Meeting Aims:

1. Summarise Q4 outcomes
2. Gauge sentiment towards outcomes and expectations on members
3. Reflecting on the year so far to define the right ambition level for 2027

Summary:

It was agreed that:

• Q3 outcomes were broadly on track, and the updated website timeline would continue through Q4 and Q1 with a target launch at Open Data Day in March.
• feedback from the session would be taken away and shared with all team members and co-chairs so that it could inform Q4 and 2027 delivery.
• recruitment for the next cohort of Change Champions will proceed through the usual channels.

It was noted that:

• the website refresh has been delayed due to additional tooling due diligence.
• the AI readiness work was expected to result mainly in smaller metadata changes rather than major schema changes.
• delivery during 2026 had been affected by changing priorities, delayed funding, and limited team capacity.
• a significant part of the meeting focused on where Stream delivers most value for members and whether the current balance between commercial work and broader open data ambitions is the right one.
  • Some members weren’t sure what to expect from their Stream membership.
• there was a question around the current model and whether the team size is sufficient to deliver the level of ambition being discussed.

It was discussed that:

• there’s a risk of losing momentum if too few new datasets are published.
• a lightweight and continuous approach to publishing datasets alongside the current use case process could be supported, to maintain momentum and enable more organic open data release.
• membership fees could be framed around the value delivered rather than around keeping costs low.
• there’s a challenge of supporting members who are at very different stages of open data maturity.
• individual stakeholder turnover within member organisations means the case for Stream and its value may need to be repeatedly restated within organisations.
• it is important to consider value for external innovators and other non-member users of the data, not only member organisations.

Next meeting: Thursday 24 September 2026 10:00-11:30 BST

Formal records, including attendees, are maintained by the secretariat. 

These are confidential to the Advisory Group Members.

Please note that throughout this consultation, Icebreaker One (IB1) uses the terms Open, Shared and Closed data as defined here.

If you have any questions about our submission or require clarifications please do not hesitate to contact us via policy@ib1.org. We have omitted questions which we did not answer. 

Thank you for considering our submission.

Call for input response:

As stated in IB1’s response to Ofgem’s AI in the Energy Sector Guidance Consultation, AI presents immense opportunities for delivering customer benefits, driving economic growth, accelerating the transition to net zero, and enhancing financial and regulatory frameworks. However, AI must be deployed responsibly – ensuring trust, transparency, and interoperability across sectors.

The questions in this consultation lean towards gathering information on what individual organisations are doing to assure themselves about their own use of AI. This is a necessary first step, but covers only a part of today’s interconnected energy sector services. To have real impact, assurance must be extended to cover AI-mediated data that is shared and used across organisations, sectors and use cases. It is in that context of data sharing governance that IB1 is responding to the consultation. 

IB1 highlights two key points about data assurance:

1. Assurance is evaluated in the context of the entity using the data, not the entity providing it. What is adequate for one usage situation may not be enough for another. Consequently, data sharing assurance can only be established by convening data users and publishers and developing their assurance needs and options around clearly defined use cases
2. Assurance is a journey, not a destination. As available data, data users and use cases proliferate, so do their assurance needs. A sector approach to assurance must establish an agile process for determining assurance needs, and encoding and enforcing them so that machines (AI or otherwise) can rely on them at scale.

These requirements for data sharing assurance existed before recent AI advances. AI hasn’t changed them, but the opacity of inference and the non-deterministic nature of outputs that are features of Generative Pretrained Transformer (GPT) models bring new challenges compared to deterministic and probabilistic approaches. A distinction between “Narrow AI” (AI designed for a specific task such as weather forecasting) and “General AI” (AI with human-like performance at different task types) may prove useful in assurance. Despite the rapid uptake of both types of AI, governance is still nascent within organisations, and even more so between them. These factors amplify the need and shorten the timelines for determining what “fit for purpose” means. 

Over the past six years, IB1 has developed and openly published co-design processes (Icebreaking) and Trust Framework-based approaches to enable rapid decision-making and implementation of governance of data sharing. These apply equally when AI is in the mix. Trusted use of AI demands well-understood, open governance with agile co-design by market participants delivered at ecosystem scale. The importance of these factors multiply as AI capacity and independence grows, with oversight often taking time to catch up.

IB1 recommends that, at the end of this information-gathering phase of its AI work, Ofgem ensures that the proposed Digitalisation Coordination Function is tasked with convening data stakeholders and working at pace to uncover, elucidate and specify assurance standards within a governance framework.

Question 1: Current AI assurance practices

1. How do organisations evidence that AI systems are operating as intended and delivering safe, fair and effective outcomes?

No response

2. What AI assurance approaches are currently used or under development, including in-house and third-party?

No response

3. What tools and technical capabilities are available to support AI assurance in practice, how mature and effective are they, and where are there gaps or opportunities for shared or sector-wide approaches?

A range of tools are available to support AI assurance, including model testing, performance monitoring, explainability techniques, audit logging and cybersecurity controls. While these approaches are becoming increasingly mature for assuring AI within individual organisations, there is much less maturity in assessing AI operating across organisational boundaries and shared data ecosystems. A lack of trusted data flows into AI systems could lead to poor, potentially unaccountable, decisions made or informed by machines and human-machine systems.

Effective AI assurance depends on trusted data flows. Capabilities such as provenance and lineage metadata, verifiable signatures, machine-readable licenses and organisational identity provide confidence in where data originated, how it has been processed and who is accountable. 

However, the characterisation and verification of AI models and their applications within data-sharing remains immature. More work is needed to identify and develop use cases that surface assurance requirements. Developing assurance around real-world use cases will help establish reusable assurance ontologies, with lessons drawn from more mature sectors such as autonomous vehicles or medical diagnostics. 

4. How are AI governance frameworks translated into operational practice?

No response

5. Which assurance or governance practices are most effective in supporting reliable outcomes?

The most effective assurance and governance practices are those that provide clear, proportionate evidence that data and AI systems are fit for their intended purpose. Assurance should be assessed in the context of how AI-mediated data will be used, recognising that while assurance signals originate from the data publisher, the decision to trust and use that data ultimately sits with the data user. 

IB1’s work on data assurance is a good starting point to adapt for AI-mediated data. Organisational and dataset assurance can be extended to provide consistent, machine-readable signals relating to provenance, quality, governance, licensing and accountability. As AI becomes more prevalent, these assurance signals become increasingly important for establishing credibility, compliance, quality and usability across organisational boundaries.

AI governance in the energy sector must also be codified to require a demonstrable contribution to net zero targets and consumer outcomes. This should be supported by appropriate explainability standards that are needed for AI-driven decisions, such as pricing and grid management, alongside regulatory monitoring to prevent AI-driven market monopolisation.

6. What skills, expertise and resources are required for effective AI assurance, and where are the main capability gaps?

Effective AI assurance requires a combination of technical expertise, governance, and stakeholder engagement. While AI introduces new technical considerations, many of the core capabilities are the same as those required for trusted data sharing. 

Core capabilities include: 

• Stakeholder engagement to understand ecosystem needs and develop proportionate, useful assurance to meet those needs
• Appropriate model and process selection
• Risk characterisation and management
• Data annotation (metadata) using appropriate standards
• Quality control and verification

AI’s potential to unlock data-driven innovation must be balanced with privacy, security, and ethical considerations. Therefore, IB1 advocates for AI models that:

• Respect consumer consent and data sovereignty, using decentralised identity frameworks
• Support open standards to ensure interoperability between AI-driven systems
• Embed transparency and explainability to mitigate AI biases and prevent regulatory fragmentation

Organisations using AI must possess relevant expertise that encompasses the above bullet points to ensure their solutions provide, secure, fair and sustainable AI.

Question 2: Risks and challenges

1. What are the main barriers to implementing effective AI assurance?

The fundamental barriers to effective assurance of AI-mediated data are structural and cultural. Organisations tend to work in siloes, which limits the sharing of both data and assurance practice. There is no accepted standard for AI explainability or verification, making it hard to establish a common baseline for what “good” assurance looks like. This is compounded by the pace of change in the underlying technology – rapidly evolving models and increasingly agentic processes make static assurance frameworks quickly outdated.

Stemming from this, the data which AI requires is also fragmented, held in inconsistent formats across organisations, and subject to different consent regimes to access. System-wide data that would support assurance work, such as LV feeder loads, flexibility capacity, and network constraints, often exists but remains inaccessible or non-interoperable between organisations. 

Privacy-preserving technology and synthetic data are effective methods to enable innovation without creating data protection issues. While good work is being done to provide this for smart meters, such as via the Faraday project, there is a need for similar efforts on other datasets, such as flexibility assets and behind-the-meter energy use. 

Finally, there is a lack of structured methods to evaluate and communicate assurance for data processing, including using AI, across organisational boundaries, so even where individual organisations assure their own systems, that assurance doesn’t travel or compound across the sector. This is worsened by a general lack of consideration by data publishers about appropriate AI use by the downstream applications of data users. For instance, no mechanism exists to signal which models are appropriate for which specific applications, leaving adopters to make that judgement without guidance.

2. What are the key risks associated with AI use in the energy system (including system reliability, market functioning and consumer outcomes)?

The key risks associated with AI in the energy system extend beyond performance of individual AI models to the governance of the data ecosystems on which they depend. Focusing solely on AI assurance for organisations’ use of their own AI systems risks overlooking compounding risks for data sharing scenarios across organisations, use cases and sectors.

Without clear, actionable assurance signals describing the provenance, quality, licensing and appropriate use of AI-mediated data, downstream users may be unable to assess whether the data is fit for purpose. Similarly, without the use of open and shared access frameworks, AI capabilities will consolidate in the hands of large incumbents who already hold proprietary datasets, creating market concentration risks that undermine innovation, consumer, and environmental benefits. 

We anticipate that cost, usage and IP conditions will hamper otherwise technically possible uses of the data. We recommend early surfacing of this information to mitigate five risks: 

1. Regulatory and compliance complexity: Data licensing must align with compliance rules around grid data, market data, and critical infrastructure. It is important to ensure data inputs to AI systems, and the outputs of the AI, remain compliant.

2. Third-party data dependencies AI models in energy often rely on weather feeds, satellite imagery, market pricing, and sensor data from multiple vendors. Each source carries its own licensing terms around permitted use, commercial exploitation, and AI training rights. Identifying these dependencies early prevents data supply chain disruptions during development, or worse, after deployment.

