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.
DATA THAT CAN BE USED
Data governance and data products.
The gap between stored data and usable knowledge is an operating problem.
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.
TWO PERSPECTIVES. ONE BUSINESS QUESTION.
Explore the choices behind the judgement.
01Energy data products and business semanticsThe data advantage is whether someone else can use it correctly.
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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.
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.
WHAT THIS MEANS FOR THE BUSINESSConnect the view to resources, ownership and action
01Organise products around decisionsRead the choiceCollapse
Define a data product through a concrete question about returns, risk or maintenance. Include definitions, versions and limits instead of simply renaming a database.
02Connect supply and demand ownershipRead the choiceCollapse
Providers maintain dependable supply; consumers explain suitability and business effects. They should jointly decide quality and timeliness investment.
03Measure the effect of reuseRead the choiceCollapse
Track the explanation needed for a new consumer, waiting caused by exceptions and repeated judgements avoided. Download counts offer only a partial signal.
What would make us revise this judgement?
Not all data merits a shared product. Infrequent, highly contextual or ambiguously owned information needs evidence of reuse value and clear boundaries first.
A QUESTION FOR THE NEXT BUSINESS DISCUSSION
Can another team understand this data, recognise its limits and take responsibility for how it is used?
Energy data products and business semanticsFull perspective and practical approach
02Trusted knowledge and data readiness for AIAI readiness depends on whether knowledge can be cited in context.
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AI readiness depends on whether knowledge can be cited in context.
Unstructured content is often treated as model input. In energy operations, however, knowledge has effective dates, equipment scope and accountability. We believe readiness depends less on the volume imported than on whether a system can tell when a passage applies.
AI readiness depends on whether knowledge can be cited in context.
THE BUSINESS LOGIC OF TECHNOLOGY
THE REASONINGThe business conditions behind the view
Versions change business meaning
Documents with the same name may concern different equipment, regions or dates. Similarity retrieval cannot establish applicability; the newest version may not explain a historical event.
Permissions shape knowledge flow
Content a model can read should not automatically become an answer every user can receive. Knowledge services must inherit business permissions and recognise when confirmation is required.
Updates determine lasting reliability
A complete launch-time repository does not remain trustworthy by itself. Revised procedures, equipment changes and expert corrections must enter an update chain.
WHAT THIS MEANS FOR THE BUSINESSConnect the view to resources, ownership and action
01Prioritise consequential knowledgeRead the choiceCollapse
For safety, contractual and important operating judgements, establish authoritative sources, applicability and version relationships first. Lower-consequence materials can warrant lighter preparation.
02Carry uncertainty into the answerRead the choiceCollapse
Conflicting sources, missing conditions or insufficient permission should trigger clarification, explicit limits or human review rather than a forced complete narrative.
03Treat corrections as knowledge assetsRead the choiceCollapse
Distinguish retrieval failures, semantic confusion and outdated information. Use operational corrections to improve knowledge structure rather than relying on model tuning alone.
What would make us revise this judgement?
Open-ended, low-consequence creative tasks do not require the same knowledge controls as production procedures. Preparation should match the consequences of use.
A QUESTION FOR THE NEXT BUSINESS DISCUSSION
When two documents disagree, can the system explain which applies and why?
Trusted knowledge and data readiness for AIFull perspective and practical approach
Three turning points to watch in digital transformation.
We watch where the constraint moves after a new capability enters the business. Better technical indicators are a starting point; sustained advantage depends on changes in resources, accountability and coordination.
- 01
Compute shifts from central buying to adaptable placement
What we watch
Track movable workloads, edge connectivity and whether compute and power conditions are considered together.
How it changes the judgement
If inference becomes easier to distribute, identity, versioning and operating consistency become more important. Weak field connectivity keeps autonomy and recovery at the front of the agenda.
- 02
Data is consumed against service commitments
What we watch
Watch whether business teams rely on shared data for real decisions and whether quality exceptions have an accountable owner.
How it changes the judgement
Growing reuse with less coordination supports expansion. Repeated interpretation at every use points to unresolved semantics and responsibility.
- 03
Software delivery follows the rhythm of the business
What we watch
Track time from need to use, recovery after failed changes and business waiting caused by platform upgrades.
How it changes the judgement
Shared capability merits further investment when it reduces waiting. Dependencies amplified by centralisation require a fresh boundary between sharing and autonomy.
A VIEW THAT CAN BE PUT TO WORK
The ZHICE Digital Capability MethodExplore the framework and implementationA perspective must stand up in practice.
Five stages—value definition, capability design, connected data, phased delivery and value management—bring planning, delivery and operations onto one roadmap. Business and technology teams make decisions together. The approach draws on MIT CISR research on digital business transformation, DORA findings on organizational and technical collaboration, and public specifications for data interoperability and OT security. ZHICE translates these perspectives into work designs for energy businesses; the business’s actual constraints determine implementation priorities.
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:
Contact ZHICE