A field name cannot carry all meaning
Availability, maintenance and settlement states for the same asset support different decisions. Common names do not guarantee common meaning; conditions of use and update timing matter too.
Energy data products and business semantics.
The data advantage is whether someone else can use it correctly.
Low confidence in extracting value from unstructured data points to a wider gap: accumulating information does not establish an organisation’s ability to use it. We believe data governance will increasingly be judged at the point of consumption, not in the catalogue.
A SIGNAL WORTH EXAMININGOnly 26%
of surveyed data leaders are confident they can create business value from unstructured data2
2025 · 1,700 senior data and analytics leaders globallyTHE ZHICE INTERPRETATION
The gap between stored data and usable knowledge is an operating problem.
Energy businesses hold equipment records, procedures, contracts and expert knowledge. Connecting them to a model does not make their value available automatically. In our view, the next priority is to deliver business meaning, versions, conditions of use and ownership together. Data products that support a real decision deserve more attention than expanding catalogues and storage.
THE ARGUMENT BEHIND THE VIEW
A judgement needs a complete argument.
The data advantage is whether someone else can use it correctly.
THE BUSINESS LOGIC OF TECHNOLOGY
THE REASONINGThe business conditions behind the view
Reuse needs predictable accountability
Teams depend on shared data when they know who explains exceptions and when they will be resolved. Clear ownership allows data services to replace repeated preparation and reconciliation.
Value accumulates across the consumption chain
A product connecting operations, trading and investment may create value by reducing the cost of interpretation, not merely by producing a report faster.
IMPLICATIONS FOR LEADERS
What should leaders reconsider?
Organise products around decisions
Define a data product through a concrete question about returns, risk or maintenance. Include definitions, versions and limits instead of simply renaming a database.
Connect supply and demand ownership
Providers maintain dependable supply; consumers explain suitability and business effects. They should jointly decide quality and timeliness investment.
Measure the effect of reuse
Track the explanation needed for a new consumer, waiting caused by exceptions and repeated judgements avoided. Download counts offer only a partial signal.
WHEN TO RECONSIDER
Not all data merits a shared product. Infrequent, highly contextual or ambiguously owned information needs evidence of reuse value and clear boundaries first.
Can another team understand this data, recognise its limits and take responsibility for how it is used?
FROM PERSPECTIVE TO PRACTICEPut this perspective to work.
ExpandCollapse
Put this perspective to work.
The same asset may have different identifiers in production, finance and trading systems. Availability and settled-energy measures may use different boundaries. Aggregation can produce apparently complete but incomparable reports and pass those ambiguities into AI applications.
Start with a real consumer
Start with a task such as operating analysis or trading settlement. Identify the minimum data, definitions and update cadence needed for the decision before expanding the catalogue.
Co-design the data product
Agree asset identifiers, time granularity and formulas with business owners. Establish contracts, quality rules, lineage and access, with owners and resolution paths for exceptions.
Validate use and reuse
Validate results and interpretation on real business samples, then test reuse with a second consumer. Track consumption, issues and version changes over time.
Work products
Business semantics and critical-metric dictionary
Data-product cards, contracts and quality rules
Ownership model, service catalogue and operating dashboard
What to measure
- Time to first usable data
- Time from an authorised, agreed requirement to data that passes its quality checks.
- Critical-rule pass rate
- Measure critical-rule outcomes by product and version; do not hide severe defects within an all-field average.
Productising data does not establish an asset valuation or permission for external trading. Sensitive-data uses require separate validation of applicable conditions.
Data notes and research foundationsExpandCollapse
- 1IEA · Key Questions on Energy and AI, 2026
Central projection for all global data centres, not AI alone or a capacity plan for an individual enterprise.
IEA 2026 · Global data centres · 2030 projection - 2IBM Institute for Business Value · 2025 CDO Study
Self-reported confidence across 27 geographies and 19 industries; not an energy-sector adoption rate or observed project success rate.
IBM 2025 · 1,700 senior data and analytics leaders globally - 3DORA · 2024 Report Infographic
The study uses a broad platform definition; associations with productivity, throughput and stability are mixed.
DORA 2024 · Cross-industry software-delivery survey
THE NEXT CONVERSATIONThe question worth discussing:
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