3. Intellectual property and model ownership: Who owns the AI model trained on licensed data? Many data providers now include clauses that restrict or claim rights over derivative works, including trained models.

4. Onward data publishing and monetisation: Energy sector companies typically want to share or sell AI-derived insights. Licensing terms set upstream can block valuable downstream opportunities. 

5. Long-term data access and continuity risk: Many foreseeable AI systems in the energy sector (e.g. predictive maintenance, load forecasting) need consistent, long-term data access. Identifying long-term data rights is critical to operational resilience.

3. Which risks are most difficult to assess, evidence, or link to real-world outcomes?

Risk assessment must be based on concrete use cases. These allow the counter-assessment of the risks of not using AI or not sharing the data created. The hardest risks to assess are those where there is insufficient information for a data user to make an informed judgement.

4. Where are current AI assurance approaches most limited in practice?

Data assurance, whether the data was generated by AI or otherwise, is unevenly applied in the UK energy sector. There is a lack of attention to the data foundations and their transparency and accountability. A coherent, well-governed trust framework with standards for assurance signals is required, along with processes to develop and monitor them.

As discussed in 1c, the characterisation and verification of AI models and their applications within data-sharing remains immature. More work is needed to identify and develop use cases that surface assurance requirements. Developing assurance around real-world use cases will help establish reusable assurance ontologies that can be implemented within trust frameworks. 

Question 3: Critical infrastructure considerations

1. How should AI assurance reflect the criticality of energy systems as national infrastructure?

No response

2. What level of rigour is appropriate for high-impact or safety-critical AI use cases?

An appropriate level of rigour can only be determined on a use case by use case basis.

Question 4: Consumer protection and fairness

1. How can AI assurance support fair treatment of consumers, including vulnerable groups?

AI assurance can support fair treatment of consumers by ensuring that AI-enabled decisions are transparent, accountable and designed around the needs of those affected, including vulnerable groups. This requires more than assessing technical performance; it requires governance arrangements that consider social impacts, consumer rights and the ability for individuals to understand and challenge decisions that affect them. 

IB1 advocates that data governance should establish clear principles, structures, roles and responsibilities, agreed by market participants, to enable accurate and timely data sharing at market-wide scale. These principles extend to AI assurance, ensuring that approaches are developed with cross-sector collaboration and learning rather than imposed through a purely top-down process.

2. What risks arise from AI-driven pricing, segmentation or prioritisation, e.g. fairness, transparency, consumer outcomes?

We strongly advocate for the adoption of a broader concept of social sustainability in defining fairness. This must conceptualise people in a manner beyond their economic roles and should also be capable of viewing people in terms of groups. This approach is vital to assessing a more holistic range of AI impacts beyond the individualised economic sphere. 

As stated by Which? “consumers [must] have the right to challenge decisions that are made about them by computers alone. This right is particularly important because it forces transparency and accountability in systems, making sure companies can be held to account if things go wrong”. Transparency and accountability are precisely what assurance seeks to bring.

Question 5: Cyber security and resilience

1. How should AI assurance align with existing cyber and operational security frameworks, e.g. NIS Regulations?

Assurance must align entirely.

2. How can AI assurance support system resilience, including identifying and mitigating cyber, operational and AI-specific risks?

IB1’s 2025 Positioning Paper on AI set out five dimensions of resilience for consideration with AI, going beyond the purely technical. The benefits of assurance to each are below:

1. Economic Resilience
   • Assurance enhances the likelihood that AI-driven data-sharing infrastructure (e.g. smart data initiatives​) is interoperable and equitable, preventing market concentration and boosting innovation and SME growth
2. Sustainability and Environmental Resilience
   • Assurance can increase confidence in the use of AI to optimise energy efficiency, emissions tracking, and climate risk modelling, helping industries and governments meet net zero goals​.
   • Assurance can be used to evidence that AI contributes to the UK’s net zero targets, enabling the requirement to be open to monitoring and audit
3. Regulatory and Governance Resilience
   • Consistent, well-understood assurance signals enable AI-driven compliance automation, reducing administrative burden and maintaining public trust in AI-enabled services
4. Digital and Cyber Resilience
   • Open and interoperable digital identity frameworks alongside clear governance and accountability enable data users to be assured about the provenance of the information being relied upon to make operational decisions
   • Robust data governance policies, evidenced as part of assurance, ensure AI systems remain secure, transparent, and resistant to manipulation
5. Social and Community Resilience
   • Well-understood, structured assurance enables confident deployment of  data-driven AI interventions to novel scenarios, including emergency responses and disaster preparedness
   • AI assurance can demonstrate that inferences and decisions are free from bias and discrimination, ensuring fair access to economic opportunities, financial services, and public resources.

Question 6: Proportionality

1. What does proportionate AI assurance look like across different use cases and risk levels?

The use cases drive both the assurance and the proportionality. Potential aspects of the assurance include:

• The identity of the assuring party
• Information about the training data
• Information about the contextual data used for a specific inference
• Model choice and use
• Explainability
• Verification
• Data protection
• Liability and redress
• Prompts for appropriate use
• Reports from other users
• Monitoring reports and incident logging
• Reproducibility
• Reliability of data availability and comparability in the long term

2. How can assurance approaches be tailored while remaining effective and practical, including for smaller organisations?

Clarity on the data use case, by both data producer and data user, greatly assists in identifying appropriate, proportionate assurance. Over time, informed by modelling and in-use analysis, individual use cases may well cluster into categories, further simplifying decision-making. Case studies, automated compliance, and effective, transparent monitoring and verification all lead to building greater confidence amongst stakeholders.

Question 7: External assurance and standards

1. Are existing frameworks and standards sufficient, or is sector-specific AI assurance guidance needed?

Existing standards are not sufficient, especially in data sharing ecosystems involving LLMs and agents. Governance concepts, structures and implementations are nascent, both within organisations and and between them.

Ideally, the UK would have cross-sector guidance and standards for AI assurance to enable assured data flows throughout the economy. Ofgem would then build on this where further refinement is needed, for example regarding critical national infrastructure and energy supply considerations. 

In the absence of UK-wide guidance, approaches taken by Ofgem should be designed to be replicable elsewhere by adopting existing standards where available, and openly publishing governance processes, data standards, and ontologies.

2. What role should independent assurance (e.g. audits, certification) play?
   

The role of independent assurance is use case specific. It is likely to be required for scenarios involving personal/customer data and where decision-making affects customers. 

Audits may be applied to both parties sharing data: the AI-mediated data provider to ensure their assurance signals are correct, and the data user to ensure they are not using AI-mediated data for unintended purposes.

3. What are the benefits and risks of external assurance approaches?

No response

Question 8: Future guidance

1. What would be most useful in future AI assurance guidance for the energy sector?

Future AI assurance guidance for the energy sector should focus on supporting practical decision-making rather than prescribing a single approach. Guidance should help organisations identify their AI use cases, understand the level of assurance required for the intended purpose and apply proportionate governance measures based on risk and impact. 

A key priority should be the development and adoption of open standards for representing assurance information, enabling organisations to communicate. Similar to the role of standards like Dublin Core in describing metadata, common assurance standards would support interoperability, transparency, and more efficient trust decisions across AI-enabled data ecosystems.

Guidance should also support cross-sector collaboration, engagement and knowledge sharing through forums where organisations can exchange approaches. IB1’s own experience with collaborative governance models and Trust Frameworks demonstrates the value of bringing participants together to establish shared principles, standards and assurance approaches.

2. What types of evidence are most useful in demonstrating outcomes in practice?

Useful evidence should demonstrate that assurance requirements are being met and that they lead to reliable outcomes in practice. This could include:

• Membership of data sharing Schemes
• Volume and continuity of data transactions
• New products and services enabled by AI-intermediated data
• Evidence of exceptions, “Red Flags,” when assured data hasn’t proven to be as reliable as asserted
• Audit reports
  

3. What examples or case studies would be valuable?

Assured Open Data

With input from Open Energy members, IB1 developed the Assured Open Data (AOD) scheme in the Energy Sector Trust Framework in order to provide a standard externally credible mechanism for organisations to demonstrate their implementation of Data Best Practice Guidance (DBPG). 

In the scheme, progressive assurance levels operate at both organisational and dataset level. Organisational assurance verifies identity, governance, and accountability. Dataset assurance covers metadata quality, format, provenance, licensing, accessibility, and update cadence. Four cumulative levels run from minimum DBPG-aligned entry expectations through to comprehensive, machine-readable cross-market reuse. Assurance is signalled through metadata that maps to human-readable badges.

SSEN-D was the first organisation to adopt AOD and has assured many datasets on its portal (e.g. SSEN Substation Data). AOD is in the process of implementation by other UK regulated energy sector companies.

Perseus Assurability Framework

Perseus is a UK-led Smart Data Scheme for SMEs to embed sustainable finance with trusted, automated carbon emissions reporting. In it, SMEs give permission for their energy consumption data to be processed securely by carbon accounting platforms in order to provide emissions data to financial service providers to evidence carbon reductions.

In order for financial service providers to reach reasonable assurance in the data they receive under the scheme, an assurability framework was developed, implemented by signed provenance records provided at each data exchange. These capture the identity of the sending and receiving parties within the trust framework, specify the data’s origins, codify the processing that has taken place, and make clear the permission and licence that covered the processing and transfer. Provenance records are chained as data is shared onwards, providing a verifiable record of assurance claims by processing parties.

While the assurance information carried is use case specific, the building blocks of provenance records – identity, origin, licence, permission, processing, transfer, receipt – are broadly applicable. This enables assurance requirements, once defined, to be deployed within trust frameworks very rapidly.

Question 9: Communication

1.  How should organisations communicate AI assurance to different audiences?

By working in collaboration with the users of AI-mediated data to identify use cases and the assurance they require, organisations can design and communicate assurance in terms that are meaningful to those users. Open publication of the purpose and rationale for the assurance signals provided allows for a rapid adopt-and-adapt approach for new scenarios.  

To support this, well-defined frameworks and guidance should be established, facilitating open standards, clear ontologies and machine readable formats for interoperability among people and systems. 

