical milestones.
Transparency enables continuous improvement cycles. When you can see exactly which data influenced a decision, you can diagnose errors with precision rather than guesswork. You can identify which specific data sources introduce noise or bias into model outputs, and measure whether data quality improvements affect AI performance in production, creating feedback loops.
Organisations that try to reconstruct this visibility only during audits pay for it repeatedly in delays, extended debugging, and failed deployments. By contrast, those that build transparency from the start move faster through development, testing, deployment, monitoring, and iteration. Transparency then becomes a competitive advantage rather than compliance burden.
What Real AI Success Looks Like
Real AI success isn’t a single breakthrough model. It’s the ability to deliver value repeatedly and at scale. Successful organisations move from proof of concept to production without major rework or last-minute governance scrambles.
In these organisations, AI consumes the same governed data as everyone else. Agents and models access data through shared interfaces and policies, alongside business users and operational systems. There’s no special ‘AI data prep’ phase, no separate pipelines to maintain, and no governance gap to close before go‑live. AI is simply another consumer of trusted data.
Explainability comes by default. Because lineage, transformations, and quality metrics are already present in the data, teams can trace any decision without forensic effort. When governance is built into data flows, AI teams can retrain on fresh data without re-running compliance reviews, keeping models current as business conditions change. Iteration cycles shorten from months to weeks or days.
This foundation also enables decentralised experimentation with centralised trust. Teams can launch AI initiatives against shared, governed data products without creating shadow pipelines.
Getting There: Three Practical Shifts
Reaching this future state doesn’t require a multi-year overhaul or new tech stack. It needs a focused effort in how data is delivered to every consumer, including AI, with governance built in rather than added later.
First, stop treating AI data preparation as a separate workstream. If AI teams need specially cleaned and packaged data, you’ve created handoffs that introduce delay and governance risk. Instead, provide data that all consumers can trust and use immediately.
Second, embed semantic context with the data. AI needs business context and meaning, not just schemas. What does ‘customer’ mean in this specific context – prospect, active user, former customer? What business rules apply, and what relationships matter for decision making? This semantic layer enables AI to interpret data correctly without custom workarounds.
Third, make lineage and quality observable by default. When lineage, quality scores, and transformation history are always available, debugging, compliance, and continuous improvement happen without emergency efforts.
Yet our research reveals the challenge: whilst 56% of UK organisations cite data as their top AI challenge, only 33% are prioritising investment in it. This misalignment between stated priorities and actual investment is precisely why AI initiatives continue to struggle, regardless of confidence levels.
The ROI Equation
The companies that achieve AI ROI faster aren’t the ones with the biggest budgets or the most sophisticated models – they’re the ones that eliminated the friction between data and AI consumption through proper governance infrastructure.
When governance is intrinsic, projects don’t wait for approval gates. With built-in transparency, debugging is fast, and audits are painless. The result? Shorter time to value, lower risk of public failure, reduced regulatory exposure, and AI investments that deliver measurable impact. These aren’t abstract benefits – they show up in project timelines, deployment success rates, and business KPIs.
The technology is ready. The question is whether your data governance is ready to support AI at scale. Success requires the transparency and trust that only proper governance foundations can provide.
Lee Nolan, GM UK&I at Hitachi Vantara, on why AI will not be defined by the sophistication of the models being deployed but the strength, consistency and reliability of the data that sits behind them
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Spend five minutes in any boardroom and AI will come up. Strategies are being signed off, budgets are being released and pilots are already underway, giving the impression that momentum is building at pace. Yet beneath that surface is a more uncomfortable reality, one that is becoming harder to ignore as organisations move beyond experimentation and into delivery.
Most organisations are trying to build AI on foundations that were never designed for it. The ambition is clear and well-funded, but the underlying data infrastructure has not kept up. That gap between intent and readiness is now becoming visible, particularly as organisations look to scale beyond isolated use cases and deliver outcomes that are consistent and commercially meaningful.
Recent research into UK businesses reinforces this point. While adoption continues to move forward, only a small number of organisations are genuinely set up to support AI at scale. The issue is not tools or talent, but the condition of the data at the core of the business.
AI Exposes What Businesses Would Rather Ignore
AI is often described as a layer that can be applied to existing systems to unlock value. In practice it does the opposite. It brings complexity into sharp focus, exposing inconsistencies and inefficiencies that may have been tolerated for years.
That is why data quality has moved to the centre of the conversation. Around 67% of UK organisations now cite it as the primary driver of AI success. The shift reflects a growing awareness that no level of investment in AI can compensate for weak or unreliable inputs.
For many organisations, data has evolved without a consistent approach to governance. In fact, Gartner estimates that 80% of organisations attempting to scale digital initiatives will fail due to weaknesses in data and analytics governance. Systems have been added over time, ownership is unclear and definitions vary. The result is a fragmented environment where the same metric can mean different things across the business. When AI is introduced, it does not resolve those inconsistencies, it amplifies them.
The Hype Cycle is Giving Way to Reality
Over the past year, there has been a shift in how organisations approach AI. The early phase was driven by urgency, with businesses keen to move quickly and demonstrate progress. That momentum remains, but it is now being balanced by a more realistic perspective.
There is a greater focus on outcomes, with more scrutiny on how AI is delivering value. Many organisations have realised that quick wins are harder to achieve when the underlying data is not fit for purpose, and that scaling AI requires a level of operational discipline that cannot be bypassed.
What was initially framed as a technology challenge is now understood as a business challenge, spanning processes, ownership and governance as much as platforms and tools.
Confidence is High but Capability is Uneven
Confidence across organisations remains high, but it often does not reflect reality.
While many businesses consider their data infrastructure to be mature, progress is often uneven. Some teams may be working with well governed data, while others are still reliant on manual processes and disconnected systems. This creates a situation where parts of the business are ready to move forward, while others are not, a challenge reflected in wider industry research from McKinsey & Company, which highlights siloed data as one of the biggest barriers to scaling AI.
That inconsistency is where problems begin. AI depends on trust in the data If that trust is not consistent across the organisation, outputs become unreliable and adoption slows. From the outside, many organisations appear ready, but internally the foundations are still being stabilised.
Control vs Convenience
There is also a growing emphasis on control, particularly around where data is stored and how it is managed. Data sovereignty is now playing a central role in decision making, with around 85% of UK organisations saying it directly influences how they deploy AI.
This reflects a broader recognition that data is both a critical asset and a potential point of risk. As organisations become more reliant on it, they are also becoming more deliberate in how it is governed and protected.
At the same time, the expectation that everything should be built in-house is fading. Many organisations are turning to external partners to accelerate progress, while retaining control over their data and strategic direction. This balance allows them to move faster without losing oversight.
Vague AI Strategies
One of the most persistent challenges is the lack of clarity around what AI is meant to deliver. Too many initiatives begin with broad ambition and little definition of success, resulting in activity that is difficult to measure and even harder to scale. This is reflected in wider industry trends, with Gartner noting that only 53% of AI projects make it from prototype into production.
The organisations making progress are far more structured. They define clear objectives, establish measurable outcomes and maintain a focus on value throughout. In the UK, a growing majority are now putting formal KPIs in place for their AI initiatives.
This shifts AI from being an experiment to something that can be managed, evaluated and improved over time.
AI Will not Wait for Organisations to Catch Up
AI will continue to evolve at pace, and the pressure on organisations to keep up is unlikely to diminish. What is changing is the nature of the conversation. It is becoming less about what AI can do in theory, and more about what organisations are actually capable of delivering.
Most are still in the process of building the foundations required to support AI effectively. That is not a failure, but it does define the scale of the challenge ahead.
Because ultimately, AI will not be defined by the sophistication of the models being deployed. It will be defined by the strength, consistency and reliability of the data that sits behind them.
Sanofi: Supporting the World’s Health Through Data
This month’s cover story spotlights Sanofi, one of the world’s largest pharmaceutical companies. For an organisation that puts the end-user – the patient – first, this requires an unwavering focus on R&D and continuous improvement. For the sake of the world’s health; every patient counts. So, when opportunities arose to improve services through data and advanced technology like AI, Sanofi brought in experts to steer and develop the journey.
Snehal Patel, Head of Global Data and AI Platform, takes a deep dive with Interface… “These innovations have fundamentally transformed Sanofi’s data and AI value chain,” says Patel. “It’s enabled scalable and efficient development across the organisation. We now have a far more agile development environment that supports the broader AI initiatives at Sanofi.”
Anson Cho, Director of Information Security & Data Protection at Langham Hospitality Group, discusses the pandemic’s silver lining and the development of a proprietary matrix to embed security into the heart of operational excellence.
“Our strategy wasn’t about over-engineering our systems to match the spend of a global financial institution; it was about increasing our defensive maturity so we are never an easy mark,” says Cho. “In cybersecurity, you want to ensure your barriers are sophisticated enough that attackers move on. We focus on staying ahead of the curve and continuously evolving so that our security posture remains a formidable deterrent.”
FNB: Redefining Data Science in Commercial Banking
Yudhvir Seetharam, Chief Analytics Officer at South Africa’s First National Bank (FNB) on a data science journey characterised by curiosity, culture and the drive for a competitive edge.
“Ours is a holistic approach focusing on the customer,” he explains. “Understanding the context of each customer journey and then using that context so that when we interact with you, we’re able to drive the right conversation with the right customer, at the right time, through the right channel and for the right reason. These ‘five rights’ make our interactions with clients more impactful than a spray and pray approach.”
Katja Hakoneva, Product Manager at Tuxera, on delivering tomorrow’s data storage security today
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Smart meters are no longer just data endpoints. They’re intelligent, connected nodes embedded into the national infrastructure. As energy networks undergo rapid digital transformation, the focus has largely been on secure communications and real-time data transmission. But beneath the surface lies the local data storage, which often becomes a critical blind spot.
Smart meters store large volumes of sensitive data from energy usage profiles to firmware logs and grid event histories on embedded memory. If this information is accessed, altered, or deleted, it can trigger billing inaccuracies, regulatory breaches, and customer mistrust. With meters expected to operate in the field for up to 20 years, data-at-rest security is a critical requirement.
Storage Vulnerabilities: The Silent Cyber Threat
These embedded systems face multifaceted risks. Attackers may gain access to stored data by physically tampering with a meter or exploiting software vulnerabilities that bypass weak authentication. Malicious actors could manipulate logs to alter billing records, mislead consumption analytics, or mask larger cyberattacks on grid infrastructure.
In many cases, such intrusions go undetected until tangible damage, such as lost revenue or reputational fallout. With increasing dependence on smart infrastructure, utilities can no longer afford to treat embedded storage as a passive component.
Counting the Real Costs of Cybersecurity
Securing smart meters comes with technical requirements, as well as, operational and resourcing demands. For many UK manufacturers and utilities, managing cybersecurity internally means building and retaining specialist teams, often requiring three to five full-time professionals to handle vulnerability monitoring, patch management, and threat response throughout the year.
Aligning with regulatory frameworks frequently demands hardware upgrades to handle stronger encryption and secure configurations, impacting Bill of Materials (BOM) costs and development timelines. Many existing software stacks require optimisation to support modern security protocols within resource-constrained devices. These efforts are necessary, with a single undetected cyberattack costing companies an average of $8,851 (≈£6,900) per minute, and the consequences extending beyond financial loss to potential regulatory fines and service disruptions.
The CRA and the new Era of Cyber Regulation
The Cyber Resilience Act (CRA), set to come into force across the EU by 2027, will reshape how connected devices are designed, developed, and supported. For UK-based vendors serving the European market, or collaborating with EU counterparts, compliance with CRA is becoming a strategic imperative.
Key CRA requirements include:
Security by design: Devices must be secure from the outset, not retrofitted post-deployment.
No known vulnerabilities at market launch: Products must undergo security validation prior to release.
Default secure configurations: Devices should avoid insecure settings out of the box.
Lifecycle management: Vendors must support patching and vulnerability resolution throughout the device’s operational lifespan.
For smart meters, which often run in the field for two decades or more, the CRA introduces accountability that extends well beyond product launch. Compliance with the CRA will become part of the CE marking process, meaning global manufacturers must align if they wish to sell into the EU energy market.
Engineering Security: Confidentiality, Integrity, and Authenticity
Designing resilient smart meters starts with three pillars:
Confidentiality protects sensitive user data from unauthorised access. This includes encrypting both data and encryption keys, restricting user access levels, and securing communication channels.
Integrity ensures stored data remains unaltered and trustworthy. Power failures, for instance, can corrupt memory. Using flash-optimised file systems and secure boot processes can prevent such vulnerabilities.
Authenticity confirms that firmware and data updates come from trusted sources. Techniques like digital signatures and update validation prevent attackers from injecting malicious code into meters.
Together, these pillars enable smart meters to meet regulatory expectations while protecting both users and grid operations.
Future-proofing Data Storage
Cybersecurity for smart meters is not just a feature; it requires organisational readiness. Frameworks like the CRA, NIST, and IEC 62443 emphasise secure processes, documentation, and people alongside secure products.
