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      Why better data cannot wait

      Predicting the unpredictable - that’s what energy leaders are increasingly being asked to do.

      Geopolitical shocks, changing trade flows, supply constraints and the ongoing energy transition can alter prices, margins and operational priorities rapidly, reducing the reliability of long-term plans built on outdated assumptions.

      And the implications are not identical across the sector. Upstream operators, downstream businesses and utilities each face different commercial pressures, regulatory obligations and operational realities. They also rely on different combinations of operational, customer, market and third-party data. However, the common challenge is the need to interpret change quickly and translate it into action.

      Many energy organisations remain constrained by fragmented operating models, legacy systems and slow decision-making processes. Where markets can pivot overnight, competitive advantage increasingly depends on the ability to understand what has changed, model the implications and act quickly.

      There is no single, or easy, answer to this challenge. But as AI opens new possibilities, better data has become a critical lever that can strengthen the impact of the other commercial, operational and strategic levers available to leaders. Trusted enterprise data provides the foundation, while experienced people and clear accountability, supported by AI, can accelerate how quickly it is interpreted and converted into decisions. The case for better data is not new; what has changed is the urgency and the opportunity to turn it into faster, more confident decision-making.

      So, what might the vision for a data-enabled, AI-powered energy business look like? And how can it be achieved?

      Nick Whitfeld

      Partner, Data and Analytics

      KPMG in the UK

      The bottleneck: fragmented data means slower decisions

      For many energy organisations, the main barrier to a rapid response is not ambition or expertise but enterprise fragmentation. Leaders may have abundant operational, commercial, customer and regulatory data, yet are unable to combine it reliably into a single, actionable view. Decades of legacy infrastructure, business unit autonomy and separation between operational technology (OT) and information technology (IT) have created isolated and inconsistent data sets that undermine the pace and quality of decision-making.

      The real cost of fragmented data is decision latency.

      When market conditions change, leaders need to answer apparently simple questions quickly: What is the impact on margin? Which production, trading, logistics or customer decisions should change? Where should scarce capacity or capital be deployed?

      In many organisations, answering those questions still requires teams to reconcile different data sets, definitions and assumptions across functions and business units. By the time decisions are made, opportunities fade, margins are eroded and capital allocation delayed.

      So what is causing this friction?

      • Operational silos:

        Autonomous business units using disconnected systems and inconsistent assumptions, preventing real-time commercial alignment between upstream production scheduling, midstream transport commitments and downstream refining yield optimisation. In simple terms, the left hand does not always know what the right hand is doing.

      • Legacy system entrenchment:

        Customised IT and OT architectures lacking interoperability, trapping operational data in proprietary systems.

      • Ungoverned data islands:

        Inconsistent definitions, units of measure, ownership and data quality turning basic executive analysis into weeks of manual reconciliation and clunky, inefficient processes.

      • Fragmented external insight:

        Customer, market, regulatory and third-party data often sitting outside core operational systems, making it difficult to build a complete picture of risk, opportunity and performance.

      Executives cannot predict geopolitical shocks. They can, however, master their enterprise data, data architecture and data management processes to increase decision-making pace and confidence.

      What could the future look like?

      How should senior leaders turn data into a strategic decision-making asset?

      First, consider what a future capability designed to support quick, confident decisions might look like. Imagine a scenario where an energy organisation had available to it an “enterprise decision engine” that helps leaders maximise value from available product flows, assets and capacity by:

      • Bringing together operational, commercial, customer, regulatory and relevant third-party data
      • Showing the current state of operations across the relevant value chain
      • Continuously modelling future scenarios and flagging those requiring human attention
      • Providing a retrospective view of operational losses and their value, creating opportunities to learn

      This capability would enable leaders to make timely operational decisions supported by analytics, scenario modelling, linear programming and human judgement. Four ingredients are essential:

      Trust:

      Business units need to share data and, at times, accept that maximising enterprise value may reduce local performance. Performance measures and value-management disciplines must reflect that trade-off. Governance should define permitted data use and controls, helping organisations balance enterprise optimisation with customer, regulatory and joint-venture obligations.

      A central reporting and analytics capability:

      Many energy organisations have already invested in cloud data platforms. The priority is to focus investment on a strategic platform and extend it with AI-enabled modelling, analysis and governance.

      High-quality data:

      Shared data must be accurate, consistently defined and standardised so it can be combined. In asset-intensive industries, focused remediation and clear ownership may be needed to make priority data fit for use.

      People and accountability:

      Technology alone will not change decisions. Leaders need people with the operational expertise to interpret outputs, challenge assumptions and act, supported by clear ownership for data quality and value realisation.

      Data as the controllable lever and value engine

      So how to translate this theory into reality? For starters, a common, governed and real-time enterprise data capability can be one of the most valuable levers available to an energy leader. It can improve process efficiency, strengthen scenario planning and help organisations identify and protect value.

      This has not been achieved consistently before because the barriers are not purely technical. Legacy estates, competing business-unit incentives, unclear ownership, regulatory constraints and underinvestment in data maintenance have all slowed progress. AI increases the potential value of connected, high-quality data, but it also makes the consequences of weak data and governance more significant.