2. What information is most useful for consumers, boards, senior management and affected groups?

The type and usefulness of information is use case specific, and should be developed alongside the assurance needs for each use case.

Meeting Aims 

1. Update on the roadmap, AGs and DOC
2. Discuss engagement and tactics for Perseus
3. Plan a 2026 gathering

Summary:

• It was agreed that:
  • Five live case studies remain the year-end delivery target.
  • The Perseus for Domestic briefing note will be circulated
  • Innovation workshops with individual members will be explored as a complement to the top-down engagement approach.
  • AG4 will build a joined-up coalition narrative, including what it means for end customers
• It was noted that:
  • Xero has joined as a new member alongside Intuit and Sage, giving the ecosystem combined reach to over 1 million SMEs.
  • 2026 roadmap milestones are broadly on track, albeit with FSP case studies slow, but progress is being made. Q3 priorities include agreeing interoperability with Open Banking.
  • Palace of Westminster events are planned for the autumn with a sponsor secured: a build-up session in a committee room and a larger terrace event.
  • AG1 highlighted the need to resolve marketing permissions, embed finance where the customer is, and consider energy intensity as a metric.
  • AG2 highlighted sandbox improvements, better synthetic data, member-contributed datasets, and a planned autumn developer meetup focused on AI-assisted integration.
  • AG4 highlighted the need to demonstrate short-term value to members, tailor communications to different organisational priorities, and better articulate non-product value.
  • The DOC highlighted that Perseus’ financial outlook for 2026 has been revised down again, as membership commitments in principle are converting slowly to paid invoices. Prioritisation is ensuring core delivery is on track and the Perseus team is focusing on helping members deliver use cases.
• It was discussed that:
  • Identifying advanced, specific SME users who could champion Perseus use cases, would complement top-down institutional messaging with bottom-up engagement.
  • Messaging should lead with opportunity and optimism. There is scope to interlock with the Climate Hub’s existing “grow, save costs, be resilient” framing.
  • Members imagining the counter-outcome: without Perseus reaching scale, sustainable finance risks will fail to reach mass market.
  • ‘Perseus for Domestic’ could align with the Warm Homes Initiative, this is at a coalition-building and political alignment stage.

Next meeting: Monday 2 November 2026 13:00-15:00 BST

Formal records, including attendees, are maintained by the secretariat. 

These are confidential to the Steering Group Members.

With over 5 million customers worldwide, Xero is a leader in cloud accounting across New Zealand, Australia and the UK, bringing valuable expertise in supporting SMEs through connected digital tools and services.

As part of Perseus, Xero will contribute to a coalition of like-minded industry leaders exploring how trusted, interoperable data-sharing can simplify sustainability reporting for SMEs. With Perseus targeting a £5–10bn opportunity in embedded sustainable finance, the collaboration reflects the wider evolution of Smart Data and Open Finance in the UK and beyond, creating more connected, efficient ways for organisations and individuals to share and use data securely.

“Project Perseus is a practical example of how Smart Data can solve real problems for small businesses. Xero sees value in work that helps create trusted, interoperable approaches to emissions and sustainability data, making reporting simpler and helping build stronger foundations for better access to finance and future Open Finance innovation.”

Stuart Miller
Director, Public Policy & Technology Research, Xero

Bid submission: 26 August 2026 | Project start: 1 December 2026

Icebreaker One is leading a bid consortium for UKRI’s Consumer Led Flexibility (CLF) competition, part of the Clean Energy Superpower Mission. Our focus is building the trusted data sharing infrastructure required to make industrial and commercial (I&C) energy flexibility work at scale.

Many businesses are facing pressures around rising energy costs, the need for greater operational resilience, and growing expectations around net zero commitments. This project helps organisations understand where flexibility exists within their operations, how it could unlock access to finance for low-carbon investment, and how flexibility actions can contribute towards emissions reporting and decarbonisation goals.

As a consortium partner, you’ll help shape the infrastructure that could make these opportunities easier to access across the market.

Why join?

Partners will work with the consortium to:

• source and analyse energy data from a target site or business stream
• understand where flexibility capacity genuinely exists within safe operational limits
• explore how that capacity grows as you electrify (EV fleets, heat pumps, solar, batteries)
• investigate what it all means for your emissions reporting.
• shape an innovative smart data Scheme 

Participation is designed to fit around your organisation. Together, we’ll agree on the trial scope whether that’s target sites, single business lines or dummy data rather than live. Automation is the goal, not manual participation.

Why now? 

I&C consumers remain under-represented in flexibility markets, and participation has actually declined since the 2010s. Despite 170MW of I&C flex being added in 2026, the Clean Flexibility Roadmap estimates that another 580MW will be needed by 2030.

Over six months of discovery research with more than 70 stakeholders across 48 organisations we found that the business case for flexibility often doesn’t stack up on market revenues alone, and the data needed to unlock wider value is often fragmented. 

We’ve therefore narrowed our focus to:

1. Access to enabling finance through faster, cheaper routes to funding low carbon tech, flexibility assets and control systems
2. Evidencing carbon impact so flexibility actions can count towards decarbonisation strategies and ESG reporting

We’re particularly interested in multi-site businesses with multiple business lines or complex, varied demand profiles, exactly the type of organisations the current market is least set up to serve. 

Cost recovery and work package ownership are open for discussion in line with UKRI funding rules.

Key dates:

26 August 2026 – Bid submission
28 October 2026 – Applicants notified
1 December 2026 – Project start

Given the submission date, we’d like to hear from interested organisations in the next couple of days. Reach out to gea@icebreakerone.org

If you have any questions about our submission or require clarifications please do not hesitate to contact us via policy@ib1.org. We have omitted questions which we did not answer.

Overall Position 

We welcome Ofgem’s focus on strengthening the governance of energy system data but believe this consultation should fundamentally be about improving decision-making rather than selecting a technical solution. Before investing in new infrastructure, there must be a clear, transparent, and consistent process for assessing what data should be open, shared, or closed. Processes must be applicable at the level of individual datasets, however the sector also requires a mechanism for considering publishing decisions at the aggregate level, for example when risks associated with publication multiply at scale. Without this, there is a risk of building technology that does not address the underlying governance challenge. 

We support the Educational Model as the preferred approach, subject to some adjustments, as it addresses the challenge of improving decision-making without introducing centralised infrastructure that has potential to compound security and resilience risks. We also recommend expanding the assessment criteria to explicitly consider liability, governance, resilience, and interoperability. Responsibility should remain clearly assigned to each data publisher while recognising that some risks require collective assessment. A Trust Framework provides the appropriate mechanism for a collective approach to decision-making, data triage and risk assessment without centralised data infrastructure.

Effective governance should define, articulate, mandate, and enforce a monitoring, reporting, and verification process to ensure published data meets agreed requirements while allowing technical implementation to remain decentralised. This approach avoids creating single points of failure, strengthens system resilience, and maintains interoperability through common standards and assurance mechanisms. The Digitalisation Coordinator should focus on establishing and maintaining governance processes rather than operating centralised technical services.

We strongly recommend the adoption of a transparent, evidence-based approach to risk assessment. The consultation proposes solutions before clearly describing the threats, vulnerabilities, or risk reduction expected from each option. A structured risk assessment with established methodologies, such as the NCSC Framework, should underpin any changes to Open Data policy. Security considerations must also be balanced against the UK’s net zero objectives, recognising that unnecessarily restricting data access may hinder consumer benefits, innovation, system coordination, and decarbonisation without reducing risk. Given that much infrastructure information is already publicly available, decisions should be made based on demonstrable risk reduction rather than assumptions about the benefits of data restriction. 

Consultation question responses:

Question 1: Please provide examples of where data made available under DBP Guidance has allowed your business model to develop either new products and services, or make efficiency savings?

Icebreaker One and partners have used the data extensively in our work to assess and develop use cases enabling data to work harder to support energy system decarbonisation. Data made available under the DBP guidance has supported use cases in areas including, but not limited to:

• EV infrastructure development, including a targeted use case serving households without off-street parking 
• Heat decarbonisation and heat pump roll-out
• Local authority planning and LAEP development
• Cross sector data sharing between energy-water-telecoms for e.g. storm response
• Community energy build out supporting the Local Power Plan

Use cases are especially valuable in considering data security as these provide an opportunity to clearly define the purpose of data access, identify relevant stakeholders, and understand user needs. This approach helps minimise unintended consequences by ensuring that decisions about whether data should be open, shared, or closed are based on clear understanding of who needs the data, for what purposes, and under what conditions. 

Currently, we are using the data to form part of our development work to assess how a data sharing scheme could accelerate Industrial and Commercial (I&C) participation in electricity flexibility. Data included in the landscape assessment supporting the use case includes: network flexibility data (e.g. forecasts, zoning, trades), network constraints/headroom, and connections data (e.g. LCT connections, capacity registers). 

Question 2: Do you agree with the criteria underpinning the Options Analysis as described above? 

The proposed criteria provide a useful basis for the Options Analysis, but we believe they are currently incomplete and, in some cases, do not fully support an objective comparison of the proposed models. We make the following observations on the existing criteria.

Ownership and accountability: We are concerned that the current scoring does not appear to reflect that distributed responsibilities, when supported by common standards and governance, can provide clear and consistent accountability. There is a risk that the scoring methodology unintentionally favours centralised delivery models by assuming that the Digitalisation Coordination Function (DCF) is able to define and operate triage standards and processes internally that cannot be disseminated and carried out by data publishers. The scoring should assess how standardisation and governance can reduce risk irrespective of architecture. They should also flag where risks are present with regards to assigning responsibilities to the DCF as a body whose remit has not yet been defined.

The assessment should also consider how conflicts of ownership and decision-making would be managed in practice. For example, tensions may arise between network operators and a central coordination body where publication decisions differ, particularly if decisions contradict current publication requirements set out by sector governance regimes such as the Codes. Similar complexity exists for smart meter data, where governance may overlap between the Smart Energy Code (SEC), Central Switching Service (CCS/RECCo), UK GDPR, the Data (Use and Access) Act, Data Access and Privacy Framework, and Ofgem’s Data Best Practice Guidance. We therefore recommend that the assessment explicitly considers governance arrangements and conflict resolution mechanisms, in addition to whether any proposed central body (e.g. DCF) would assume the role and responsibilities of Data Controller under UK GDPR.