For companies looking to prepare, it is smart to start with common pillars such as maintaining up-to-date Software Bills of Materials (SBOMs), conducting regular supply chain and risk assessments, keeping detailed test reports, and establishing clear incident response plans. Internally, training staff on cybersecurity best practices, setting clear data retention policies, and defining access controls and responsibilities are critical steps to ensure cybersecurity is embedded within the culture of the organisation. This approach ensures security is not a one-off compliance task but a sustainable practice that protects smart infrastructure long-term.
Smart meters deployed today could still be operating in the 2040s. This timeline intersects with the anticipated emergence of quantum computing, which may break today’s encryption standards. Though post-quantum cryptography is still evolving, vendors must prepare now to ensure systems remain secure in a post-quantum world. Smart meter software should be designed with cryptographic agility to allow it to adapt and upgrade algorithms as threats evolve.
Lessons from Long-Term Deployment
Smart meters are designed for longevity, but memory wear remains a primary failure point. Meters that lack flash-aware storage systems face early data loss, increasing the cost of maintenance, replacements, and warranty claims.
Utilities and OEMs that embed file systems capable of wear levelling, garbage collection, and secure boot processes have extended meter lifespans by more than 50%, even in challenging conditions. One example showed meters surviving over 15,000 power interruptions without any data loss.
Integrating secure storage delivers operational and commercial benefits. It ensures compliance with CRA and other evolving global frameworks, reduces maintenance and warranty costs, minimises carbon impact through fewer replacements, enhances brand credibility and trust with procurement teams, strengthens the business case for longer-term contracts and partnerships. As the smart energy market matures, these benefits are becoming differentiators, especially as digital infrastructure grows in complexity.
Delivering Tomorrow’s Data Storage Security Today
The next generation of smart infrastructure will be fast and connected, as well as, secure, resilient, and regulation-ready. For vendors and utilities alike, embedding data protection deep into the meter architecture is a business-critical move.
By preparing for the CRA today, smart meter manufacturers will position themselves as forward-thinking, trustworthy partners in tomorrow’s energy ecosystem, delivering technology that’s not only built to last but built to protect today and tomorrow.
Emma Steeley, CEO of Infinian, the global real time credit intelligence bureau providing data to banks, lenders and other data businesses, explains the consequences of credit data being stuck in the past, and how banks and fintechs can overcome the mounting consequences
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Despite a cost-of-living crisis and unpredictable economic outlook, too many lenders are forced to make credit decisions using information that belongs to another era. This outdated data is based on small samples, derived from national averages and historical surveys that fail to capture the volatility and diversity of financial realities defining life in the UK today.
That disconnect between data and reality harms consumers, distorts pricing, and drags on the wider economy. In short, affordability decisions are outdated before they are made. Borrowers are judged on figures that don’t reflect their actual costs, creditworthy customers are turned away, while others are approved for loans they can’t afford. Real-time, accurate, large-sample data is essential for fair and functional credit markets, and as an industry we must work to ensure decision-making is dragged into the modern day, to support the integrity of financial services and the aims of Consumer Duty for the good of financial services and consumer duty.
Legacy Models Versus Modern Risks
For years, affordability models have relied on spending benchmarks from the Office for National Statistics (ONS) and other national-level datasets. ONS data, often sourced from the Living Costs and Food Survey, can lag real-world conditions by more than a year. It captures what households spent yesterday, not what they face today.
When models depend on national averages and retrospective surveys, they miss the nuances of how people earn and spend. Workers on variable incomes, renters, and those without long credit histories are most likely to be penalised. They may be financially stable, but legacy data can’t see that, leading to unnecessary declines and reinforcing the gap between those who can access affordable credit and those who can’t. Moreover, outdated data also increases the risk of false positives, meaning lenders may approve those who are likely to default.
False positives and negatives aren’t the only concerns, but also compliance – the Financial Conduct Authority’s Consumer Duty makes clear that firms must deliver “good outcomes” for retail customers, including through fair pricing and practical support. If lending decisions are based on incomplete or obsolete data, it becomes difficult to evidence that duty. The FCA’s own CONC 5.2A rules require a “reasonable assessment” of a customer’s ability to repay; data that misrepresents current affordability can’t reasonably support that test.
Legacy benchmarks, once a useful proxy, now risk embedding unfairness. They distort pricing, entrench exclusion, and hold back lending when the economy most needs momentum.
Gaining a True Perspective on Affordability
Fresher, more granular data is changing what responsible lending can look like. Real-time or high-frequency data streams from verified income flows, transaction activity, and recurring payment histories provide lenders with a comprehensive picture of affordability.
Unlike static surveys, these sources track actual behaviour. They show how a household’s disposable income shifts month to month, how energy or rent payments fluctuate, and how consistently people meet obligations. When used responsibly, this information enables lenders to make faster, more informed decisions that align with each borrower’s actual circumstances.
The payoff is fairer, more inclusive, and more responsible: three goals that don’t have to be in tension. Real-time credit intelligence can also help reduce unnecessary declines, extend access to consumers previously considered “thin-file,” and still maintain prudent risk controls. In other words, responsible lending doesn’t have to mean lending less; it means lending smarter.
It also helps lenders identify early signs of financial stress. If outgoings begin to rise faster than income, that signal appears immediately rather than months later, allowing firms to step in with tailored support before problems escalate. By closing the gap between reality and response, real-time data enables lenders to be both fairer to customers and more agile in managing their portfolios.
The Commercial Case for Better Data
Aside from the moral argument, and the benefits it will bring to compliance and consumer protection, there’s also commercial incentives to modernise credit data.
With access to better data, lenders can approve more of the right customers without increasing risk. Decision engines will become sharper, with improved acceptance rates and portfolio performance simultaneously.
Speed is another advantage. Consumers nowadays expect instant answers and laggy underwriting processes can make customers shift to faster competitors. Access to real-time credit data enables lenders to expedite these processes, thereby improving satisfaction and conversion rates. In a crowded market, those gains translate directly into loyalty and market share.
Basing decisions on current financial behaviours also reduces the need for unnecessary full-bureau checks and manual interventions, lowering the cost per decision and freeing up resources for higher-value activity.
Ultimately, modernisation is about competitiveness. Financial institutions, whether banks or fintechs, that invest in real-time credit intelligence today will be well-placed to earn trust, loyalty, and market advantage.
The Future of Fair Finance
Credit markets rely on accuracy, and accuracy in turn depends on timeliness. When the information behind lending decisions lags behind real life, fairness falters, capital is mispriced, and opportunities are lost.
Real-time, representative data allows lenders to extend credit responsibly, price risk precisely, and support customers before problems arise. It strengthens inclusion while improving overall performance.
In a world where household finances can change in weeks, lending models must keep pace with reality. Institutions that invest in live, comprehensive data today will set the benchmark for fair and effective finance in the years ahead.
Jalal Charaf, Chief Digital & AI Officer of the University Mohammed VI Polytechnic (UM6P) and Managing Director of Ecole Centrale Casablanca on how Africa can seize its moment to lead on data
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In today’s world, data is not just about numbers and technology; it shapes how people live, how governments plan, and how businesses grow. It influences who gets a loan, who receives medical care, and who has access to education. That’s why control over data, called data sovereignty, is becoming one of the most important sources of power in the 21st century.
Unfortunately, Africa is still on the margins of this new reality. Although the continent is home to over 1.4 billion people, 18% of the world’s population, it provides less than 4% of the data used to train today’s most powerful AI systems. Most African data is stored in foreign data centres, beyond the reach of African laws and courts. This is no longer just a ‘digital divide’, it’s a dependence on outside systems that don’t fully understand or represent African realities.
What’s Holding Africa Back?
There are several key reasons why Africa remains largely underrepresented in the global digital economy.
First, representation. Most AI systems are built on data from outside Africa. As a result, they often misjudge or misrepresent African realities, whether it’s credit scoring, medical diagnostics, or speech recognition. The absence of African data creates blind spots that affect real lives.
Third, governance. With 29 different national data protection laws, Africa lacks a unified approach to managing data. In contrast, the European Union negotiates data rules as a single bloc. Africa’s fragmented regulatory landscape makes it harder to attract investment or protect citizens’ rights.
Morocco offers a model of what digital sovereignty can look like. In June 2025, a consortium led by Nexus Core Systems announced a 500-megawatt, renewables-powered AI infrastructure project on the Atlantic coast. Phase one, with 40 MW of NVIDIA’s Blackwell AI chips, will go live in early 2026, exporting compute power across Europe, the Middle East, and Africa.
Critically, this infrastructure is under Moroccan jurisdiction, not subject to U.S. laws like the CLOUD Act. The project proves that African countries can host cutting-edge data systems while protecting their own legal and strategic interests.
How Africa Can Lead
To turn early momentum into lasting sovereignty, African governments, institutions, and partners must work together across four pillars:
Data creation and curation. Countries should invest at least 1% of GDP in digital public infrastructure, such as national ID systems, crop mapping satellites, and open data portals. These systems ensure that African data reflects African lives.
Compute and storage. Regions with access to renewable energy can build local ‘green AI corridors’ linked by neutral internet exchanges. This keeps data close to where it’s generated and cuts dependence on foreign servers.
Policy and regulation. The African Union should lead a continent-wide Data Sovereignty Compact, a framework to harmonise data protection, localisation, and AI ethics. A unified legal environment will attract investment and support responsible innovation.
Talent and research. African universities and public agencies should develop homegrown AI talent. Governments can require that models trained on African data are hosted locally. Research must be rooted in African languages, priorities, and realities, not just imported standards.
A Role for Everyone: From Governments to Global Partners
Governments should commit at least 10% of their ICT budgets to data sovereignty and adopt AU-wide standards. Local cloud facilities and fibre infrastructure deserve long-term funding, not just short-term pilots.
Private industry must shift from short-lived cloud credits to permanent, on-the-ground investment. Companies should publish annual data localisation reports and follow the example set by Nexus Core Systems.
Universities, civil society groups, and non-profits also have a responsibility. Open data repositories, civic tech labs, and ethical data governance initiatives must be scaled up to support innovation that’s inclusive and local.
Africa has everything it needs to become a global leader in digital intelligence. Its young population, growing tech talent, and renewable energy potential are powerful advantages. But sovereignty will not be handed over, it must be built.
We must act now, before the rules of the digital world are written without us. Morocco’s Nexus Core project shows what’s possible when ambition meets action. It’s time for the rest of the continent to follow suit, and shape a future where Africa owns its data, tells its stories, and sets its own course.
Cyrus Gilbert-Rolfe, Chief Commercial Officer at Kezzler, dives into how supply chain professionals can prepare for the future by standardising their data.
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In today’s world of fragmented value chains and increasing uncertainty, supply chain disruption is no longer an exception – it’s sadly, often, the norm. Whether due to global conflicts, climate events, pandemics, or regulatory pressure, businesses must now operate with agility and foresight. And at the heart of this transformation lies a simple but critical need: data.
More specifically, the ability to capture, share, and interpret granular supply chain data in real time is becoming a cornerstone of operational resilience, sustainability, and regulatory compliance. That’s where EPCIS 2.0, GS1’s visibility data standard, comes into play.
Unlike its predecessor, EPCIS 2.0 reflects the reality of modern supply chains. It supports richer, more structured data, enabling interoperable traceability across systems, stakeholders, and borders.
Digital traceability is no longer optional
The demand for traceability is growing exponentially. Consumers expect to know where their products come from, under what conditions they were made, and how they can be reused or recycled. Regulators, particularly in the EU, are implementing frameworks like the Digital Product Passport (DPP) to enforce such transparency.
These shifts introduce massive data requirements that legacy systems were never designed to handle. Fragmented systems, paper-based processes, and non-standard formats not only increase inefficiencies, but they also make compliance, sustainability, and recall management nearly impossible to scale.
EPCIS 2.0 is built to address this. It provides a common language for supply chain events, allowing businesses to capture detailed, event-based data such as where an item was shipped, under what temperature conditions, or which batch of raw material was used. This level of insight can be the difference between a swift product recall and a full-blown crisis.
From compliance to circularity: What EPCIS 2.0 enables
The relevance of EPCIS 2.0 extends far beyond compliance. Its core capabilities are based on capturing the ‘what, when, where, why, and how’ of each product movement or transformation, making it a foundational tool for the circular economy.
Sustainability: By embedding certifications, sustainability claims, and environmental data into digital events, companies can provide transparent proof of product provenance and lifecycle impacts.
Recall and risk management: When a problem arises, whether a contaminated food ingredient or faulty component, companies can immediately isolate and trace the affected batches, minimising financial and reputational damage.
Product lifecycle management: By tracking items from production through repair, resale, and recycling, EPCIS 2.0 supports extended producer responsibility and enables efficient returns or refurbishment programs.
Crucially, this level of traceability is achieved not through bespoke integrations or proprietary software, but through global standards, enabling seamless interoperability across borders and industries.