      Moving from silos to an agile, data-driven enterprise requires a disciplined approach that delivers early value while building towards broader transformation. Five leadership actions can help organisations make progress:

      Start with the decisions that matter:
      Start with the decisions that matter:
      Identify the high-value decisions where greater speed, confidence or foresight would make the biggest difference. Define the outcome and how value will be measured before determining what data or technology is required.
      Build the minimum trusted data foundation:
      Build the minimum trusted data foundation:

      Focus first on the data needed to support those priority decisions. Improve quality incrementally, standardising critical data, removing duplication and establishing clear ownership at source rather than attempting large-scale remediation all at once.

      Bring the right data together:
      Bring the right data together:

      Connect the operational, commercial, customer, regulatory and relevant third-party data needed to create a more complete view. Clear governance and data-sharing arrangements across business units and joint ventures are essential to making this work at enterprise level.

      Use AI to accelerate insight and action:
      Use AI to accelerate insight and action:

      Apply AI and advanced analytics where they can shorten the journey from data to decision, from identifying anomalies and modelling scenarios to directing human attention towards the issues that matter most. This should build on a governed enterprise data platform rather than create another layer of fragmentation.

      Measure outcomes and scale what works:
      Measure outcomes and scale what works:

      Track whether use cases are improving decision speed, operational performance or enterprise value. Use clear owners, baselines and measures to demonstrate impact, learn from early use cases and prioritise where to scale next.

      Start with the decisions that matter:

      Start with the decisions that matter:
      Identify the high-value decisions where greater speed, confidence or foresight would make the biggest difference. Define the outcome and how value will be measured before determining what data or technology is required.

      Build the minimum trusted data foundation:

      Build the minimum trusted data foundation:

      Focus first on the data needed to support those priority decisions. Improve quality incrementally, standardising critical data, removing duplication and establishing clear ownership at source rather than attempting large-scale remediation all at once.

      Bring the right data together:

      Bring the right data together:

      Connect the operational, commercial, customer, regulatory and relevant third-party data needed to create a more complete view. Clear governance and data-sharing arrangements across business units and joint ventures are essential to making this work at enterprise level.

      Use AI to accelerate insight and action:

      Use AI to accelerate insight and action:

      Apply AI and advanced analytics where they can shorten the journey from data to decision, from identifying anomalies and modelling scenarios to directing human attention towards the issues that matter most. This should build on a governed enterprise data platform rather than create another layer of fragmentation.

      Measure outcomes and scale what works:

      Measure outcomes and scale what works:

      Track whether use cases are improving decision speed, operational performance or enterprise value. Use clear owners, baselines and measures to demonstrate impact, learn from early use cases and prioritise where to scale next.

      The organisations that outperform in volatile markets will not necessarily be those that predict the future most accurately. They will be those that recognise change earlier, understand its implications faster and mobilise the enterprise more decisively.

      That is the real prize from better data and AI. Not more information, more dashboards or more sophisticated technology, but a fundamentally shorter distance between signal and action.

      Leaders cannot control volatility but they can control the responsiveness of the enterprise they lead.

      Better data, faster decisions

      How energy leaders can connect fragmented data and use AI to respond faster to market volatility 

      THE QUESTION: When market conditions change quickly, how long does it take your organisation to understand the implications and act?

      Why it matters: Energy leaders cannot control geopolitical volatility, changing demand or market disruption. What they can control is the responsiveness of their organisation. Yet fragmented operating models, legacy systems and inconsistent data can leave teams reconciling information while market conditions continue to move.

      The real cost is decision latency: the gap between recognising change, understanding its implications and taking action. Trusted data, experienced people and clear accountability, supported by AI, can help close that gap.

      Three facts leaders should know

      1. Fragmented data creates decision latency

      When information, definitions and assumptions are disconnected, the market can move faster than the enterprise can build a common view.

      2. AI raises the value of trusted data

      AI can model scenarios, detect anomalies and focus human attention, but poor data and weak governance undermine the result.

      3. The value is in connecting data, not simply collecting it

      Operational, commercial, customer, regulatory and third-party data often sit across different systems. Connecting the right data around priority decisions is what turns information into actionable insight.

      What leaders should do now

      • Start with priority decisions:

        Focus on high-value operational and commercial use cases, and define owners, baselines and value measures from the outset.

      • Build trust into the operating model:

        Set clear ownership, enterprise standards and data-sharing agreements across business units and joint ventures.

      • Improve data and AI capability together:

        Strengthen priority data in phases and develop a governed platform for analysis, anomaly detection and scenario modelling.

      The takeaways

      • Treat data as a business asset, not an IT by-product.
      • Connect data, AI and human judgement rather than investing in technology alone.
      • Measure success through faster, higher-quality decisions and value realised.

      The bottom line

      Leaders cannot remove volatility from energy markets, but they can build organisations that respond with greater pace and confidence. Those that connect trusted data, AI-enabled insight and experienced people will be better placed to protect value, act on opportunity and differentiate themselves.

      Our energy insights

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