Data security:  We agree that data security should remain a core assessment criterion. However, security should be assessed across the entire data lifecycle, including how data is stored, governed, transferred, and accessed, rather than focusing solely on publication decisions. In particular, the analysis should recognise that centralising data storage or transferring data to a central body may increase systemic risk by creating attractive targets for attack and introducing potential single points of failure. These architectural trade-offs should be explicitly reflected in the assessment.

Data quality:  We welcome the inclusion of data quality into the assessment. To clarify scoring in this area, we suggest providing an authoritative definition of what is meant by “data quality”, distinguishing, for example, between schema compliance, completeness, accuracy, timeliness, and fitness for purpose. This distinction is particularly important when assessing the extent to which automated processes can improve quality. The assessment should also identify who is responsible for improving data quality under each option, together with the associated implementation and operational costs.

Cost:
We encourage further transparency on the assumptions underpinning the cost assessment. In particular, it is unclear how anticipated savings for individual licensees have been calculated and whether these represent genuine efficiency gains or simply the transfer of costs to a central coordination function. The analysis should also consider how any savings would be used in practice. For example, would reduced expenditure on local publishing platforms enable greater investment in data quality, governance, and workforce capability, or would these simply be treated as financial savings? In addition, we are concerned that indirect costs – including staff training, specialist expertise, organisational change, and ongoing governance – are underrepresented relative to technical implementation costs. In our experience, these organisational costs frequently exceed technology costs and should form part of any comparison of delivery models.

Question 3: Would you suggest any other criteria that you would consider critical for analysis?

We recommend the following additions to Ofgem’s analysis criteria:

Liabilities: while ownership/accountability is an analysis criteria, this does not fully enable the required assessment of who/which body would be held liable for publication decisions, nor assess processes required to handle situations in which data publishers and other relevant decision-makers disagree. In the Hybrid model, it is also notable that the use of automated processing may incur a specific discussion of liability where machine decision-making interacts with human decisions. Liabilities assigned to a potential DCF are also significant and not yet discussed, which requires further thought – particularly where liabilities are affected by other forms of legislation (e.g. Data Use and Access Act (DUAA)) or Codes (e.g. DCUSA data publishing specifications).

Governance: The governance of a system cannot be left as separate to the architecture of the system, but governance details would benefit from further depth in all options. In the Central and Hybrid functions in particular, this creates a large and undefined burden on a future body, whose own format and governance model remains subject to future consultation. As part of governance assessment, we suggest that Ofgem considers the clarity, transparency, and accountability of decision-making processes. For example, processes for assessing risks at the collective level, and determining action, would benefit from further detail. Such processes are important as they intersect with liability assessments. For example, if a licensed entity’s decision to publish Open data is challenged, this is left at conflict with the licensee’s internal process and/or potential obligations under industry codes.

Resilience: current analysis does not identify and assess risks emerging from the potential to create new single points of failure within the energy data landscape. This consideration goes beyond practices within monopoly bodies to also implicate single points of failure regarding aspects such as:

• An open data publishing portal (central/hybrid models)
• Decision-making (central model)
• Automation processes (central/hybrid models)

Interoperability: while the consultation presents arguments for open data publishing to be architecturally separate from other Trust Frameworks, this separation should not be extended to process and data assurance. IB1 suggests that the triage process – and the off-ramp for sharing data subject to restrictions (Shared data) – is not adequately discussed. Rather than presenting a vulnerability, consistency of process and data governance between the DSI, adjacent Trust Frameworks (e.g. CCS, Open Banking, IB1) and Open Data practices lend benefit to data security. Additionally, integration with Trust initiatives in the sector could offer the benefit of integrating Identity and Verification (ID&V) for data users and publishers, thereby streamlining onboarding, increasing confidence in the provenance of published Open Data and reducing the capacity for bad actors to misrepresent themselves across different platforms and processes. Failing to integrate could also unintentionally increase costs through duplication, as flagged via industry engagement groups in relation to Trust Frameworks being developed for the DSI and CCS.

We include analysis under these four categories as part of our response to Q4-6 below.

Question 4: Do you agree with our Option Assessment scoring and conclusion for the Centralised Model?

Our analysis suggests that a Centralised model presents the highest risks and lowest additional advantage as a pathway for improving the sector’s open data security, as well as uncertainty on costs. While the current Options Assessment captures some of these risks, we suggest that the full depth of risks to data security presented through centralised infrastructure have not been fully explored. There are also considerable legal and governance implications for permitting a central coordination body to view and triage all raw data. We suggest a number of points below that, if incorporated in the scoring, we believe would downgrade the Centralised model to the lowest scoring option.

Data security:

• 3.9: ‘The process flow diagram above shows how licensees would send ESD (untriaged) through their Data Preparation Node (DPN) across the Data Sharing Infrastructure (DSI), where it would be subject to Data Quality (DQ) review and then passed to a Triage Function within the Digitalisation Coordination Function.’:
  • Transference of large volumes of data to a central body creates a large threat risk, as acknowledged in the wider literature on information security and engineering. While section 3 describes this as ‘reducing the threat surface area’ this is not an accurate representation of risk; rather than reducing the threat, it concentrates it.
  • Currently, the Options Assessment does not specify how the proposed central structure would handle key governance decisions such as data deletion. If the body decides that data should not be published, it is unclear how the data is handled, where it sits within the central body vs licensees, and how decisions are documented and recorded. 
  • The boundaries of what raw data is transferred to the central function on this basis are unclear, as well as who makes the decision about what is or isn’t included for analysis. Scope creep presents a potential issue which could increase costs and act as a resource drain in the central body.
• 3.16 ‘the risk of accidental over-publication is lowered’ – analysis currently makes the assumption that trained individuals in the centralised process are less likely to create errors. It is unclear how this is different to equivalently-trained individuals in distributed licensees. Additionally, when they occur, a centralised body potentially increases the scale of consequences for errors.
• We suggest that tooling or methods applied to check triage compliance and consistency could be decentralised, defined and enforced via a Trust Framework. Centralisation of this function is not necessary to deliver the same outcomes.

Cost:

• We agree with the assessment on cost (score 1 – poor). Design, implementation and operation of the triage/data publishing service would duplicate functions already present in DNOs.
• The overall cost score appears to be contradicted by point 3.14: ‘The model should provide savings for the licensees, as the costs of triage and running an Open Data Platform would be reduced significantly’. This depends on how the DCF is funded and managed, which is not yet determined.

Data quality:

• We query why this metric is scored as 4. In particular, we raise concerns that assumptions have been made about the necessity and feasibility of the role that the central function is envisaged to perform in 3.16 ‘Additional data utility benefits can be accrued through a single centralised portal, increasing interoperability, and allowing for data quality and schema validation as part of data processing, increasing the consistency of data offerings across the sector.’:
  • Analysis assumes that the digital coordinator is successful in defining a schema all parties agree with, and 
  • Will ensure that data provided using the schema is conformant (this may be costly or face limits on compliance). 
• We suggest that schema agreement and conformance do not require centralisation; the same outcomes could also be achieved in a decentralised manner via mandating the use of a Trust Framework.

Liability:

• Structure not currently discussed, though the model implies a high degree of reliance on the process and decisions of the DCF. 
• Potential for liability conflict unless clarified.

Governance:

• Governance of key processes run by the DCF are as yet undefined.
• Governance of the process to decide whether metadata is published openly is unclear.

Resilience:

• This model creates single points of failure in relation to process/decision-making and technical architecture (portal). 
• Relationships/liabilities between data providers and the DCF require clarifying with regards to how licensees may be impacted by a failure or breach of centralised systems. 

Interoperability:

• If successfully imposed (this is a risk – see Q2-3), the application of Schema could result in a high degree of data interoperability with the DSI. However, a centralised approach is not the only way to ensure this.
• Legal interoperability must also be addressed; the consultation currently does not propose a function to address licence consistency.
• Interoperability with other Trust Frameworks has not been actively considered in the consultation document.

Question 5. Do you agree with our Option Assessment scoring and conclusion for the Hybrid Model?

Presentation of the Hybrid model offers advantages in terms of checks for consistent application of triage processes, while retaining primary decision-making as a decentralised function. However, the current Option Assessment for the Hybrid Model does not adequately address the governance of automated checks, how this functions with human decision-making, how collective decision-making will be conducted, or how data quality improvements are guaranteed. We believe that amendments to scoring based on points raised below would downgrade the Hybrid model’s overall score and encourage Ofgem to consider this when determining their minded-to position.

Data security:

• Issues related to data centralisation and deletion remain, as described in Q4.
• The automated component of the model may function for certain aspects of assessment – e.g. providing an additional compliance function to check triage steps have been followed – however further exploration of how this interacts with human decision-making would be beneficial.
• Governance of automated checks is not fully described at present. This potentially interacts with gaps in liability assessment identified in Q2-3. Example: dataset is approved by automated compliance function but later found to present risks that were not picked up: does the original data publisher, centralised body, or provider of the tool (if third party) hold liability?
• The model does not fully address how decisions beyond compliance will be made, particularly regarding data which:
  • requires an assessment of risk at the collective level, and
  • requires an assessment of risk related to landscape changes over time.
• We suggest that tooling or methods applied to checking triage compliance and consistency could be decentralised, using a Trust Framework to both define good and enforce it.

Data quality:

• Observations outlined in Q4 are also applicable to the Hybrid model; it is unclear how data quality improvements are guaranteed through this proposal in a manner that is different to improved coordination/accountability applied to decentralised data triage. 

Liability:

• The Hybrid model’s liability structure, and relationship to DCF liabilities, is not yet defined.
• Liabilities for automated processing decisions are not discussed.

Governance:

• Governance of key processes run by the DCF requires definition.
• Governance of automated processes is not currently discussed.
• Monitoring, Reporting, and Verification (MR&V) mechanisms are missing to ensure data published conforms to requirements.

Resilience:

• This model reduces certain single points of failure present in the Centralised model by keeping triage processing decentralised and adding an automated process check. 
• However, the data portal element remains centralised, as does data Schema assessment.

Interoperability:

• If successfully imposed (this is a risk – see Q2-3), the application of Schema could result in a high degree of data interoperability with the DSI. However, this can also be achieved in a more decentralised manner than the Hybrid model presents.
• Legal interoperability must also be addressed; the consultation currently does not propose a function to address license consistency.
• Interoperability with other Trust Frameworks has not been actively considered in the consultation document.