A real-world example: Building a data marketplace at scale
The journey toward end-to-end digital traceability can be complex. But when done right, the benefits extend far beyond logistics.
Take the case of Migros Group, Switzerland’s largest retailer. Facing challenges around fragmented data, inefficient returns processes, and lack of supply chain visibility, Migros set out to modernise its operations – not through piecemeal tools, but through the creation of a centralised Logistics Data Marketplace based on EPCIS 2.0.
This initiative involved:
Assigning unique digital identities to each returnable transport item (RTI), enabling precise tracking and reuse.
Automating data capture using RFID, which reduced reliance on manual entry and minimized errors.
Capturing EPCIS event data for key steps like aggregation, shipping, and receiving – allowing for full visibility of every batch, pallet, and shipment.
The result? Improved shelf availability, reduced waste, faster goods receiving, and a stronger foundation for sustainability reporting. Most notably, the data was not siloed – it was made available through a collaborative platform where all stakeholders, from manufacturers to distributors, could access the same real-time insights.
How supply chain leaders can prepare
While EPCIS 2.0 is technically advanced, its real power lies in its simplicity: using shared standards to enable shared visibility. But to implement it successfully, companies need to follow some strategic steps:
Start with your business problems: Whether it’s improving inventory accuracy, meeting regulatory demands, or enabling product take-back schemes, your use case should drive your data model – not the other way around.
Map your critical process steps: Identify where visibility matters most. For example, in a cold chain, temperature monitoring at transit points may be critical. In manufacturing, the transformation of raw materials into finished goods is key.
Model visibility events: Using EPCIS’s event types you can structure how each step is tracked, verified, and shared.
Use the Core Business Vocabulary (CBV): Adhering to standardised vocabulary ensures your data can be understood and used by partners and regulators alike.
Enable interoperability through Digital Link: Combining EPCIS 2.0 with the GS1 Digital Link standard allows serialized product data to be directly embedded into on-pack codes, creating a bridge between physical products and digital data.
Looking ahead: A foundation for resilience
The convergence of regulation, consumer expectation, and technology is changing how businesses think about supply chains. What was once an operational back end is now a strategic asset – central to reputation, revenue, and resilience.
By adopting EPCIS 2.0, companies are not simply responding to change – they are laying the groundwork for a future-ready infrastructure. This approach enables real-time, data-driven decision-making, facilitates transparent product journeys that help build consumer trust, and allows for faster, more accurate responses to disruptions. Additionally, it fosters smarter collaboration across supply chain networks, ensuring all stakeholders can operate with a shared understanding and greater agility.
The stakes are high, but the opportunity is greater. For those willing to embrace data standardisation and traceability, EPCIS 2.0 offers a clear and powerful path forward.
Transformational success with technology is about more than just ‘keeping the lights on’. Our cover story this month spotlights National Grid with the story of an innovation programme empowering everyone across the organisation on a shared transformation journey. Global Head of Data Strategy, Andrew Burns, tells Interface how connections like these are driven by data.
“We have new energy sources, greater demand and an opportunity to gather more data than ever before. Technologies like artificial intelligence (AI) and augmented reality (AR) are revolutionising how we use that data. Today, data and these technologies are combining to increase our ability to deliver value to our customers, and society.”
Asian Hospital and Medical Center: Leading the technology revolution in healthcare
Asian Hospital and Medical Center, one of the largest and fastest growing premiere hospitals among the close to 30 hospitals in the Metro Pacific Health Group, is the pioneer of an integrated healthcare network in the Philippines. Frank Vibar, CITO at Asian Hospital and the former Group CIO of the MPH Group, reveals the IT strategic roadmap that will deliver a true regional hospital.
“AHMC’s vision is to become the centre of global expertise in caring for the unique needs of our patients and the communities we serve.”
Also in this issue of Interface…
We hear from Tecnotree on the year ahead for the Telco industry; get the lowdown on meeting the challenges of integrating Agentic AI from Confluent; learn about the importance of Cybersecurity investment in OT (Operational Technology) from Claroty; and discover how IoT-enabled digital customers are reshaping customer experiences with Content Guru.
Deepak Parameswaran, Sector Head – Energy, Manufacturing & Resources at Wipro, talks innovation with National Grid’s Global Head of Data Strategy Andrew Burns
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Partners for over 25 years, Wipro and National Grid have been laying the foundation for progress… By taking data to the cloud, creating value and leveraging their common work to deliver advanced, data-driven innovations across the National Grid enterprise.
Meeting the transformation challenge
As a utility, National Grid seeks to provide safe, affordable, and reliable electric and natural gas service for its customers. As such, the company is hyper-focused on natural gas, electricity grid modernisation, customer satisfaction and the integration of business and technology processes across the entire business as gas and electricity demand increases across the markets. Wipro offers actionable solutions, providing the innovative technology and domain expertise necessary for organisations like National Grid to transform and become leaders in sustainability within their respective industries.
Delivering bespoke solutions for Innovation
Traditional utility technologies can pose challenges in terms of complexity and capital investment. With Cloud and AI technologies emerging as game changers, Wipro delivers a proven ecosystem, incorporating analytics, IoT, Generative AI, and Augmented Reality, tailored to meet the needs of customers, assets, and grid management. This makes for easier, scalable, and faster to market solutions that allow National Grid to quickly realise the benefits. Wipro’s Utility Enterprise solutions have delivered on key elements of the digital transformation journey at National Grid. This allows for a constant data presence across the globe, creating a common, secure cloud environment.
Wipro’s partnership with National Grid
Wipro’s collaboration with National Grid continues to be built on a foundation of continuous innovation, with a commitment to:
Staying ahead of utility business trends
Supporting National Grid’s clean energy transition
Developing sophisticated data and AI solutions for enhanced customer service
Maintaining agility to address emerging challenges
“Wipro has been our biggest partner in executing use cases through the Innovation Lab, enabling us to be agile and deliver multiple projects with direct, tangible business benefits. Their support has been vital in ensuring a clear, efficient process and rapid execution, making them key to our success.”
Andrew Burns, Global Head of Data Strategy, National Grid
Click here to read more about National Grid’s Innovation story
Tech Show London is coming to Excel March 12-13. Register for your free ticket now!
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Unlock unparalleled value with a single ticket that gets you free access to five industry-leading technology shows. Welcome to Cloud & AI Infrastructure, DevOps Live, Cloud & Cyber Security Expo, Big Data & AI World, and Data Centre World.
Tech Show London has it all. Don’t miss this immersive journey into the latest trends and innovations.
Discover tomorrow’s tech today
Unleash Potential, Embrace the Future. Hear from the greatest tech minds, all in one place.
Dive into a world where cutting-edge ideas shape your tomorrow. Tech Show London is the epicentre of technology innovation in London and beyond, hosting the brightest minds in technology, AI, cyber security, DevOps, and cloud all under one roof.
The Mainstage Theatre is not just a stage; it’s a launchpad for innovative ideas. Witness a stellar lineup featuring world-renowned experts from across the tech stack, influential C-level executives, key government figures, and the vanguards of AI and cybersecurity. All ready to share ideas set to rock the industry.
GLOBAL INSPIRATION, LOCAL IMPACT
Seize the opportunity to be inspired by global visionaries. Furthermore, with speakers from the UK, USA, and beyond, prepare to be inspired by transformative concepts and actionable strategies from technology insiders, ensuring your business stays ahead in an ever-evolving technology landscape.
Where the future of technology takes the stage
Secure your competitive edge at Tech Show London, the UK’s award-winning convergence of the industry’s brightest tech minds.
On 12-13 March 2025, gain vital foresight into the disruptive technologies reshaping your market, and position your organisation at the forefront of technology’s next frontier.
If you’re defining your business’s tech roadmap, register for your free ticket to join us at Excel London.
Catching up with Mitha-Ai’s Co-Founder, Arash Saberi, we dive into the vital importance of a solid data foundation.
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Whether we’re talking about gen AI, 10X, or any other kind of advanced tech solution, data is at the core of the discussion. And when data isn’t clean or ready for the implementation of something being built on top of it, businesses can end up significantly held back. Mithra-Ai is an organisation that helps its customers to build trust in their data, which is a core issue for many.
“That sets us apart,” says Arash Saberi, Co-Founder of Mithra-AI. “We help procurement leaders and category managers create, execute, and realise their strategies. This is backed by reliable, comprehensive data, both internal and external, tailored specifically for their categories.
“Maintaining high-quality data is crucial as it influences the accuracy and reliability of AI-driven insights and recommendations. That’s where Mitha-AI comes in. Our cleansing, enrichment, and auto-classification engines ensure that procurement stakeholders, including data scientists, begin with a reliable data foundation.”
Cleaning and classifying data
Mithra-Ai is an AI-native SaaS solution, which starts off by proposing a meaningful spend hierarchy for every category. What’s key is that this is paired with an automated cleansing and classification engine. This is so important because the only way to achieve truly clean data is to make sure it enters the system clean in the first place.
“Clear visibility into categorised spending eliminates uncategorised expenses and wrong assumptions,” says Saberi. “When supplemented by relevant external data intelligence, category managers are empowered to negotiate with confidence, achieve greater savings, and monitor initiatives effectively.”
A world beyond cost savings
When launching Mithra-Ai in 2021, the company’s founders rightly foresaw that the role of procurement would evolve beyond focusing merely on cost savings, and become the central hub of every organisation. Because of that, they knew that accurate, reliable information was needed – hence the necessity for Mithra-Ai.
As procurement has shifted, the status quo is no longer good enough. It’s an exciting time for the sector, but also one of high demand in the race to adopt increasingly advanced technology. But it’s necessary for efficiency and growth.
“Tesla and Nvidia exemplify the power of embracing change over maintaining that status quo,” says Saberi. “Procurement is facing intense pressure to evolve with organisational needs. Those organisations can opt for incremental changes, which will likely slow them down, or pursue a 10X leap to maintain competitive advantage. The latter requires bold and decisive leadership from heads of procurement.”
The road to 10X thinking
The way to drive 10X thinking, Saberi believes, is through having a clear vision of your goals. Sometimes businesses, especially ones which are going through major change or those navigating outdated legacy systems, are at risk of losing sight of their goals. But having that vision is a foundational necessity, regardless of what stage you’re at.
“Set aspirations high, and question existing norms,” says Saberi. “Procurement leaders can draw inspiration from startups by fostering a culture of innovation through small-scale initiatives that can rapidly expand. Reevaluate the skills and team structure necessary for future success.”
Another important aspect to bear in mind when considering these things is the level of risk you’re willing to undertake when setting goals and aspirations. “That’s often overlooked,” Saberi continues. “Determining the acceptable level of risk is crucial. It significantly influences partner selection and the outcome of RFPs.”
Thinking big, starting small
While ambition is vital to 10X thinking and beyond, businesses must also make sure they don’t bite off more than they can chew. Launching into adopting huge volumes of advanced technology can lead to overwhelm and can make a business stall rather than evolving. A more careful approach is required.
“Think big, start small,” says Saberi. “Prioritise high-impact, low-effort initiatives over those requiring significant effort. Many transformation projects fail to deliver the expected benefits and incur high costs during the program.” This is another reason to decide on the appropriate risk level early on, in order to guide prioritisation decisions and transformation pace.
It’s an incredibly exciting time for procurement, and that includes Mithra-Ai. In a very short time, it’s developed several foundational modules for its data-driven category management solution. This includes the Collaborative Initiative Tracker that was launched during DPW Amsterdam 2024 – just one of Mithra-Ai’s inspiring undertakings as we approach 2025.
“The tracker means that procurement teams can now involve multiple stakeholders in collaboratively tracking and enhancing the impact of key initiatives, such as cost-saving measures,” says Saberi. “Exciting times lie ahead.”
DPW Amsterdam is the perfect stage for launching a solution like this. It’s an event that inspires a culture of innovation, bringing procurement professionals together to teach, learn, and shout about their latest additions to the procurement landscape.
“DPW stands out as the premier procurement tech event of the year,” says Saberi. “Practitioners can explore and engage with procuretech suppliers, showcasing valuable use cases and personal stories across multiple stages. DPW is a catalyst for ideation, creating trust and confidence in the benefits of applying cutting-edge technologies to improve business outcomes. This year’s event felt even more international than previous years. I look forward to seeing it continue to grow.”
Saberi’s main takeaway from DPW Amsterdam this year is that a solid data foundation is essential – something he was well aware of as part of Mithra-Ai. “Without it, transformation projects and new technologies will struggle to succeed,” he concludes. “In the past two years, there has been increased focus on sustainability and risk intelligence, driven by numerous new solution providers. However, during the DPW Amsterdam 2024 conference, we observed new trends coming up and, again, more focus on data quality, which works to our advantage.”
Our cover star, EY’s Global Chief Data Officer Marco Vernocchi, tells Interface why data is a “team sport” and reveals…
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Our cover star, EY’s Global Chief Data Officer Marco Vernocchi, tells Interface why data is a “team sport” and reveals the transformation journey towards realising its potential for one of the world’s largest professional services organisations.
Welcome to the latest issue of Interface magazine!
Global Chief Data Officer, Marco Vernocchi, reflects on the data transformation journey at one of the world’s largest professional services networks.
“Data is pervasive, it’s everywhere and nowhere at the same time. It’s not a physical asset, but it’s a part of every business activity every day. I joined EY in 2019 as the first Global Chief Data Officer. Our vision was to recognise data as a strategic competitive asset for the organisation. Through the efforts of leadership and the Data Office team, we’ve elevated data from a commodity utility to an asset. Our formal data strategy defined with clarity the purpose, scope, goals and timeline of how we manage data across EY. Bringing data to the centre of what we do has created a competitive asset that is transforming the way we work.”
PivotalEdge Capital
Sid Ghatak, Founder & CEO of asset management firm PivotalEdge Capital, spoked to us about the pioneering use of “data-centric AI” for trading models capable of solving the problems of trust and cost.
“I’ve always advocated data-driven decision-making throughout my career,” says Ghatak. “I knew when I started an asset management firm that it needed to be data-centric AI from the very beginning. A few early missteps in my career taught me the importance of having a stable and reliable flow of data in production systems and that became a criterion.”
LSC Communications
Piotr Topor, Director of Information Security & Governance at LSC Communications, discusses tackling the cyber skills shortage, AI, and bringing together the business and IT to create a cyber-conscious culture at a global leader in print and digital media solutions.
Topor tells Interface: “The main challenge we’re dealing with is overcoming the disconnect between cybersecurity and business goals.”
América Televisión
Interface meets again with Jose Hernandez, Chief Digital Officer at América Televisión, who reveals how the company is embracing new business models, and maintaining market leadership in Peru.
“Launching our FAST channel represents a pivotal step in diversifying our content delivery and monetisation strategies. Furthermore, aligning us with global trends while catering to the changing viewing habits of our audience,” says Hernandez.
Also in this issue of Interface, we hear from eflow about new approaches to Regtech; get the lowdown on bridging the AI skills gap from CI&T; and GCX on the best ways to navigate changing cybersecurity regulations.
We chatted with Johan-Peter Teppala from Sievo about why procurement needs to use technology wisely.
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When CPOstrategy attended the DPW NYC Summit back in June, one of the buzzwords of the day was trends. Trends in procurement, trends in technology, and how to combine the two. The event was filled with productive discussions around how procurement can benefit from data and advanced technology. This led to a hopeful vibe throughout the day, despite and because of acknowledgements of procurement’s shortfalls.
We caught up with Johan-Peter Teppala, Chief Customer Officer of Sievo, at the NYC conference. For Teppala, that hopefulness is something he took away from the event. “It is great to see so many companies out there with keen interest in adopting new securities and technologies,” he says. “Procurement has increasing demand to do more with less, which explains also the need for technology to drive efficiency and to deliver more. I think it’s just inertia that’s slowing us down.”
However, advanced technology is helping shift the inertia that’s so prevalent across procurement. “Developments in GenAI have been exceptionally fast, especially recently,” Teppala adds. “With an increasing amount of practical Gen AI use cases, this has become a topic that touches each and everyone in procurement. At Sievo, we are dedicating R&D budgets to AI innovations. We have quickly been able to ramp up many practical use cases for our clients to deliver business value in this area.”
Using data and technology wisely
Teppala continues: “Sievo strives to withhold our position as the leading Procurement Analytics partner for large enterprises. We are driven by the goal to close the data-to-action gap. We believe analytics alone has zero value, it’s the actions that we take that drive the value.” This was a topic that was repeated several times during the DPW NYC Summit.
“As a result, SIevo’s goal is to ensure our customers can use their time most efficiently. We help them make business-impacting decisions and best use their expertise, whilst Sievo automatically surfaces insights that they can take action on. First and foremost, our work is about carving out insights. And once you have those insights, how do you automate those actions to create opportunities? That’s definitely one thing we’re keen to solve.”
Sievo is also focusing its attention on gen AI – how it can be adopted and what the use cases are. “AI for data cleansing has been around for a while,” says Teppala. “Right now, Gen AI is getting really good traction from a technology point of view. It’s not just insights, but adopting AI into chat interfaces, and reaping the benefits with implementable actions. It’s amazing.”
The changing talent landscape
The increased adoption of AI is going to also change the talent landscape within procurement. Another heavily-discussed topic during DPW NYC was the talent shortage and how it has the potential to slow procurement down. However, advanced technology may be the thing that accelerates it once again.
“The talent you need is changing,” says Teppala. “The procurement mandate has widened beyond delivering cost savings. Now, it’s also about driving sustainability initiatives, emission reductions, increasing diverse spending, and preventing supply chain risks. Procurement has to be creative and resource-effective for reaching ideal outcomes. This is a big challenge but also a big opportunity and also impacts the talent needed in procurement.
“You don’t necessarily need to hire superstars who know everything. It’s about teamwork. Building a procurement team out of people who possess all these modern talents, who can support each other. I can’t know whether this is going to solve the talent shortage, but at least we’re shifting towards a different kind of talent as capabilities change.
Teppala concludes: “We need to be thinking more about what kind of team we actually want to build – not just what kind of really good, talented individual we can find.”
Expert analysis of the tech trends set to make waves this year
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Digital transformation is a continuing journey of change with no set final destination. This makes predicting tomorrow a challenge when no one has a crystal ball to hand.
After a difficult few years for most businesses following a disruptive pandemic and now battling a cost-of-living crisis, many enterprises are increasingly leveraging new types of technology to gain an edge in a disruptive world.
With this in mind, here are what experts predict for the next 12 months…
1. Process Mining
Sam Attias, Director of Product Marketing at Celonis, expects to see a rise in the adoption of process mining as it evolves to incorporate automation capabilities. He says process mining has traditionally been “a data science done in isolation” which helps companies identify hidden inefficiencies by extracting data and visually representing it.
“It is now evolving to become more prescriptive than descriptive and will empower businesses to simulate new methods and processes in order to estimate success and error rates, as well as recommend actions before issues actually occur,” says Attias. “It will fix inefficiencies in real-time through automation and execution management.”
2. The evolution of social robots
Gabriel Aguiar Noury, Robotics Product Manager at Canonical, anticipates social robots to return this year. After companies such as Sony introduced robots like Poiq, Aguiar Noury believes it “sets the stage” for a new wave of social robots.
“Powered by natural language generation models like GPT-3, robots can create new dialogue systems,” he says. “This will improve the robot’s interactivity with humans, allowing robots to answer any question.
“Social robots will also build narratives and rich personalities, making interaction with users more meaningful. GPT-3 also powers Dall-E, an image generator. Combined, these types of technologies will enable robots not only to tell but show dynamic stories.”
3. The rebirth of new data-powered business applications
Christian Kleinerman, Senior Vice President of Product at Snowflake, says there is the beginning of a “renaissance” in software development. He believes developers will bring their applications to central combined sources of data instead of the “traditional approach” of copying data into applications.
“Every single application category, whether it’s horizontal or specific to an industry vertical, will be reinvented by the emergence of new data-powered applications,” affirms Kleinerman. “This rise of data-powered applications will represent massive opportunities for all different types of developers, whether they’re working on a brand-new idea for an application and a business based on that app, or they’re looking for how to expand their existing software operations.”
4. Application development will become a two-way conversation
Adrien Treuille, Head of Streamlit at Snowflake, believes application development will become a two-way conversation between producers and consumers. It is his belief that the advent of easy-to-use low-code or no-code platforms are already “simplifying the building” and sharing of interactive applications for tech-savvy and business users.
“Based on that foundation, the next emerging shift will be a blurring of the lines between two previously distinct roles — the application producer and the consumer of that software.”
He adds that application development will become a collaborative workflow where consumers can weigh in on the work producers are doing in real-time. “Taking this one step further, we’re heading towards a future where app development platforms have mechanisms to gather app requirements from consumers before the producer has even started creating that software.”
5. The Metaverse
Paul Hardy, EMEA Innovation Officer at ServiceNow, says he expects business leaders to adopt technologies such as the metaverse in 2023. The aim of this is to help cultivate and maintain employee engagement as businesses continue working in hybrid environments, in an increasingly challenging macro environment.
“Given the current economic climate, adoption of the metaverse may be slow, but in the future, a network of 3D virtual worlds will be used to foster meaningful social connections, creating new experiences for employees and reinforcing positive culture within organisations,” he says. “Hybrid work has made employee engagement more challenging, as it can be difficult to communicate when employees are not together in the same room.
“Leaders have begun to see the benefit of hosting traditional training and development sessions using VR and AI-enhanced coaching. In the next few years, we will see more workplaces go a step beyond this, for example, offering employees the chance to earn recognition in the form of tokens they can spend in the real or virtual world, gamifying the experience.”
6. The year of ESG?
Cathy Mauzaize, Vice President, EMEA South, at ServiceNow, believes 2023 could be the year that environmental, social and corporate governance (ESG) is vital to every company’s strategy.
“Failure to engage appropriate investment in ESG strategies could plunge any organisation into a crisis,” she says. “Legislation must be respected and so must the expectations of employees, investors and your ecosystem of partners and customers.
“ESG is not just a tick box, one and done, it’s a new way of business that will see us through 2023 and beyond.”
7. Macro Trends and Redeploying Budgets for Efficiency
Ulrik Nehammer, President, EMEA at ServiceNow, says organisations are facing an incredibly complex and volatile macro environment. Nehammer explains as the world is gripped by soaring inflation, intelligent digital investments can be a huge deflationary force.
“Business leaders are already shifting investment focus to technologies that will deliver outcomes faster,” he says. “Going into 2023, technology will become increasingly central to business success – in fact, 95% of CEOs are already pursuing a digital-first strategy according to IDC’s CEO survey, as digital companies deliver revenue growth far faster than non-digital ones.”
8. Organisations will have adopted a NaaS strategy
David Hughes, Aruba’s Chief Product and Technology Officer, believes that by the end of 2023, 20% of organisations will have adopted a network-as-a-service (NaaS) strategy.
“With tightening economic conditions, IT requires flexibility in how network infrastructure is acquired, deployed, and operated to enable network teams to deliver business outcomes rather than just managing devices,” he says. “Migration to a NaaS framework enables IT to accelerate network modernisation yet stay within budget, IT resource, and schedule constraints.
“In addition, adopting a NaaS strategy will help organisations meet sustainability objectives since leading NaaS suppliers have adopted carbon-neutral and recycling manufacturing strategies.”
9. Think like a seasonal business
According to Patrick Bossman, Product Manager at MariaDB corporation, he anticipates 2023 to be the year that the ability to “scale out on command” is going to be at the fore of companies’ thoughts.
“Organisations will need the infrastructure in place to grow on command and scale back once demand lowers,” he says. “The winners in 2023 will be those who understand that all business is seasonal, and all companies need to be ready for fluctuating demand.”
10. Digital platforms need to adapt to avoid falling victim to subscription fatigue
Demed L’Her, Chief Technology Officer at DigitalRoute, suggests what the subscription market is going to look like in 2023 and how businesses can avoid falling victim to ‘subscription fatigue’. L’Her says there has been a significant drop in demand since the pandemic.
“Insider’s latest research shows that as of August, nearly a third (30%) of people reported cancelling an online subscription service in the past six months,” he reveals. “This is largely due to the rising cost of living experienced globally that is leaving households with reduced budgets for luxuries like digital subscriptions. Despite this, the subscription market is far from dead, with most people retaining some despite tightened budgets.
“However, considering the ongoing economic challenges, businesses need to consider adapting if they are to be retained by customers in the long term. The key to this is ensuring that the product adds value to the life of the customer.”
11. Waking up to browser security
Jonathan Lee, Senior Product Manager at Menlo Security, points to the web browser being the biggest attack surface and suggests the industry is “waking up” to the fact of where people spend the most time.
“Vendors are now looking at ways to add security controls directly inside the browser,” explains Lee. “Traditionally, this was done either as a separate endpoint agent or at the network edge, using a firewall or secure web gateway. The big players, Google and Microsoft, are also in on the act, providing built-in controls inside Chrome and Edge to secure at a browser level rather than the network edge.
“But browser attacks are increasing, with attackers exploiting new and old vulnerabilities, and developing new attack methods like HTML Smuggling. Remote browser isolation is becoming one of the key principles of Zero Trust security where no device or user – not even the browser – can be trusted.”
12. The year of quantum-readiness
Tim Callan, Chief Experience Officer at Sectigo, predicts that 2023 will be the year of quantum-readiness. He believes that as a result of the standardisation of new quantum-safe algorithms expected to be in place by 2024, this year will be a year of action for government bodies, technology vendors, and enterprise IT leaders to prepare for the deployment.
“In 2022, the US National Institute of Standards and Technologies (NIST) selected a set of post-quantum algorithms for the industry to standardise on as we move toward our quantum-safe future,” says Callan.