Question 6: Do you agree with our Option Assessment scoring and conclusion for the Educational Model?

We disagree with the current scoring of the Educational Model. In particular, the Options Assessment does not address how decentralisation automatically increases security threats despite high cybersecurity standards within licensees, why data quality cannot be assured with effective data governance, or any MR&V mechanisms for the proposed model. 

Ownership and accountability: 

• We suggest that this score is revisited; distributed ownership does not necessarily complicate accountability. All data publishers are regulated parties whose accountability to Ofgem, and other bodies, is guaranteed in relation to many other functions they deliver. We disagree with the current assessment score on this basis. 

Data security:

• We disagree with the low scoring for this category. Licensees are required to maintain high cybersecurity standards for many forms of operational data, including critical national infrastructure. Based on this, it is unclear why distributed responsibility equates to low cybersecurity scoring.
• We suggest that distributed data presents a lower security threat than centralised infrastructure for several reasons. This includes:
  • No single point of failure or leverage
  • Different internal security infrastructure at each licensee makes “full spectrum” breaches much harder
  • Untriaged data does not leave the organisation boundary
• On this basis we suggest that the data security score is reviewed and recategorised. 

Data quality:

• We disagree with the low scoring for this category. The assessment appears to define data quality through only a centralised scheme validation despite the ability for agreed standards, accountability, and assurance processes with effective data governance. 
• The same data quality investments considered for the Centralised and Hybrid model should be included for the educational model. 

Liability:

• Liability is clearly assigned to each data publisher.
• Collective liability would need to be addressed, e.g. in the case where data publishing needs to be assessed at the collective level. This could be addressed meaningfully through a Trust Framework approach with appropriate governance and associated decision-making.

Governance:

• Effective governance within a Trust Framework can define what is required and enforce it without centralisation.
• MR&V mechanisms would be required to ensure data published conforms to requirements.
• The Educational Model could be adapted to give publishing parties the triage check tooling from the Hybrid Model that otherwise sits centrally in the digitalisation coordinator. The DCF, or anyone else who has the specifications and technical ability, could provide the checking functionality, allowing the DCF to focus only on decision-making and collective assessments.

Resilience:

• See security section above: this option presents significant advantages by avoiding the creation of single points of failure and making a full spectrum breach less likely. 
• We do not believe that system resilience has been adequately accounted for in Ofgem’s current analysis – doing so could significantly change the minded to position.

Interoperability:

• A Trust Framework can support interoperability by establishing common requirements and assurance mechanisms across publishers.
• Identities and standards established in the DSI trust framework may be used to harmonise trust signals for Open data, such as provenance and assurance, with those for data shared securely within the DSI, with both operating the same peer-to-peer data sharing principle.

Question 7: Do you agree with our minded to position? If not, what is your view as to the best approach to this issue?

We support Ofgem’s focus on strengthening data governance but believe the key challenge is improving the decision-making framework and governance processes that determine whether data should be Open, Shared, or Closed, rather than developing new technical infrastructure. Our preferred approach is the Educational Model, strengthened through a Trust Framework, enabling collective decision-making and standard-setting for data triage and risk assessment. With the addition of governance, liability, resilience, and interoperability as core criteria, the Educational Model provides more robust assurance while avoiding unnecessary centralisation of technology or liability. Effective governance should establish clear standards with monitoring, reporting, and verification processes, allowing the DCF to focus on oversight and decision-making. Any changes to Open Data policy should be supported by evidence-based risk assessment that balances security considerations with consumer benefits, innovation, system coordination, and progress towards net zero. 

Meeting Aims

1. Surface levels of comfort and any concerns regarding Stream’s direction of travel to function as an independent entity
2. Clarify and identify the stakeholders required to sign off on the direction of travel
3. Agree and sign off the Open Data Maturity Assessment policy and implementation activities

Summary:

• It was agreed that:
  • Outstanding SG votes must be completed promptly, including the previous meeting’s vote on Q3 outcomes and the Open Data Maturity Assessment vote from this meeting.
    • Current voting returns remain slow, causing delays and additional burden on the IB1 Secretariat, this is particularly exacerbated during the APR period.
  • Members should review the full Open Data Maturity Assessment pack and complete the Open Data Maturity Assessment by October 2026
  • Stream will proceed with drafting the transition paper explaining the move from project to independent entity, taking note of member feedback.
  • Business development principles received no objections so the process will be embedded and tested.
  • The business development process would be updated to include Member‑led (as opposed to externally requested) use cases.
  • Price‑review points will be added to Stream’s annual calendar (Oct/Nov) to ensure that what is being charged is in line with cost to serve. These will be scheduled to align with strategy planning.
  • Stream will formalise and publish its values, and define service offerings for members vs paid users.
  • Stream will support members with procurement and internal sign‑off, preparing collateral for exec‑level conversations.
• It was noted that:
  • Q3 will be a busy quarter, covering transition roadmap, Open October, three‑year strategy, WBC bids, continuous water quality monitoring, citizen science proposition, and refreshed web presence.
  • Resource constraints exist in some companies so the Product Team will provide procedural guidance where needed.
  • Transition to an independent entity is less overhead than might be perceived – primarily a change in where money flows and how contracts are held. Two transition checkpoints will occur:
    • 2027: new entity begins receiving funds within the existing project window
    • 2028: full legal transition when the collaboration agreement ends
  • Regulatory support from Ofwat is strengthening, including alignment with wider data‑sharing initiatives and future policy guidance.
  • Stream remains an exemplar of sector collaboration with strong cross‑company engagement.
• It was discussed that:
  • Members need clearer, more regular executive‑level communication from Stream to help articulate value internally.
  • Standardised service packages and repeatable processes will reduce bespoke estimation and support scale‑up.
  • The value framework is the critical decision point for assessing incoming commercial work and is being strengthened.
  • Transition planning should consider:
    • Funding risks and minimum viable membership numbers must be understood to ensure long‑term sustainability and Stream’s future funding model must balance member contributions with grants and sponsorship, keeping base costs low and avoiding discretionary‑spend risk.
    • Future recognition of Stream as a regulated business expense could support stable funding through business plans. Stream must avoid becoming a discretionary spend, requiring strong narrative, regulatory alignment, and visible outputs.
    • Breakout groups raised concerns about procurement blockers, transition clarity, scaling, and funding gaps, all to be addressed in the transition paper.
    • Procurement due diligence will be required when Stream becomes a supplier (anti‑slavery, ethics, minimum standards).
    • Scaling considerations (e.g. citizen science) must be carefully planned to remain sustainable.
  • Suggested internal member organisation stakeholders for sign off on the transition strategy include:
    • Exec sponsor (often CIO/CDO/IT Director or Chief Engineer)
    • Procurement
    • Legal
    • Open Data Governance / Data Strategy groups

Next meeting: Tuesday 10 September 10:00-12:00 BST

Formal records, including attendees, are maintained by the secretariat. 

These are confidential to the Steering Group Members.

Meeting Aims:

1. Identify how comms and engagement work can position Perseus as essential to members
2. Identify milestones and comms moments in H2

Summary:

• It was agreed that:
  • IB1 should review member communications with a view to providing more regular, digestible, value-oriented summaries for members.
  • The group should better package engagement value for members, including parliamentary events, reports, and roundtables, so value is not framed only through technical progress.
  • The group should explore convening members to discuss how they maintain internal engagement and what support helps them make the case for Perseus internally.
• It was noted that:
  • The internal case for sustainability initiatives has become harder over the past 3–4 years, with executives sometimes finding reasons not to engage.
  • Members need to see short-term value from Perseus, such as visibility, knowledge, and connection, rather than only a long-term proposition.
  • The value case for Perseus needs to be calibrated to each member organisation’s priorities, which differ across firms.
• It was discussed that:
  • Perseus should position itself as business-critical and “must-have” for members.
  • There is a need for Perseus to equip members to sell the proposition internally with evidence of immediate value and organisation-specific messaging.
  • Perseus could seek to build more external prestige. Co-branding with members’ research, events, and outputs might help.
  • Perseus-convened roundtables with micro-businesses were a practical benefit for many members.

Next meeting: Thursday 8th October 2026 10:00-10:40 BST

Formal records, including attendees, are maintained by the secretariat. 

These are confidential to the Advisory Group Members.

As digitalisation and data initiatives across the energy sector gather pace, there is a growing imperative to ensure that activities are coordinated. Before deciding who should perform this coordination role, it is important to establish what the role needs to do and why.

To support this conversation, we have undertaken a delivery body-neutral analysis of a future digital coordination entity. Our analysis identifies the responsibilities, functions and capabilities required to deliver effective coordination with the findings underpinning our concept note, which sets out a proposed model for a sector-owned coordination entity.

Our aim is to support decision-makers by identifying the key considerations that should be addressed before decisions are made on institutional design and ownership. By focusing on the functions, capabilities and delivery model of a future coordination entity, this analysis is intended to inform sector deliberation, future public consultation, and the institutional arrangements needed to deliver effective digital coordination for the benefit of the climate, consumers and economic growth.

Read the concept note here

Emily Judson

Emily Judson, Head of Energy at Icebreaker One presented an analysis of the format, function and capabilities of a future digital coordination entity for the energy sector at our Open Energy Steering Group meeting.

Defining the role

Digitalisation is a broad and complex area of sector transformation. At present, large sector initiatives – such as the Data Sharing Infrastructure (DSI) and Consumer Consent Solution (CCS) – are driving significant change focused primarily on data and data sharing. The recent Digitalisation Vision also sets out new responsibilities for data domain coordinators, with NESO, RECCo and Elexon assigned new intended roles. 

We suggest that this focus on data must be clearly reflected in the initial scope of the coordinator role, allowing effort and resources to focus on the area where coordination is most urgently needed.

Our analysis also highlights that clear rights, responsibilities and accountability, alongside stronger feedback loops between delivery, oversight and decision-making, must be established to support effective coordination.