“In 2023, standards bodies like the IETF and many others must work to incorporate these algorithms into their own guidelines to enable secure functional interoperability across broad sets of software, hardware, and digital services. Providers of these hardware, software, and service products must follow the relevant guidelines as they are developed and begin preparing their technology, manufacturing, delivery, and service models to accommodate updated standards and the new algorithms.”
13. AI: fewer keywords, greater understanding
AI expert Dr Pieter Buteneers, Director of AI and Machine Learning at Sinch, expects artificial intelligence to continue to transition away from keywords and move towards an increased level of understanding.
“Language-agnostic AI, already existent within certain AI and chatbot platforms, will understand hundreds of languages — and even interchange them within a single search or conversation — because it’s not learning language like you or I would,” he says. “This advanced AI instead focuses on meaning, and attaches code to words accordingly, so language is more of a finishing touch than the crux of a conversation or search query.
“Language-agnostic AI will power stronger search results — both from external (the internet) and internal (a company database) sources — and less robotic chatbot conversations, enabling companies to lean on automation to reduce resources and strain on staff and truly trust their AI.”
14. Rise in digital twin technology in the enterprise
John Hill, CEO and Founder of Silico, recognises the growing influence digital twin technology is having in the market. Hill predicts that in the next 20 years, there will be a digital twin of every complex enterprise in the world and anticipates the next generation of decision-makers will routinely use forward-looking simulations and scenario analytics to plan and optimise their business outcomes.
“Digital twin technology is one of the fastest-growing facets of industry 4.0 and while we’re still at the dawn of digital twin technology,” he explains. “Digital twins will have huge implications for unlocking our ability to plan and manage the complex organisations so crucial for our continued economic progress and underpin the next generation of Intelligent Enterprise Automation.”
15. Broader tech security
With an exponential amount of data at companies’ fingertips, Tricentis CEO, Kevin Thompson says the need for investment in secure solutions is paramount.
“The general public has become more aware of the access companies have to their personal data, leading to the impending end of third-party cookies, and other similar restrictions on data sharing,” he explains. “However, security issues still persist. The persisting influx of new data across channels and servers introduces greater risk of infiltration by bad actors, especially for enterprise software organisations that have applications in need of consistent testing and updates. The potential for damage increases as iterations are being made with the expanding attack surface.
“Now, the reality is a matter of when, not if, your organisation will be the target of an attack. To combat this rising security concern, organisations will need to integrate security within the development process from the very beginning. Integrating security and compliance testing at the upfront will greatly reduce risk and prevent disruptions.”
16. Increased cyber resilience
Michael Adams, CISO at Zoom, expects an increased focus on cyber resilience over the next 12 months. “While protecting organisations against cyber threats will always be a core focus area for security programs, we can expect an increased focus on cyber resilience, which expands beyond protection to include recovery and continuity in the event of a cyber incident,” explains Adams.
“It’s not only investing resources in protecting against cyber threats; it’s investing in the people, processes, and technology to mitigate impact and continue operations in the event of a cyber incident.”
17. Ransomware threats
As data leaks become increasingly common place in the industry, companies face a very real threat of ransomware. Michal Salat, Threat Intelligence Director at Avast, believes the time is now for businesses to protect themselves or face recovery fees costing millions of dollars.
“Ransomware attacks themselves are already an individual’s and businesses’ nightmare. This year, we saw cybergangs threatening to publicly publish their targets’ data if a ransom isn’t paid, and we expect this trend to only grow in 2023,” says Salat. “This puts people’s personal memories at risk and poses a double risk for businesses. Both the loss of sensitive files, plus a data breach, can have severe consequences for their business and reputation.”
18. Intensified supply chain attacks
Dirk Schrader, VP of security research at Netwrix, believes supply chain attacks are set to increase in the coming year. “Modern organisations rely on complex supply chains, including small and medium businesses (SMBs) and managed service providers (MSPs),” he says.
“Adversaries will increasingly target these suppliers rather than the larger enterprises knowing that they provide a path into multiple partners and customers. To address this threat, organisations of all sizes, while conducting a risk assessment, need to take into account the vulnerabilities of all third-party software or firmware.”
19. A greater need to manage volatility
Paul Milloy, Business Consultant at Intradiem, stresses the importance of managing volatility in an ever-moving market. Milloy believes bosses can utilise data through automation to foresee potential problems before they become issues.
“No one likes surprises. Whilst Ben Franklin suggested nothing can be said to be certain, except death and taxes, businesses will want to automate as many of their processes as possible to help manage volatility in 2023,” he explains. “Data breeds intelligence, and intelligence breeds insight. Managers can use the data available from workforce automation tools to help them manage peaks and troughs better to avoid unexpected resource bottlenecks.”
20. A human AI co-pilot will still be needed
Artem Kroupenev, VP of Strategy at Augury, predicts that within the next few years, every profession will be enhanced with hybrid intelligence, and have an AI co-pilot which will operate alongside human workers to deliver more accurate and nuanced work at a much faster pace.
“These co-pilots are already being deployed with clear use cases in mind to support specific roles and operational needs, like AI-driven solutions that enable reliability engineers to ensure production uptime, safety and sustainability through predictive maintenance,” he says. “However, in 2023, we will see these co-pilots become more accurate, more trusted and more ingrained across the enterprise.
“Executives will better understand the value of AI co-pilots to make critical business decisions, and as a key competitive differentiator, and will drive faster implementation across their operations. The AI co-pilot technology will be more widespread next year, and trust and acceptance will increase as people see the benefits unfold.”
21. Building the right workplace culture
Harnessing a positive workplace culture is no easy task but in 2023 with remote and hybrid working now the norm, it brings with it new challenges. Tony McCandless, Chief Technology Officer at SS&C Blue Prism, is well aware of the role organisational culture can play in any digital transformation journey.
“Workers are the heart of an organisation, so without their buy in, no digital transformation initiative stands a chance of success,” explains McCandless. “Workers drive home business objectives, and when it comes to digital transformation, they are the ones using, implementing, and sometimes building automations. Curiosity, innovation, and the willingness to take risks are essential ingredients to transformative digitalisation.
“Businesses are increasingly recognising that their workers play an instrumental role in determining whether digitalisation initiatives are successful. Fostering the right work environment will be a key focus point for the year ahead – not only to cultivate buy-in but also to improve talent retention and acquisition, as labor supply issues are predicted to continue into 2023 and beyond.”
22. Cloud cover to soften recession concerns
Amid a cost-of-living crisis and concerns over any potential recession as a result, Daniel Thomasson, VP of Engineering and R&D at Keysight Technologies, says more companies will shift data intensive tasks to the cloud to reduce infrastructure and operational costs.
“Moving applications to the cloud will also help organisations deliver greater data-driven customer experiences,” he affirms. “For example, advanced simulation and test data management capabilities such as real-time feature extraction and encryption will enable use of a secure cloud-based data mesh that will accelerate and deepen customer insights through new algorithms operating on a richer data set. In the year ahead, expect the cloud to be a surprising boom for companies as they navigate economic uncertainty.”
23. IoT devices to scale globally
Dr Raullen Chai, CEO and Co-Founder of IoTeX, recognises a growing trend in the usage of IoT devices worldwide and believes connectivity will increase significantly.
“For decades, Big Tech has monopolised user data, but with the advent of Web3, we will see more and more businesses and smart device makers beginning to integrate blockchain for device connectivity as it enables people to also monetise their data in many different ways, including in marketing data pools, medical research pools and more,” he explains. “We will see a growth in decentralised applications that allow users to earn a modest additional revenue from everyday activities, such as walking, sleeping, riding a bike or taking the bus instead of driving, or driving safely in exchange for rewards.
“Living healthy lifestyles will also become more popular via decentralised applications for smart devices, especially smart watches and other health wearables.”
Our cover story this month investigates how Fleur Twohig, Executive Vice President, leading Personalisation & Experimentation across Consumer Data & Engagement Platforms, and her team are executing Wells Fargo’s strategy to promote personalised customer engagement across all consumer banking channels
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This month’s cover story follows Wells Fargo’s journey to deliver personalised customer engagement across all its consumer banking channels.
Welcome to the latest issue of Interface magazine!
Partnerships of all kinds are a key ingredient for organisations intent on achieving their goals… Whether that’s with customers, internal stakeholders or strategic allies across a crowded marketplace, Interface explores the route to success these relationships can help navigate.
Our cover story this month investigates the strategy behind Wells Fargo’s ongoing drive to promote personalised customer engagement across all consumer banking channels.
Fleur Twohig, Executive Vice President, leading Personalisation & Experimentation across the bank’s Consumer Data & Engagement Platforms, explains her commitment to creating a holistic approach to engaging customers in personalised one-to-one conversations that support them on their financial journeys.
“We need to be there for everyone across the spectrum – for both the good and the challenging times. Reaching that goal is a key opportunity for Wells Fargo and I have the pleasure of partnering with our cross-functional teams to help determine the strategic path forward…”
IBM: consolidating growth to drive value
We hear from Kate Woolley, General Manager of IBM Ecosystem, who reveals how the tech leader is making it easier for partners and clients to do business with IBM and succeed. “Honing our corporate strategy around open hybrid cloud and artificial intelligence (AI) and connecting partners to the technical training resources they need to co-create and drive more wins, we are transforming the IBM Ecosystem to be a growth engine for the company and its partners.”
Kate Woolley, IBM
America Televisión: bringing audiences together across platforms
Jose Hernandez, Chief Digital Officer at America Televisión, explains how Peru’s leading TV network is aggregating services to bring audiences together for omni-channel opportunities across its platforms. “Time is the currency with which our audiences pay us, so we need to be constantly improving our offering both through content and user experiences.”
Portland Public Schools: levelling the playing field through technology
Derrick Brown and Don Wolf, tech leaders at Portland Public Schools, talk about modernising the classroom, dismantling systemic racism and the power of teamwork.
Also in this issue, we hear from Lenovo on how high-performance computing (HPC) is driving AI research and report again from London Tech Week where an expert panel examined how tech, fuelled by data, is playing a critical role in solving some of the world’s hardest hitting issues, ranging from supply chain disruptions through to cybersecurity fears.
Our cover story investigates how the latest cybersecurity technologies ensure the Commonwealth Bank and its customers are protected from cybercrime
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Our cover story this month charts how the Commonwealth Bank is strengthening its cybersecurity posture to protect 16 million customers
Welcome to the latest issue of Interface magazine!
Cybersecurity, and the need to share data safely and securely, goes beyond the day to day requirements of one organisation, it’s about enterprises at all levels collaborating to develop an ecosystem for the greater global good.
Our cover star Memo Hayek, General Manager Group Cyber Transformation & Delivery at CommBank, is leading a team on such a journey while executing the technology transformation required to fortify cybersecurity for CommBank. Leveraging the latest cutting-edge technologies from partners including AWS and Palo Alto Networks – in demand as the global attack surface grows – Hayek is flying the flag for women in STEM careers and delivering the strategies to ensure the bank, its Australian community and the wider global economy are protected from cybercrime.
https://www.youtube.com/watch?v=jQNXY2duLZs
Philip Morris International
Also in this issue, we learn how Philip Morris International (PMI) is instigating a digital revolution in the travel retail sector, merging the physical and online worlds by implementing a number of CX-driven initiatives framed around PMI’s IQOS brand which is helping smokers to non-smoke products.
Valtech
We hear again from global business transformation agency Valtech on its efforts to embrace diversity across the length and breadth of its organisation to make it better able to provide solutions that touch all of society. Una Verhoeven, VP Global Technology, gives her perspective on the diversity debate and how that’s further supported in the technological evolution with the rise of composable architecture.
Digital Transformation
Elsewhere, we discover how biotech firm Debiopharm’s digital transformation journey is ushering in a new era for drug development and clinical trials. We also reveal the innovative global IT transformation plans of market-leading tile manufacturer Terreal.
Peter Ruffley, Chairman at Zizo, discusses how the promise of AI…
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The promise of AI
At present, the IT industry is doing itself no favours by promising the earth with emerging technologies, without having the ability to fully deliver them, see Hadoop’s story with big data as an example – look where that is now.
There is also a growing need to dispel some of the myths surrounding the capabilities of AI and data led applications, which often sit within the c-suite, that investment will give them the equivalent of the ship’s computer from Star Trek, or the answer to the question ‘how can I grow the business?’ As part of any AI strategy, it’s imperative that businesses, from the board down, have a true understanding of the use cases of AI and where the value lies.
If there is a clear business need and an outcome in mind then AI can be the right tool. But it won’t do everything for you – the bulk of the work still has to be done somewhere, either in the machine learning or data preparation phase.
AI ready vs. AI reality
With IoT, many organisations are chasing the mythical concept of ‘let’s have every device under management’. But why? What’s the real benefit of doing that? All they are doing is creating an overwhelming amount of low value data. They are expecting data warehouses to store a massive amount of data. If a business keeps data from a device that shows it pinged every 30 seconds rather than a minute, then that’s just keeping data for the sake of it. There’s no strategy there. The ‘everyone store everything’ mentality needs to change.
One of the main barriers to implementing AI is the challenges in the availability and preparing of data. A business cannot become data-driven, if it doesn’t understand the information it has and the concept of ‘garbage in, garbage out’ is especially true when it comes to the data used for AI.
With many organisations still on the starting blocks, or having not yet entirely finished their journey to become data driven, there appears to be misplaced assumption that they can quickly and easily leap from being in the process of preparing their data to implementing AI and ML, which realistically, won’t work. To successfully step into the world of AI, businesses need to firstly ensure the data they are using is good enough.
AI in the data centre
Over the coming years, we are going to see a tremendous investment in large scale and High-Performance Computing (HPC) being installed within organisations to support data analytics and AI. At the same time, there will be an onus on data centre providers to be able to provide these systems without necessarily understanding the infrastructure that’s required to deliver them or the software or business output needed to get value from them.
We saw this in the realm of big data, when everyone tried to swing together some kind of big data solution and it was very easy to just say we’ll use Hadoop to build this giant system. If we’re not careful, the same could happen with AI. There’s been a lot of conversations about the fact that if we were to peel back the layers of many AI solutions, we’ll find that there is still a lot of people investing a lot of hard work into them, so when it comes to automating processes, we aren’t quite in that space yet. AI solutions are currently very resource heavy.
There’s no denying that the majority of data centres are now being asked how they provide AI solutions and how they can assist organisations on their AI journey. Whilst organisations might assume that data centres will have everything to do with AI tied up. Is this really the case? Yes, there is a realisation of the benefits of AI, but actually how it is best implemented, and by who, to get the right results, hasn’t been fully decided.
Solutions to how to improve the performance of large-scale application systems are being created, whether that’s by getting better processes, better hardware or whether it’s reducing the cost to run them through improved cooling or heat exchange systems. But data centre providers have to be able to combine these infrastructure elements with a deeper understanding of business processes. This is something very few providers, as well as Managed Service Providers (MSPs) and Cloud Service Providers (CSPs) are currently doing. It’s great to have the kit and use submerged cooling systems and advanced power mechanisms but what does that give the customer? How can providers help customers understand what more can be done with their data systems?
How do providers differentiate themselves and how can they say they harness these new technologies to do something different? It’s easy to go down the route of promoting that ‘we can save you X, Y, Z’ but it means more to be able to say ‘what we can achieve with AI is..X, Y, Z‘. Data centre providers need to move away from trying to win customers over based solely on monetary terms.
Education and collaboration
When it comes to AI, there has to be an understanding of what the whole strategic vision is and looking at where value can be delivered and how a return on investment (ROI) is achieved. What needs to happen is for data centre providers to work towards educating customers on what can be done to get quick wins.
Additionally, sustainability is riding high on the business agenda and this is something providers need to take into consideration. How can the infrastructure needed for emerging technologies work better? Perhaps it’s with sharing data between the industry and working together to analyse it. In these cases, maybe the whole is greater than the sum of its parts. The hard bit is going to be convincing people to relinquish control of their data. Can the industry move the conversation on from being purely technical and around how much power and kilowatts are being used to how is this helping our social corporate responsibility/our green credentials?
There are some fascinating innovations already happening, where lessons can be learnt. In Scandinavia for example, there are those who are building carbon neutral data centres, which are completely air cooled, with the use of sustainable power cooling through solar. The cooling also comes through the building by basically opening the windows. There are also water cool data centres out there under the ocean.
Conclusion
We saw a lot of organisations and data centres jump in head first with the explosion of big data and not come out with any tangible results – we could be on the road to seeing history repeat itself. If we’re not careful, AI could just become another IT bubble.
There is still time to turn things around. As we move into a world of ever-increasing data volumes, we are constantly searching for the value hidden within low value data that is being produced by IoT, smartphone apps and at the edge. As the global costs of energy rise, and the numbers of HPC clusters powering AI to drive our next generation technologies increase, new technologies have to be found that lower the cost of running the data centre, beyond standard air cooling.
It’s great to see people thinking outside of the box on this with, with submerged HPC systems and full, naturally aerated data centres, but more will have to be done (and fast) to meet up with global data growth. The appetite for AI is undoubtedly there but for it to be able to be deployed at scale and for enterprises to see real value, ROI and new business opportunities from it, data centres need to move the conversation on, work together and individually utilise AI in the best way possible or risk losing out to the competition.
As UK businesses look towards the cloud to enable digital innovation, more than half (58%) say the move has been…
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As UK
businesses look towards the cloud to enable digital innovation, more than half
(58%) say the move has been more costly than envisaged, according to new
research from Capita’s Technology Solutions division.
However, the
research reveals that cloud migration (72%) remains the top transformational
priority for most organisations, ahead of process automation (45%), big data
analytics (40%), and artificial intelligence/machine learning (31%). This is a
further indication that organisations see cloud as a core component to
effectively enabling these next-generation technologies.
The ‘From Cloud Migration
to Digital Innovation’ report, which surveyed 200 UK IT decision
makers, cites reduced cost (61%), improved speed of delivery (57%), and
increased IT security (52%) as the main reasons for organisations to move to
the cloud. However, 90% of respondents admitted that cloud migration had been
delayed in their organisation due to one or more unforeseen factors. Issues
such as cost (39%), workload and application re-architecting (38%), security
concerns (37%), and skills shortages (35%) all point to a process that is more
complicated than expected.
“Cloud adoption is a critical foundational step towards opening up real
transformative opportunities offered by cloud-native technologies and emerging
digital platforms and services. While some forward-thinking organisations are able to keep their eye on
the goal, the complexity of the migration and application modernisation process
tends to introduce delays and cost-implications that slow down progress,” said
Wasif Afghan, head of Cloud and Platform at Capita’s Technology Solutions
division.
A more
complex and costly migration than expected
On average,
those businesses asked had migrated 45% of their workloads and applications to
the cloud. However, this did correlate to organisation size as organisations
with more than 5,000 employees have further to go, with less than a third (31%)
of workloads and applications migrated. This could be the result of having
larger, more complicated systems.
Nearly half
(43%) of respondents found security to be one of the greatest challenges they
had faced during their migration. A lack of internal skills (34%), gaining
budget approval (32%), and progressing legacy migration solutions (32%) were
other significant challenges organisations had faced.
In fact, half
of respondents found their organisation had to ‘rearchitect’ more workloads and
optimise them for the cloud than they had expected. Further, only just over a
quarter (27%) found that labour/logistical costs have decreased – a key driver
for moving to the cloud in the first place.
“Every migration journey
is unique in both its destination and starting point. While some organisations
are either ‘born in the cloud’ or can gather the resources to transform in a
relatively short space of time, the majority will have a much slower, more
complex path. Many larger organisations that have been established for a long
time will have heritage IT systems and traditional processes that can’t simply
be lifted and shifted to the cloud straight away due to commercial or technical
reasons, meaning a hybrid IT approach is often required. Many organisations
haven’t yet fully explored how they can make hybrid work for them, combining
the benefits of newer cloud services whilst operating and optimising their
heritage IT estate,” said Afghan.
A platform
for innovation
Despite some of
the challenges outlined in the report, the majority (86%) of respondents agree
that the benefits of cloud are compelling enough to outweigh its downsides. For
more than three-quarters (76%) of organisations, moving to the cloud has driven
an improvement in IT service levels, while two-thirds (67%) report that cloud
has proven more secure than on-premise.
Overall,
three-quarters of organisations claimed to be satisfied with their cloud
migrations. However, only 16% were ‘extremely satisfied’ – indicating
that most organisations have not yet seen the full benefits or transformative
potential of their cloud investments. In addition, 42% of respondents currently
believe that cloud had ‘overpromised and underdelivered’.
“It’s no longer enough to think
of cloud as simply a way to benefit from initial cost savings or just another
place to store applications and data. Today, the move to cloud is driving a
spirit of innovation right across the enterprise, paving the way for advanced
digital services to be rolled out in a highly accessible, faster and more
cost-effective way – whether that’s AI, RPA, complex data analytics or machine
learning. Only through the alignment of IT and lines of business leadership –
in terms of goals, vision, direction and mindset – can organisations fully unleash the potential of cloud to
address their key business objectives, whether that is improving business
agility, delivering an enhanced customer experience or enhancing business
efficiencies.” said Afghan.
Experts have been predicting for some time that the automation technologies that are applied in factories worldwide would be applied…
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Experts
have been predicting for some time that the automation
technologies that are applied in factories worldwide would be applied to
datacentres in the future. Not only to improve their efficiency but to help
gather business insights from ever-increasing pools of data. The truth is that
we’re rapidly advancing this possibility with the application of Robotic
Process Automation (RPA) and machine learning in the datacentre environment.
But why is this so important?
At
the centre of digital transformation is data and thus, the datacentre. As we
enter this new revolution in how businesses operate, it’s essential that every
piece of data is handled and used appropriately to optimise its value. This is
where the datacentre becomes crucial as the central repository for data. Not
only are they required to manage increasing amounts of data, more complex machines
and infrastructures, we also want them to be able to generate improved
information about our data more quickly.
In
this article, Matthew Beale, Modern Datacentre Architect at automation and
infrastructure service provider, Ultima explains how RPA and machine learning
are today paving the way for the autonomous datacentre.
The legacy datacentre
Currently,
businesses spend too much time and energy on dealing with upgrades, patches,
fixes and monitoring of their datacentres. While some may run adequately, most
suffer from three critical issues;
• Lack of consistent support, for
example, humans make errors when updating patches or maintaining networks
leading to compliance issues.
• Lack of visibility for the business,
for example, multiple IT staff look after multiple apps or different parts of
the network with little coordination of what the business needs.
• Lack of speed when it comes to
increasing capacity or migrating data or updating apps.
Human
error is by far the most significant cause of network downtime. This is
followed by hardware failures and breakdowns. With little to no oversight of
how equipment is working, action can only be taken once the downtime has
already occurred. The cost impact is much higher as the focus is taken away
from other things to manage the cause of the issue, combined with the impact of
the actual network downtime. Stability, cost and time management must be
tightened to provide a more efficient datacentre. Automation can help achieve
this.
‘Cobots’ make humans six times
more productive
Automation
provides ‘cobots’ to work alongside humans with unlimited benefits. The
precisely structured environment of the datacentre is the perfect setting to
deploy these software robots. There are many medial, repetitive and time
intensive tasks that can be taken away from users and given to a software robot
with the effect of boosting both consistency and speed.
Ultima
calculates that the productivity ratio of ‘cobot’ to human is 6:1. By reviewing
processes that are worth automating, software robots can be programmed, and
once verified, they can repeat them every time. Whatever the process is,
robotics ensure that it is consistent and accurate, meaning that every task
will be much more efficient. This empowers teams to intervene only to make
decisions in exceptional circumstances.
The self-healing datacentre
Automation
minimises the amount of time that human maintenance of the datacentre is
required. Robotics and machine learning restructures and optimises traditional
processes, meaning that humans are no longer needed to perform patches to
servers at 3 am. Issues can be identified and flagged by machines before they
occur, eliminating downtime.
Re-distribution of resources
and capacity management
As
the lifecycle of an app across the business changes, resources need to be
redeployed accordingly. With limited visibility, it’s extremely difficult, if
not impossible, for humans to distribute resources effectively without the use
of machines and robotics. For example, automation can increase or decrease
resources accordingly towards the end of an app’s life to maximise resources
elsewhere. Ongoing capacity management also evaluates resources across multiple
cloud platforms for optimised utilisation. When the workload is effectively
balanced, not only does this offer productivity cost savings, it also allows
for predictive analytics.
The art of automation
These
new, consumable automation functions are the result of what Ultima has already
been doing for the last year when it found itself solving similar problems for
three of its customers. It was moving three customers from their end of life
5.5 version of VMWare and recognised that it would be helpful to be able to
automatically migrate them to the updated version, so it developed a solution
to do this. Where once it would have taken 40 days to migrate workloads, the
business cut that in half, resulting in a 33 per cent cost saving for those
companies. It then moved on to looking at other processes to automate with the
ambition of taking its customers on a journey to full datacentre automation.
Using
discovery tools and automated scripts to capture all data required to design
and migrate infrastructure to the automated datacentre, Ultima’s infrastructure
is used as a code to create repeatable deployments, customised for customer
environments. These datacentre deployments are then able to scale where needed
without manual intervention.
The journey to a fully
automated datacentre
The first level of automation provides information for administrators to take action in a user-friendly and consumable way, moving to a system that provides recommendations for administrators to accept actions based on usage trends. From there automation leads to a system that will automatically take remediation actions and raise tickets based on smart alerts. Then you move to a fully autonomous datacentre utilising AI & ML, which determines the appropriate steps and can self-learn and adjust thresholds.
AI-driven operations start
with automation
Businesses
are adopting modern ways of consuming applications as well as modern ways of
working. Over 80 per cent of organisations are either using or adopting DevOps
methodologies, and it is critical to the success of these initiatives that the
platforms in place can support these ways of working while still keeping
efficiency and utilisation high.