Essential functions

The coordinating body would perform seven core functions as identified by DESNZ and Ofgem (Digitalisation Vision, 2026):

1. Own and coordinate the digitalisation architecture
2. Assure digitalisation delivery & architecture against strategic documents
3. Manage governance processes for industry coordination
4. Provide strategic recommendations to government and the regulator
5. Ensure interoperability and alignment with other sectors
6. Coordinate and align data domains
7. Identify, manage and mitigate risks

We recommend that the scope of these functions could be expanded to incorporate the following:

• Coordination of technical and legal interoperability requirements for federated trust frameworks – in energy, across sectors, and potentially internationally; 
• Coordination of rights and liabilities in digital architecture and delivery;
• Monitoring, reporting and verification (MRV) of digitalisation delivery against key success criteria – which must include decarbonisation and consumer outcomes;
• Supporting sector knowledge-sharing and upskilling; and 
• Conducting a periodic horizon-scan function to support responsiveness to digital landscape shifts. 

Three core capabilities

Our analysis identified three core capabilities required to empower an effective coordination entity:

• Secretariat: Required to manage processes, facilitate collaboration, and ensure transparency and accountability across participants.
• Monitoring and Evaluation: Provides monitoring of delivery and robust evaluation of outcomes; this capability supplies the evidence needed for scrutiny, learning and ongoing accountability.
• Enforcement Coordination: Coordinates delivery between existing institutions, manages interfaces with regulators and government, and ensures that agreed processes are implemented consistently. 

Each capability above has the potential to create conflicts of interest depending on where it sits. Institutional design and governance are critical considerations and our analysis suggests that independence of the coordinator should be prioritised.

Delivery body format

Following the research and analysis outlined above, we examined what possible delivery body formats could effectively serve the functions and capabilities required to underpin successful digital coordination for the energy sector.

From these options, we see the functions and capabilities of the coordination entity best delivered via an independent mission-locked non profit company. It would be limited by guarantee and run through joint Directorship, with voting seats for the core delivery bodies and further seats spanning representative areas of the wider sector. DESNZ and Ofgem would participate as observers, reflecting the coordinator’s role in facilitating coordination and delivery rather than setting strategic direction.

This approach offers several advantages:

• The entity holds no existing market role, reducing the potential for conflict of interest
• It is vendor and software-agnostic
• A socio-technical, multi-stakeholder make-up enables thorough consideration of different angles of the data landscape
• The body can move quickly, offering a flexible and agile approach that a fast-changing landscape requires.

Evaluating delivery body types

Our analysis explored a range of delivery models, including coordination via: an existing Ofgem regulated organisation – either embedded in digital programme delivery or separate from this; an existing organisation with relevant expertise but not (currently) regulated by Ofgem; and a new purpose-built entity.

Each presents different strengths and trade-offs. Existing organisations could offer sector knowledge and established relationships, but may face actual or perceived conflicts of interest. This is particularly salient if they are actively involved in the delivery of sector data programmes and/or have prior interests related to other aspects of their market position. Assigning new enforcement powers to an organisation with an existing market position may add further complexity to its other role(s). A new organisation could provide greater independence and flexibility, but could take longer to establish and embed within the sector.

Rather than recommending a preferred delivery body, this analysis provides a framework for assessing these options against the capabilities required for successful coordination. 

Get involved

We welcome feedback as this discussion develops, to join the conversation, contact energy@ib1.org or sign up to Open Energy membership here

Please note that all outputs, including this report are © Icebreaker One Ltd. 
The copyright of this content will be considered for release under a Creative Commons Attribution (CC-BY) open license based on the materiality of the outcomes at IB1’s sole discretion.

Meeting Aims: 

1. Present the proposal: what good looks like for a digital coordination entity
2. Collectively discuss a responsible body for the coordination entity
3. Seek guidance on whether to present this proposal as from Open Energy or a new independent vehicle

Summary:

• It was agreed that:
  • As the group had previously highlighted, a digital coordination layer is essential to fill the governance gap between strategic oversight and technical delivery.
  • AG1 requires a co‑chair – members should nominate themselves or colleagues.
  • IB1 will produce a synthesis paper capturing the discussion on digital coordination entity options, including SPV model.
• It was noted that:
  • Scope definition must come before deciding the organisational form of the coordination entity. Multiple members emphasised that the role, remit and success criteria for the role must be clear before choosing a responsible body.
  • The domain coordinators (NESO, RECCo, Elexon) have been working closely together but it was acknowledged that the transparency of this work is currently limited, and greater visibility would be helpful.
  • Security considerations are not expressly called out in the digitalisation coordination function slide and must be incorporated.
  • Furthermore, decarbonisation alone is not a sufficient mission framing; consumer value, affordability, and security of supply should also be considered.
  • Getting the balance of independence vs domain knowledge is critical for any future coordination entity to address conflicts and ensure practical understanding.
  • Independence might be able to be achieved through behavioural measures, such as business separation within an organisation, rather than different entities.
  • Cross‑sector learnings (open banking, open property, smart data) provide valuable patterns for governance, trust frameworks, and scheme design.
  • Ofgem/DESNZ are expected to consult on the digitalisation coordination function by the end of 2026.
  • The I&C flexibility use case discovery phase and event on 17th June concluded positively, revealing clear user needs and gaps.
  • Quarterly goals work is progressing and will continue with Q3 focus on preparation for the AGM and progressing AG1/AG2 ramp up.
  • Open Energy’s analytical approach is welcomed, provided it helps frame consultation questions rather than prematurely prescribing an answer.
• It was discussed that:
  • A “good” digital coordination entity should include:
    • Secretariat capability;
    • Monitoring & evaluation;
    • Enforcement (complex, likely shared with Ofgem/DESNZ).
  • Representation and observer roles should be considered, including:
    • Security organisations;
    • Supply chain actors;
    • Flexibility service providers.
  • There is a need for a tighter feedback loop between coordination, delivery bodies, and standards.
  • Interoperability is essential, both within the energy sector and cross‑sector, and avoiding “reinventing the wheel”.
  • There could be turbulence in the market if multiple organisations put themselves forward to host the coordination function.
  • Funding models and trust frameworks are required for any coordinating layer to operate effectively.
  • Decisive decision‑making is becoming urgent, given market movement and upcoming regulatory consultations.

Next meeting: Tuesday 8 September 2026 14:00-15:30 BST

Meeting Aims:

1. Illustrate the SME journey to a loan 
2. Update members on FCA and Perseus for Domestic discussions
3. Endorse initiative to reduce friction for SMEs to prove their address 

Summary:

It was agreed that:

• Immediate focus remains on real-world SME case studies and proving that banks can implement Perseus with their customers.
• While the business case for Perseus is clear (as per 2024 and 2025 annual reports), the internal investment cases must show material progress from the Perseus Members
• The Secretariat will continue to engage with the FCA, including preparing an updated briefing note on the potential impact of regulatory clarification.
• A working group will be formed to explore ways to streamline SME smart data access.

It was noted that:

• Perseus has been featured in DBT’s Smart Data Strategy. Discussions with DBT are positive and ongoing.
• The FCA has indicated that it can explore the potential of becoming an observer on the Perseus SG.
• From a measurement methodology perspective, there is a need for a minimum-viable, harmonised carbon intensity metric (potentially linked to revenue or EBITDA) that would meet baselining needs, but not compete with existing industry and commercial scoring models.

It was discussed that:

• From a regulatory perspective, a tension has been identified between the potential interpretation of FCA rules and banks’ perceived ability to proactively reach out to customers on climate finance. Members discussed the potential  impacting of this on the perceived size of the addressable market. This discussion helped frame inputs into the briefing note.
• From an impact perspective, smart meter data could support broader customer engagement: nudges, insights, and efficiency recommendations, not just finance offers.
• From an opportunity perspective, there is potential for Perseus (for SMEs) to expand into the domestic market (Perseus for Domestic), potential links with the Warm Homes Plan.

Next meeting: Monday 12 October 2026 10:00-11:30 BST

Formal records, including attendees, are maintained by the secretariat. 

These are confidential to the Advisory Group Members.

Meeting Aims:

1. Discuss member requirements for synthetic data in the Perseus sandbox
2. Evaluate what requirements existing for sandbox testing
3. Introduce and get feedback on the idea of a dev meetup

Summary:

It was agreed that:

• The Perseus team will formalise the existing readiness-check component into something members can use to demonstrate their integration is tested and working, building on roadmap work already under way.
• an autumn session will be organised, potentially in hackathon format, exploring how the sandbox could support AI coding agents and establishing what currently works.
• AG2 will set up a working group to design a structured approach for extending organisation role information held in the Directory, such as what data an energy data provider can supply.

It was noted that:

• The sandbox exists to let members verify their integration, build confidence ahead of live deployment, and test what cannot be tested in isolation, including correct handling of error responses. Out of scope: simulating system failures, load and performance testing, and standard authentication flows already covered elsewhere.
• Several candidate synthetic data sets exist: the Centre for Net Zero’s Faraday V5 (fully synthetic, modelled on real consumption with property and location parameters), the London Datastore (smaller, built to compare tariff changes), and Open Power System Data (domestic and small business consumption, though needing adaptation for Perseus).

It was discussed that:

• Smart data sharing is front of mind for one member, who described the market as blocked and in need of help to move forward.
• The suggestion that progress requires ICO enforcement or new legislation was not considered to be the best solution, as it relies on intervention from outside the market rather than action within it.
• Data protection uncertainty is often used as a reason to avoid progress altogether. Greater assurance on this point could remove a common blocker to engagement.
• There are concerns that RECCo’s purpose-agnostic approach to data sharing sits in tension with GDPR principles on the scope of permission granted.

Next meeting: Tuesday 6 October 2026 10:00-11:00 BST

Formal records, including attendees, are maintained by the secretariat. 

These are confidential to the Advisory Group Members.

Last week, our Open Energy accelerator workshop, held at Arup’s London office, brought together network operators, flexibility service providers, regulators, government representatives and energy users to explore a new data sharing Scheme designed to scale Industrial & Commercial (I&C) flexibility across the energy sector.

The objective was to test the use case, understand user needs and explore the data landscape surrounding flexibility markets. A key theme emerged throughout the day: while data is fundamental to scaling flexibility, participation will ultimately depend on whether we can create the right incentives, build trust and clearly communicate the value to different audiences.

This reinforced an insight from our previous webinar that scaling flexibility is far from a one-size-fits-all challenge.