In
the not too distant future is a central platform to support traditional and
next-generation workloads which can be automated in a self-healing, optimum way
at all times. This means that when it comes to migration, maintenance,
upgrades, capacity changes, auditing, back-up and monitoring, the datacentre
takes the majority of actions itself with no or little assistance or human
intervention required. Similar to autonomous vehicles, the possibilities for
automation are never-ending; it’s always possible to continually improve
the way work is carried out.
Matthew Beale is Modern Datacentre
Architect, Ultima, an automation and transformation partner. You can contact
him at matthew.beale@ultima.com and visit Ultima at www.ultima.com
By Lee Metters, Group Business Development Director, Domino, “Get closer than ever to your customers. So close, in fact, that you…
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By Lee Metters, Group Business Development
Director, Domino,
“Get
closer than ever to your customers. So close, in fact, that you tell them what
they need well before they realise it themselves.” Steve Jobs
Every brand
aspires to get close to its customers to understand what makes them tick. Those
that succeed invariably deliver better experiences that inspire long-term
loyalty. Today, the world’s biggest brands know us so well they’re able to
personalise their marketing to match our individual tastes and behaviours. When
Netflix recommends you try Better
Call Saul, it’s because it knows you binge-watched Breaking Bad. The
personal approach works; whether it’s a Netflix notification or a ‘programmatic
playlist’ from Spotify, targeted recommendations – informed by deep learning
and vast data – hugely influence the content we stream. Steve Jobs was right:
successful brands get so close to their customers, they can tell them what they
need long before they know they need it. And we all keep coming back.
However,
not all brands are as fortunate as the digital disruptors. How do you get close
to your customer when your brand isn’t an online service that’s routinely
capturing user data? If you’re marketing a physical entity – a food, a toy, a
designer handbag or a male grooming kit – how do you even know who your
customers are (let alone what they need) when complex supply chains inevitably
separate you from your end-user? How can you add brand value when you can’t
build a direct relationship with your customer or lay the foundation for
long-term engagement? The answer is: you can. In fact, as Lee Metters, Group
Business Development Director, Domino, examines, with the advent of simple,
affordable technology, you can do it quickly, easily, and
cost-effectively.
New
opportunities
A convergence of factors is creating new opportunities for marketers to transform the way they manage their brands through the consumer lifecycle. The availability of personalised barcodes combined with the ability of smartphones to read them, has reinvented consumer behaviours, with shoppers increasingly scanning product barcodes to discover more about the brands they buy. However, until recently, the absence of standardised coding meant that brands needed to create proprietary apps to deliver their value-added features, relying on customers’ willingness to download ‘yet another app’ in a world of app fatigue.
The introduction of GS1 Digital Link barcodes, which provide a standards-based structure for barcoding data, has removed this need for product-specific apps. It’s opened up the potential for marketing innovation – such as digitally activated campaigns that can transform a product into an owned media channel – enhancing the brand experience and building stronger connections with customers. This key development has been assisted by the emergence of advanced coding and marking systems that are helping brands include more information on every product, allowing them to personalise customer experiences at speed and scale.
With
customer intimacy considered a key driver of commercial success, personalised
coding and marking can help brands achieve the Holy Grail of getting closer to
their customers. What’s more, it provides a platform for value-added innovation
that builds engagement, trust, and long-term brand loyalty. The potential
applications are exciting and wide-ranging.
Internet
of Products
Digital
innovation is not limited to online brands – practically every product can form
part of a connected and accessible online ecosystem. An internet of products.
In its simplest form, personalised barcoding can provide a gateway to online
content – user manuals, product details, blogs, communities, and customer
support – that enhances the brand experience. However, beyond the basics, the
opportunities for compelling customer engagement go much further. Leading
brands are using QR codes to trigger anything from loyalty schemes and
competitions to gamification and immersive brand experiences. Progressive
brands are using barcodes to create innovative gifting solutions – allowing
customers to record personal video messages to accompany their presents, giving
their loved ones a more memorable experience.
The
potential for innovation is significant – and the rewards are too. For example,
in Germany, Coca-Cola used barcoding on cans and bottles to engage directly
with consumers, with a simple scan connecting customers with ‘in the moment’
mobile experiences. The digitally activated campaign allowed Coca-Cola to
transform its products into an owned media channel, captivating customers with
personalised content, incentives, and competitions that generated unprecedented
brand engagement. The campaign has subsequently been rolled out across 28
markets in Europe and North America.
Provenance
and authenticity
Serialisation,
first introduced to safeguard the medicines supply chain against the plague of
counterfeit drugs, is now being widely applied across many industries –
allowing brand owners and customers to track and trace products and determine
their authenticity. This is a significant value-add in sectors like food, where
discerning consumers are increasingly interested in the provenance of produce,
and the journey foods make from farm to fork. With carbon footprint and other
environmental issues now a key influence on consumer purchases, traceability is
a major value-add across most commercial industries.
The
value of data
Barcode
innovation undoubtedly provides considerable value for consumers. With research
showing that customer experience is the most competitive battleground in
consumer markets, qualities such as transparency, social responsibility, and
open engagement are all crucial ingredients in a trusted brand experience where
personalised barcoding can help. But the value exchange isn’t all one way:
marketers benefit too.
Direct link barcodes provide a mechanism to capture a rich seam of real-time data that can help brands understand – and respond to – customers’ needs. Simple information such as user profiles, geo-location, purchase history, dates, and times can be leveraged to build a dynamic picture of individual customers, helping to inform a wide range of services and communications. This data can provide a powerful marketing platform – an organic and automated CRM – to target customers and personalise communications based on identifiable preferences and behaviours.
Marketers can understand customers’ buying cycles to trigger timely and relevant alerts. They can upsell products and accessories, nudge customers when warranties expire, or past purchases are getting old and tired. And just like Netflix, they can recommend new products that customers will love – long before they know they need them.
Cracking
the code
The
emergence of GS1 Direct Link barcodes – and the smart technologies that support
them – is transforming the retail experience, helping consumers find out more
about the products they buy and bringing brands much closer to customers. As
the High Street battles tough economic conditions and the rise of digital
disruptors, the successful brands of tomorrow will be those that exploit the
creative opportunity of personalised barcoding and deploy advanced coding and
marking systems that make the magic happen.
As location data continues to dictate customer interactions, Tableau Software redefines the data driven conversation, following the unveiling of its…
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As location data continues to dictate customer interactions, Tableau Software redefines the data driven conversation, following the unveiling of its latest next-generation mapping capabilities that will enhance how people anaylse location data.
With the general availability of Tableau 2019.2 now live, the company’s product offering allows for greater understanding of location data through mapping technology. The latest release utilises Mapbox mapping technology to implement vector maps that allow people to see more detailed location data and perform greater analysis. The newest version also includes parameter actions for more visual interactivity.
This comes at a key
time in the location data conversation, as recent reports indicate that by
2022, 30% of customer interactions will be influenced by real-time location
analysis. Tableau can now provide a more
efficient and smoother experience as well as provide far greater background
mapping layers to geospatial data, including train stations, building
footprints and terrain information.
PATH, a global health organisation that
uses Tableau and Mapbox to monitor reported cases of diseases more easily and
precisely keep tabs on communicable diseases in hot spots, will see key
benefits from these new geospatial capabilities.
“Monitoring the reported cases of diseases
like malaria will be enhanced greatly by accurately placing those cases on a
map. As visualisation tools, maps engender a sense of both place and scale.
They also instigate exploration and discovery, so decision makers can see where
diseases are emerging and make comparisons to where they have available
resources such as health facilities, drugs, diagnostics or community health
workers.” said Jeff Bernson, Vice President, Technology, Analytics and Market
Innovation at PATH. “By adding more accurate and detailed vector mapping into
our work with Tableau through initiatives like Visualize No Malaria, our
country partners can more easily and precisely keep tabs on communicable
diseases in hot spots, and get help to those who need it faster.”
Tableau 2019.2 follows the recent
introduction of its Ask Data platform. Revealed earlier this year, Ask Data
uses the power of natural language processing to enable people to ask data
questions and get an immediate visual response.
“Tableau’s
unparalleled community inspires and motivates our rapid pace of innovation.
With every release, we are working to simplify and enhance the analytics
experience so that even more people can easily ask and answer questions of
their data,” said Francois Ajenstat, Chief Product Officer at Tableau. “From
empowering new analytical creativity with parameter actions, to unlocking the
power of spatial data through a richer, more advanced mapping experience,
Tableau 2019.2 takes interactivity to the next level for our customers.”
You can find out more
information on Tableau 2019.2 and a full breakdown of its features at tableau.com/new-features
The EU’s General Data Protection Regulation (GDPR) was created with the aim of homogenising data privacy laws across the EU. GDPR also applies to organisations outside the EU, if they monitor EU data subjects, or offer goods and services to them. The GDPR applies to personal data, which is defined as any information relating to an identifiable natural person.
In certain cases, frameworks such as the EU-US Privacy Shield have been implemented to ensure the protection of data being transferred outside the EEA. However, such frameworks have not been established in all countries outside of the EEA. In such cases, businesses need to be keenly aware of the data protection laws in each territory, in order to ensure compliance.
Businesses based within the EEA that wish to send
personal data outside the EEA also need to pay particularly close attention to
GDPR. GDPR restricts the transfer of any personal data to countries outside
the EEA.
The European Commission has made “adequacy decisions” as regards the data protection regimes in certain territories. Territories, where the data protection regime has been deemed adequate, include Andorra, Argentina, Guernsey, Isle of Man, Israel, Jersey, New Zealand, Switzerland and Uruguay. The EU Commission has also made partial findings as regards the adequacy of the regimes in the US, Japan and Canada.
If a business wishes to send data to a country that is not in the EEA, and which is not covered by an “adequacy decision”, it will need to ensure that the appropriate safeguards set out in the GDPR are implemented.
In order to facilitate data transfers within
multinational corporate groups, “binding corporate rules” may be submitted to
an EEA data supervisory authority for approval. If these are approved, then all
members of the group must sign up to these rules and they then may transfer
data outside the EEA, subject to the binding corporate rules.
Another way to make a restricted transfer outside the EEA is for both parties to enter into a data-sharing agreement, which incorporates the standard data protection clauses adopted by the European Commission.
The Commission has published four sets of such model clauses, which set out the obligations of both the data exporter and data importer. The clauses may not be amended and must appear in the agreement in full. The penalties for non-compliance with GDPR are significant since organisations can be fined €20 Million or 4% of their annual global turnover for breaches.
Article 49 of GDPR also sets out derogations from the GDPR’s general prohibition on transferring personal data outside the EEA without adequate protection. The derogations can apply, for example, where there is an important public interest, or the data must be transferred for legal proceedings. A derogation can also apply where the data subject has been fully informed of the risks but has given their explicit consent to the transfer.
The advent of GDPR has significance for companies doing business internationally. However, companies doing business internationally also need to think beyond GDPR. Companies may find themselves subject to the data protection regimes of third countries, even if they do not have any physical presence there. For example, international companies without a presence in Turkey may be subject to Turkish data protection law if their activities have an effect in Turkey.
A registration system for data processors
is currently being rolled out in Turkey. Data processors based outside Turkey
whose activities have an effect in Turkey may need to register by 30 September
2019.
Turkey’s 2016 Law on the Protection of
Personal Data is based largely on EU data protection law. As a candidate state
for EU membership, Turkey aligns much of its legal system with EU law. Many of
its requirements are broadly similar to EU law. However, there are also some
very important differences which companies whose businesses have an effect in
Turkey should be mindful of.
Turkish data protection law allows for
administrative fines of up to three per cent of a company’s net annual sales to
be levied if personal data is stolen, or disclosed without consent. Turkish data protection law applies to both
sensitive and non-sensitive personal information.
Personal data may not be transferred
outside Turkey without the consent of the data subject, except in strictly
limited circumstances. Regulatory approval is required for such transfers where
the transfer may harm Turkey or the data subject.
Unlike GDPR, however, “explicit consent”
is required by Turkish Law to process both sensitive and non-sensitive data.
The exceptions to this general rule include where there is a legal obligation
on a data processor to process the data, and where such processing is necessary
to protect the life of the subject. Further processing is not allowed without
specific consent, and there is no “compatible purpose” exception in Turkish
law. The definitions of consent also differ in Turkish law and under GDPR.
GDPR has caused many EEA companies to
consider in detail the laws restricting the transfer of data out of the EEA.
However, companies may also be subject to laws restricting the transfer of data
into the EEA.
According to an Accenture study, 79% of enterprise executives agree that companies not embracing big data will lose their competitive…
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According
to an Accenture study, 79% of enterprise executives agree
that companies not embracing big data will lose their competitive edge, with a
further 83% affirming that they have pursued big data projects at some point to
stay ahead of the curve. Considering that data creation is on track to grow 10-fold by 2025, it’s crucial for
companies to be able to process it more quickly, and meaningfully.