The incentive problem

It’s often assumed that financial incentives alone will drive participation, but our discussions revealed a much broader picture. For some organisations, flexibility supports ESG objectives and carbon reduction commitments. For others, it contributes to energy resilience, operational security or reducing pressure on an increasingly constrained energy system. In a UK market facing volatile prices and some of the highest energy costs in Europe, the motivations for participating vary significantly.

The ESG angle is a particularly interesting one, but as Charlotte Roniger, Flex Assure UK points out:

“For flexibility to scale in the I&C sector, ESG frameworks need to catch up. The strongest business case for consumer-led flexibility isn’t always on the balance sheet, sometimes it’s in the sustainability report.”

Even if the business case was centred around balance-sheet considerations, it’s important to be realistic about the financial returns available. Flexibility revenues alone are not always enough to drive widespread participation. If we want to attract more organisations, we need better ways of discovering, engaging and validating participants while clearly articulating the broader value flexibility can deliver. The incentives, in other words, go beyond revenue generation.

Data & Trust

While much of the discussion focused on incentives for participation, it quickly became clear that organisations cannot participate in flexibility markets if they cannot see, understand or trust the opportunities available to them.

For flexibility providers, networks and energy users to coordinate effectively, they need access to high-quality, interoperable data that can be shared securely. Visibility of assets, standardised information and clear governance frameworks all help reduce the friction that currently makes flexibility difficult to discover, assess and scale.

While some of this work is being tackled by sector initiatives already under development – for instance the Flexibility Market Asset Register – the event highlighted ongoing gaps in the data sharing needed to get energy consumers to the starting line of market participation (e.g. opportunity assessment linked to decarbonisation or electrification planning) and to evidence the impacts of trades (e.g. carbon savings).

Communicating value: a hearts and minds challenge

Even with the right data and incentives in place, bringing the right players to the table depends on how effectively we communicate the value of participating in flexibility markets. But, like incentives themselves, this is not a one-size-fits-all challenge.

Scaling flexibility requires us to communicate value differently to different audiences. What resonates with a CFO may be predictable revenue streams or accelerated connections helping a business to expand. What motivates a sustainability team may be progress against ESG goals and carbon reduction targets. For policymakers, the focus may be energy security, decarbonisation and system efficiency.

Understanding these different perspectives is critical. Participation will not come from incentives alone, it will come from building confidence, trust and a shared understanding of the role flexibility can play.

As one participant reflected: “Long-term change isn’t going to come from incentivisation alone. We need to be thinking about cultural change.” This was perhaps the most important takeaway from the workshop. Scaling flexibility is not just a technical challenge; it is a challenge of coordination, trust and engagement, as Simon Evans, Arup stated:

“Delivering flexibility at scale is a socio-technical challenge and is as much, if not more, about trust, governance, and incentives as it is about technology and data.”

Help shape the future of I&C Flexibility

Flexibility already forms a core part of the Government’s Clean Power 2030 Action Plan and has the potential to deliver clear value – from reducing system costs for networks to unlocking new revenue streams and resilience for energy users. But if flexibility is to scale, we need to look beyond technology and market design alone.

Expressions of interest are now open for joining our Open Energy advisory groups. This is an opportunity to help shape the future design of the Scheme as we move towards pilot and implementation.

Sign up now: https://ib1.org/energy0/2026-advisory-groups/

Meeting Aims 

1. Outcomes reviewed and endorsed
2. Direction set for Q4 2026 and beyond

Summary:

• It was agreed (subject to the formal voting form to follow) that:
  • Members must respond to the previous meeting’s votes as soon as possible.
  • Members accept the need to transition Stream to a more robust legal footing as a separate legal entity, provided identity and continuity are preserved.
  • Members support the website redevelopment roadmap and direction of travel.
  • Members should ensure their EIR teams are directing people to Stream.
• It was noted that:
  • Environmental Information Requests (EIRs) have a perceived increase despite proactive data publishing, possibly due to more AI‑generated requests – this should be monitored.
  • Stream’s progress is sometimes constrained by external dependencies (e.g., CReDO/CaSTCo) and should focus more on outcomes within its control.
  • Stream BAU has varying needs, maturity and internal awareness in water companies.
  • Ofwat is increasing its focus on AI adoption, regulatory reform, and potential AI sandboxes
    • The Stream team will follow up regarding AI in Stream’s roadmap, the potential for Stream to be used as a sandbox and involving Ofwat in Stream’s data sharing governance review
  • Transitioning to an independent entity could unlock grant funding, agility, and reduced corporate friction.
• It was discussed that:
  • The website redesign should better articulate Stream’s value, feel more “alive,” and improve sector engagement.
  • EIR standardisation could reduce burden, improve efficiency, and support open data pathways and conformity to regulations, however, cost savings may be limited.
  • There is a need for 1‑1 conversations with each member company to understand readiness, BAU status, and internal decision pathways.
  • It is important to articulate what happens if the legal transition does not occur, i.e. “the lights go off” – what will the impact to internal business cases be?
  • There are growing opportunities in cross‑sector use cases (health, agrifood, energy) and secure research environments.
  • Stream needs to manage the risk from multiple innovation bids requiring Stream’s support simultaneously where resources are limited.
  • lessons learned from other sectors (e.g. energy, banking, finance, smart data) should be taken into account to facilitate Stream’s next phase to move at pace. Lessons include the tensions between building tech vs building market incentives, regulatory under/over-reach and delivery of frameworks at a reasonable cost that are both adopted and used by the market.

Next meeting: Tuesday 14 July 10:00-12:00 BST

Formal records, including attendees, are maintained by the secretariat. 

These are confidential to the Steering Group Members.

Meeting Aims:

1. Discuss proposed SG member communication support
2. Gather AG4 advice and feedback on how to communicate the Perseus legal contract to members

It was agreed that:

• IB1 will revise the legal scheme structure slides to reflect the feedback from the group on the communications framing for the Perseus legal contract discussion.
• The revised version of the deck will be shared as an update at the next meeting rather than returning to this group for further review.
• The July meeting should be moved later in the month, extended to one hour, and positioned as a pre-summer wrap-up, with no meeting to be held in August.

It was noted that:

• Progress had been made in refining the approach to SG member engagement, including a shift from a one-size-fits-all approach to a more bespoke model of support for target organisations.
• The priority SG member targets remain the Federation of Small Businesses, British Chambers of Commerce, and Institute of Directors, reflecting their broad reach across UK business audiences.
• The legal contract communications material is intended to support more than one audience, including legal teams and wider senior or operational stakeholders within member organizations.
• The current visual presentation of the legal contract content appeared process-heavy and may be difficult for non-technical audiences to navigate.

It was discussed that:

• Efforts to mobilise SG members to use their voice to endorse and credentialise Perseus were underway, including the intention to produce tailored one-page briefs and build stronger relationships with communications teams over the summer.
• While legal review is an important part of the process, the broader value proposition for organisations lies in the opportunity to influence rules, implementation, and future scheme development.
• Communications materials should lead more clearly with the “why” and intended outcome, with clearer explanation of component parts and less reliance on process-led diagrams.
• Whether communications could be strengthened through references to existing precedents such as open banking, smart data schemes, and wider government direction of travel.

Next meeting: Thursday July 9th 2026 10:00-11:00 BST

Formal records, including attendees, are maintained by the secretariat. 

These are confidential to the Advisory Group Members.

On June 17, we’re hosting a use case accelerator workshop, exploring the user needs, market barriers, and data landscape shaping a new data sharing Scheme designed to scale Industrial & Commercial (I&C) flexibility across the energy sector.

The event features a networking lunch, short presentations, facilitated breakout sessions and Q&A discussions with participants invited to:

• Critically test the initial use case definition produced by Icebreaker One
• Explore user needs of different actors across use case (e.g. business case, value case, data needs)
• Define key roles and responsibilities within the use case
• Explore the data landscape surrounding I&C flexibility and the data needs of Scheme users
• Facilitate connections and network building

Attendees will leave with:

• A clearer understanding of the Scheme’s purpose, scope, and direction
• Practical insight into how their organisation can shape and participate in the Scheme
• Access to a growing network of stakeholders driving innovation in I&C flexibility

Outputs will be used to shape the agenda for Scheme advisory groups (Q3 2026 launch) and define the scope of an initial Pilot (early 2027 launch).

Participation from I&C energy consumers, I&C trade bodies, energy networks, energy suppliers, flexibility service providers, aggregators, and innovators are particularly welcomed.

A shared data scheme to accelerate I&C Flexibility

Open Energy is facilitating the collaborative development of a data sharing Scheme to accelerate Industrial and Commercial (I&C) participation in consumer led flexibility. It responds to a clear, under-served market need for I&C actors to be able to easily and securely exchange data between authorised parties for the purpose of assessing, planning, and implementing flexibility.

The potential benefits are significant; helping businesses unlock new value, supporting a more flexible and resilient energy system, and contributing to the UK’s Clean Power 2030 ambitions.

Meeting Aims:

1. Create a clear set of recommendations for the SG members in regards to the upcoming outcomes
2. Endorse Q3 outcomes
3. Review and comment on the new website outline

Summary:

It was agreed that:

• A website testing group should be set up, with interested members invited to register by email.
• The outcomes for the two pillars would be presented as a high level summary to Steering Group:
  • Use Cases and Datasets this quarter will focus on aligning data standards for priority use cases, so members are ready for adoption and data publishing while keeping delivery as far as possible within Stream’s control.
  • Ecosystem this quarter will focus on strengthening Stream’s visibility and stakeholder engagement, including a refreshed web presence, to support the transition toward an independent entity.

It was noted that:

• Several Q2 outcomes have been deferred because they depend on external projects outside Stream’s control, including:
  • CReDO has clarified the scope but funding confirmation is still outstanding
  • CaSTCo – a response on next steps is still pending
• APR is making strong progress in Q2
• The data portal would remain on ESRI for now, while the website front end would be rebuilt separately.

It was discussed that:

• There is a tension between Stream’s convening role, which depends on external partners, and the need to set outcomes that are within Stream’s control.
• Future outcomes may need to focus more on readiness and responsiveness, rather than on external milestones.
• Website content and navigation should better show Stream’s value, services, products, governance, and impact.
• The distinction between active use cases and retrospective case studies was useful and should be reflected clearly on the website.