Part
of the latest in the stream of buzzwords, “big data” gets thrown around in
business and tech circles like everyone truly understands it, but do they
really? Big data is the label for extremely large data sets that can be
analysed and provide insights around trends and patterns to influence better
business decision making.
That
may sound simple enough, and although lots of information is available about
big data technologies, few have actually mastered the art of using big data to
its full potential. In a survey undertaken by Capgemini, just 27% of executives
surveyed described their big data initiatives as ‘successful’, reinforcing that
while many are talking about it and ambitions around it, many businesses still
have much to learn
Implementing
effective, fast data processing can guarantee that your company continues to be
successful, and is only growing in importance with the diverse, and large,
amounts of data that businesses produce. While this can be seen as daunting, it
actually gives us all the ability to analyse more innovatively.
Coupled
with the growing dominance and capabilities of cloud computing, now is the
perfect time to really take a look into “big data analytics” so you too can
recognize how the power of crunching big data is bringing competitive advantage
to companies.
Big
data and cloud computing – a perfect pair
Data
processing engines and frameworks are key components in computing data within a
data system. Although there is no key difference in the definition between
“engines” and “frameworks,” it’s important to define these terms separately —
consider engines as the component responsible for operating on data
while frameworks are typically a set of components that are designed to
do the same.
Although
systems designed to handle the data lifecycle are rather complex, they
ultimately share very similar goals — to operate over data in order to broaden
understanding and surface patterns while gaining insight on complex
interactions.
In
order to do all this however, there needs to be infrastructure that supports
large workloads – and this is where cloud comes in. Clouds are considered a
beneficial tool by enterprises across the world because they have the ability
to harness business intelligence (BI) in big data. Also, the scalability of
cloud environments makes it much easier for big data tools and applications,
like Cloudera and Hadoop, to function.
Programming
frameworks available to find the right fit
Several
big data tools are available, and some of these include:
Hadoop:This
Java-based programming framework supports processing and storage of extremely
large sets of data. This is an open source framework and is part of the Apache
project, sponsored by Apache Software Foundation, which works in a distributed
computing environment. Hadoop supporting software packages and components can
be deployed by organizations in their local data centre.
Apache Spark:Apache Spark isa fast
engine used for big data processing that is capable of streaming and supporting
SQL, graph processing, and machine learning. Alternatively, Apache Storm is
also available as an open-source data processing system.
Cloudera Distributions: This is considered one of the
latest open-source technologies available to discover, store, process, model,
and serve large amounts of data. Apache Hadoop is considered part of this
platform.
Hadoop
on CloudStack to Crunch Data Effectively
Hadoop,
which is modelled after Google’s MapReduce and File System technologies, has
gained widespread adoption in the industry. This framework is similar to
CloudStack and is implemented in Java.
As
the first ever cloud platform in the industry to join the Apache Software
Foundation, CloudStack has quickly become the logical cloud choice for
organisations that prefer open-source options for their cloud and big data
infrastructure.
The combination of Hadoop and
CloudStack is truly a brilliant match made in the clouds. Considering the
availability of big data tools like these, working in the cloud to leverage
meaningful BI, now is really the perfect time to harness the power of big data
to truly drive your business forward.
Lesley Holmes Data Protection Officer at leading HR and payroll provider MHR gives a valuable insight into the future of…
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Lesley Holmes Data Protection Officer at leading HR and payroll
provider MHR gives a valuable insight into the future of technology
and how the axis of power may sway towards tech leaders.
A phrase I hear a
lot is that ‘data is the new oil’, in reference to data as an extremely
valuable commodity, which is increasing in value year by year and may well one
day have a similar value to fossil fuels.
If data is the new
oil, then the people controlling the data must be the new oil barons, maybe
even becoming even more powerful than individual oil barons at some point in
the future, as they are not tied to set geographical areas for ‘mining’ and
will never run out of new data.
Oil prices in the
global marketplace are controlled by a handful of people, yet the decisions
they make have a huge impact on world economies, so the power of data might
just create a similar group of digital oligarchs.
I feel that the
use of ‘data-mining’ by these individuals can be used in several ways:
For the public benefit.
For the benefit of a particular organisation using
its own collated data.
For the purposes of monetisation or to influence
outcomes through targeted marketing.
Public Benefit.
Most people
understand that data can benefit us all in various ways, like anti-terrorism
work and to detect other crimes, through the use of statistics, or using CCTV
footage to log crimes.
Governments also
collate data from both public and private sources to help plan public services
better and prevent economic, social and environmental issues, by identifying
data trends.
Data can also be
used for things like medical research, or to gauge public opinion and is often
done by public bodies with the public interest at heart, so data isn’t used
directly for profit; the research is done to benefit us all.
An organisation
using its own collateral.
Organisations can
gather their own data, in accordance with their privacy notice, which will make
clear what they are doing and why (in most cases anyway!).
They use this data
to improve the services they offer, work out the effectiveness of their
marketing and plan their workforce; not to mention informing strategies for
performance and profitability.
Data also has
specific uses, like assessing actuarial risk in the insurance industry, with
the aim of providing a better service based on strong data, so we get a better
quote if we are low risk customers, so there are many positives to gathering
data.
Aside from using
data to assist customers, organisations can use data they hold on their own
employees for purposes which help the business, like monitoring performance
trends, absence management and workforce optimisation.
Besides the
obvious benefit of using company data to build a better business, organisations
over a certain size are required to produce reports for the government. An
obvious example of this in recent memory, was the introduction of Gender Pay
Gap reporting, part of a wider investigation into equal pay in the UK, taking
personal data and anonymising it for reporting purposes. There is debate over
whether this data might be misused and encroach on personal freedom, but that’s
a discussion for another day…
For the purposes
of monetisation.
In the last year
there has been a huge list of articles written which illustrate the risks of
big data when misused, most notably the Facebook/Cambridge Analytica data
breach, but this isn’t an isolated event. Just like the oil barons discussed at
the beginning of this article, many other companies are extracting and refining
your personal data like oil for massive profits.
Data is already
taking a sinister turn.
Hidden cameras are
now being used which implement facial detection software to establish which adverts
shoppers like best. As they walk through shopping centres, the cameras gauge
the reaction to each advert, changing these when the reaction is a negative
expression.
While this seems
like a great advance in technology, there is an issue.
These technologies
use facial detection (capturing a blurry image), rather than true facial
recognition, but the quality of data is sufficient to distinguish gender with
90% accuracy, age to within five years and mood range (from very happy to very
unhappy) to around 80% accuracy. In many countries this happens without
consent, or even customer knowledge, which is a worrying trend.
This shows the
world is changing.
The recent
discussion around facial recognition technologies suggest these will be
exploited further to enhance the customer experience. This will come through
utilisation of ATM identity verification and hotel check-in processes, designed
to increase customer satisfaction while reducing employee demand.
Behind the scenes,
data-sets are manipulated and combined to identify trends, forecast spending
patterns, and other activities which lead to profits; including the use of
personal data for commercial purposes – such as drug trials by companies hoping
to create expensive products from the data they gather.
Facebook of course
allowed an app to harvest millions of data items to target content which may
have created political sway, which demonstrates the power of the tech companies
to influence political and social outcomes. There is much speculation
about how harvested data has been used in the political environment and who
knows? We may ourselves have been influenced by such data.
For the prevention
and detection of crime.
Data, personal and
otherwise, has been used for years to help prevent and detect crime. The use of
forensic techniques started in China in the 700’s when fingerprints were
starting to be used, but the most significant breakthroughs came in the last
century with the creation of dedicated teams to deal with this area of
investigation.
Now the Chinese
again lead the way with facial recognition being used to identify and capture
criminals as they move around the major cities. With the largest number of CCTV
cameras, China is probably embracing the technology for more than just
policing.
So what are the
dangers?
What’s clear is
that these ‘data barons’ can use the data for good, but they will be (and
perhaps already are) so powerful that anything other than the most scrupulous
data usage has the potential for disastrous societal issues.
Objection to overzealous
state control has resulted in everything from strongly worded literature to
violent protests, but at least governments can be held accountable, and we know
who’s in charge.
The clandestine
nature of the internet means that some of the most powerful public figures in
future will not be public at all, just pulling the strings through the
data-wells they possess.
What’s clear is
that we need to establish a way of controlling the use of data, or we lose
control of everything else.
By Johnny Carpenter, Director of Sales EMEA, iland If you serve on the board of a UK organisation, it’s likely…
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By Johnny Carpenter, Director of Sales EMEA, iland
If you serve on the board of a UK organisation,
it’s likely that digital transformation is high on your agenda as you look
strategically at futureproofing your business. A key part of that is ensuring
that the IT infrastructure supporting your company is functioning robustly as a
platform on which to build competitiveness, rather than a legacy anchor holding
back innovation and growth. Moving to an Infrastructure-as-a-Service (IAAS)
set-up is increasingly the way that companies aim to unlock potential and
enable more dynamic, flexible business processes.
The benefits of IAAS are clear: It’s flexible
and can easily scale as your business grows. It removes the burden of
maintaining legacy systems and allows the easy deployment of new technology
and, ideally, you only pay for what you use on a predictable opex basis; you won’t
be paying to maintain capacity that is rarely needed. It also allows you to add
on services such as analytics and disaster recovery-as-a-service and it’s the
perfect environment for the big data projects requiring large workloads and
integration with business intelligence tools.
All these drivers mean that boards can be under
pressure to quickly sign off on cloud migration projects. However, it could be
a case of more haste, less speed if boards don’t ask the right questions before
they sign on the dotted line. It’s important that decision makers don’t simply
view IAAS as a commodity purchase – there are a range of providers from
hyperscalers to vertical sector specialists and they’re not all the same.
Boards must undertake due diligence when making the IAAS decision and there are
some key questions that should be asked to ensure that the project delivers
both the operational and also the strategic outcomes required.
What’s the scale of our ambition and what
business outcomes do we want to see?
We tend to see cloud migration projects falling
into one of two camps. In the first, businesses simply want to “lift and shift”
their current operations and replicate them exactly in a cloud environment.
Naturally they want to see the benefits of cost and flexibility, but
fundamentally they want a similar experience after the migration to what they
had before. The second scenario sees companies wanting to fully overhaul their
infrastructure and deliver a completely different model back to the business –
more of a true digital transformation.
It’s important to know which camp you’re in and
be sure that your prospective IAAS provider is aligned, because in either case,
ending up with the alternative scenario will cause pain. What should be a
straightforward process becomes overly complicated when the destination is not
clear from the outset.
How much support do we require at
onboarding and ongoing?
Support for the initial cloud migration varies
between providers from do-it-yourself to a full concierge migration service.
If you opt for a hyperscale provider, you’ll
find the approach is more on the DIY side – there are a wealth of options but
it’s up to you to figure out what’s best for your business and mix and match
accordingly. This works if you have in-house capability or are happy to employ
consultancy expertise in order to manage the move.
At the other end of the scale are providers
offering an end-to-end concierge service to get you up and running with
onboarding, deployment and testing. Your IT team will be expected to bring
their existing skillsets, but little additional learning is required.
In both cases, you also need visibility of the
ongoing costs associated with support for your cloud environment and the
availability of that support.
What are our security and compliance
requirements and how will they be managed in the cloud?
Managing risk is a significant board
responsibility that only increases as regulations tighten. Company data is one
of the most high-risk assets the business possesses and its safety in the cloud
has to be beyond reproach. Prospective CSPs should be able to provide
assurances of the security offered by their cloud that meet or ideally exceed
the organisation’s compliance requirements.
Assurance at the start is one thing, but
ongoing auditing and reporting is also critical. The GDPR, for example,
requires that organisations demonstrate how they are taking steps to protect
data on a continuous basis and you’ll need to work with your CSP to achieve
this.
Again, offerings differ. Some providers will
expect you to take responsibility yourself, bringing your own security and
compliance team, software and processes with you. Others, including iland, have
built a dedicated practice around compliance that is at the disposal of
customers. This can be invaluable if your compliance team is small or you don’t
have in-house support. Either way, it’s another important consideration when
adopting IAAS.
Pricing – How flexible is flexible?
The lure of only paying for the resources
you use is a powerful motive for moving to IAAS. Whichever provider you choose,
it is likely to be more cost-effective than your legacy environment, but to
really reap the full economic benefits, you need to ensure that there’s a good
match between cloud workloads and cloud resource utilisation.
Some providers will allow you to reserve cloud
resources based on exactly the amount of GB required, with billing based on
actual compute usage, while other work on a “best fit” basis, offering a range
of predetermined instance sizes. There is a risk here of paying for resources
you don’t use, so it’s important to check that your requirements are close to
the instance size selected. You also need to ensure that you understand the
billing system and have visibility over any additional costs such as VPNs or
burstable charges that might be incurred. You certainly don’t want any nasty
surprises further down the line.
Fundamentally, adopting
infrastructure-as-a-service is a sound decision, but it still needs careful
scrutiny to make sure the business gains the maximum benefits possible. Even
though boards are under pressure to sign off deals, they should ask the right
questions to make sure their investment delivers the business outcomes they’re
looking for.