Next meeting: Thursday 25 June 2026 10:00-11:30 BST

Formal records, including attendees, are maintained by the secretariat. 

These are confidential to the Advisory Group Members.

Date: Tuesday 19 May 2026 10:00-11:30 BST

Secretariat: Icebreaker One 

Meeting Aims

1. Understand the Open Data/EIR situation and agree how to work with EIR teams. 
2. Create a clear set of recommendations for the SG members in regards to the upcoming outcomes

Summary:

• It was agreed that:
  • The outcomes for the two pillars would be presented as a high level summary to Steering Group: 
    • Data Sharing Governance will focus on establishing a sector adopted, standardised data sharing governance framework and the implementation of it for one use case
    • Technology will focus on delivering the design phase of CWQM development and redesigning and deploying Stream’s new website.
• It was noted that:
  • Work relating to citizen science data with CaSTCo was reported as deferred due to delays on the project side.
  • Environmental Information Regulations (EIR) requests were perceived to be increasing across water companies.
  • The top EIR request themes for 2025 were reported as water quality and chemicals, wastewater and pollution events, assets and infrastructure, water resources, and requests relating to particular places, assets or incidents.
  • There was concern that Stream data may be generating additional requests, despite the intention that published data should reduce handling effort.
  • AI-generated requests may be contributing to increased request volume and complexity.
  • Users may have varying levels of data literacy, affecting their ability to interpret published datasets.
• It was discussed that:
  • A survey or engagement approach should be developed with EIR teams to understand whether requests are increasing, whether Stream is affecting volumes, and what support would help those teams.
  • Any survey should likely be short, standardized, and possibly complemented by face-to-face conversations.
  • Useful metrics could include request volumes, request handling times, whether requests were closed more quickly using Stream data, and whether requests were prompted by existing Stream datasets.
  • A more standardised industry approach to EIR requests may be worth exploring, while recognising that responses would still be managed by individual companies.

Next meeting: Tuesday 30 June 2026 10:00-11:30 BST

Formal records, including attendees, are maintained by the secretariat. 

These are confidential to the Advisory Group Members.

Meeting Aims 

1. Review the emergent use cases and Perseus as a proof point
2. Agree on the go to market plan and addressable market
3. How to engage FCA to turn greenwashing regulation into an opportunity
4. Ensure our communications are consistent and reflect an agreed collective position

Summary:

• It was agreed that:
  • Perseus should continue its focus on ‘doing one thing well’ in 2026: delivering 5 SME case studies by the end of 2026.
  • The Q3 meeting (July 27th) will act as a key checkpoint ahead of the November meeting to assess whether year-end case study delivery is on track.
  • ‘Perseus for Domestic’ should be explored in a contained way so it does not distract from the core SME focus.
  • SG members should increase communications activity on Perseus, with IB1 providing tailored support, with priority outreach to identified members.
• It was noted that:
  • The Perseus member ecosystem now includes sufficient scope and reach to support an addressable market of over 1M UK SMEs.
  • At least one FSP is actively developing a higher-rate savings product tied to verified carbon intensity reductions: this is an example of live commercial product development within the membership.
  • Early FCA conversations are positive. IB1 has submitted a briefing note and will continue to engage.
  • Additional DOC members with governance or data expertise are requested.
• It was discussed that:
  • Perseus could be extended into domestic property use cases, particularly aligned to warm homes, retrofit and open property data. This could provide a timely collaboration between the Open Property Data Association, Open Banking Limited, and Perseus.
  • A blurred line exists between domestic and business energy use for home-based and hybrid-working SMEs, and there will be sensitivities around data sharing.
  • Perseus supports different strategic and communications priorities, including energy security and affordability, resilience, transition planning, adaptation and cost-reduction.
  • The incentives for SMEs will vary based on their circumstances (e.g. cheaper finance, preserved access to capital, lower reporting burden, and combinations of these).

Next meeting: Monday 27 July 2026 13:00-15:00 BST

Formal records, including attendees, are maintained by the secretariat. 

These are confidential to the Steering Group Members.

Please note that throughout this consultation, Icebreaker One uses the terms Open, Shared and Closed data as defined here.

If you have any questions about our submission or require clarifications please do not hesitate to contact us via policy@ib1.org. We have omitted questions which we did not answer. 

Call for input response

Prioritisation of sectors and use cases

Through IB1 programmes and years of expertise, IB1 supports following a use case approach to data sharing initiatives. This approach centres user needs, makes a business case for the investment in data sharing, and allows for:

1. Market incentives: there must be an economic argument that policy can then amplify or mandate. If there is no financial incentive, there will be no movement.
2. Removal of transactional friction: There must be “something in it” for everyone, or at least a path to cost reduction or a new business model. Removing friction can help everyone go together: this is never solely a ‘technology problem’ (e.g. absence of a data ontology).
3. Documentation with the identified problem statement, actors and stakeholders, a clear goal, and the envisaged impact. 

Smart Data becomes effective when it is connected

In terms of prioritisation of sector, use cases requiring cross-sector interoperability and cohesion offer the greatest immediate ability to create impact, with a manageable degree of complexity involved in rollout. These use cases support private sector growth and require achievable government intervention, allowing green growth and environmental goals to be met.

User and customer needs should be identified through a robust governance process which can understand, process, and define use cases with relevant stakeholders. In IB1’s Scheme governance (standard operating procedures), IB1 emphasises the importance of having a user needs & impact advisory group which explores, prioritises, and works through use cases (including identifying users, their needs, and mapping data value chains). This process allows for the development of business, value, and impact cases and their impact on policy, businesses, and financial instruments. 

To maximise the benefits, use cases must:

• Address governance, user needs, business, social, legal, engagement and communications to ensure the solution is fit for purpose, and can be adopted by the market. IB1 observes that technical-led programmes tend to fail to gain traction or deliver against material user needs.
• Foster a community to ensure there is cross-sector collaboration. IB1 strongly recommends taking a joined up approach which is interoperable with initiatives across the economy. IB1 suggests defining relationships with adjacent bodies in the sector and beyond to enable cross sector interoperability.

For identified energy use cases, see IB1’s response to DESNZ energy smart data scheme call for evidence question 14. 

The interplay between industry and government progress in developing schemes or regulations, and how to encourage fast progress

It is important that progress toward sharing data is incentivised before waiting for one perfect data sharing solution to be built as there is demand for data immediately. 

For example, in the near-term it is unlikely that the energy data sharing infrastructure (DSI) will be suitable for all use cases, as it is currently unclear when and how non-regulated actors will be able to access data via the DSI, for what purposes, and under what assigned roles. These actors constitute major customers for connections data (e.g. heavy industry, retail, local authorities etc). While they may well be users of the DSI in future, opportunities to service these data customers in a secure, structured and well-governed manner must not be put on hold until the DSI is ready. 

As there is demand by non-regulated users for data now, there would be benefits to developing high-impact schemes in the short term that operate autonomously, but are legally and technically structured to facilitate integration with future common data sharing infrastructure. It is essential that as the government makes progress on developing schemes and regulations that they do not block valuable industry initiatives from being established quickly.

The coordination layer

To enable valuable government and industry schemes to progress quickly in parallel while remaining coherent and interoperable, IB1 strongly recommends intentional coordination of the cross-programme rules, standards, credentials and access controls that make data flow possible at scale. We recommend that responsibility for the coordination layer sits in an independent mission-locked entity that holds – or subcontracts – the sector’s Trust Framework and provides the sector’s neutral data coordination function. While different ownership options exist, industry co-ownership and co-Directorship of such a body provides a meaningful route for ensuring stakeholder buy-in and co-funding, akin to the model of Open Banking Ltd.

A neutral data coordination function must consider:

• How will schemes’ governing bodies coordinate with developments within and beyond their own scope? 
• How will this feed into goals, design choices, and definition of technical/architectural parameters? 
• How might this need to evolve over time? For example, sectoral coordination laddering up to cross-sector.
• How might Scheme development interact with overarching sector and national data/digitalisation strategies?
• How can Schemes encourage competition, markets and service creation within and across boundaries?

The coordination function requires a Secretariat to act as a neutral facilitator for participatory governance processes which can adapt flexibly to evolving coordination needs and ensure accountability. This requires:

• Strong governance processes – e.g. covering participant selection, means of input, minuting, reporting, and decision-making
  • Ability to offer tailored mechanisms where required – e.g. working groups to focus on specific sectors or data flows, or task-and-finish groups to support elements of data strategy delivery.
  • Flexible staffing, with ability to take on additional domain specialists/contractors as necessary
• Experienced administrators to execute governance processes and communicate expectations of timescales, plans, key decisions etc.
• Where required, the provision of independent chairing or facilitation services
• Dispute resolution processes, linked to existing sector mechanisms and to individual Scheme governance processes where relevant.
• Participant accountability mechanisms 
• Commitment to open publishing as a default approach (unless there is strong reason to do otherwise) 

It is vital for the coordination body to be fully independent; it cannot be nested in a body with pre-existing market functions without risking conflict of interest or transparency problems. 

Effective coordination should also be supported by monitoring in two key areas:

• Mapping of the domain(s) in which coordination is enacted in order to support effective participatory governance in an ongoing manner
• Monitoring and reporting on the outcomes of coordination activity to improve transparency and join-up with adjacent policy/regulatory goals
  • Where relevant, this may additionally include monitoring the delivery of a sector’s data strategy / roadmap.

We suggest that the above activities would require a small permanent staff to ensure continuity of process and expertise, with additional needs met via subcontracting and secondment on a time-limited basis for agile response to emergent needs (e.g. particular technical or domain expertise concerning a coordination challenge). This lightweight approach delivers the intended benefits at a reasonable cost to the bill or tax payer, supporting the general principle of minimisation outlined earlier in this response.

Finally, we propose that any enforcement powers for the coordinator can be most readily delivered via existing regulatory and legislative capabilities. This reduces cost and risk of establishing new statutory bodies.

Best practice in scheme design, including for vulnerable and other consumers, and to maximise how well the system works for services that use data from more than one sector

A core centralised capability must be the design principles. Critically, aligning on design principles for governance will lead to greater cohesion and interoperability of outcomes. 

Governance processes should collaboratively agree upon:

• The intent to work toward interoperability and working in widely understood formats. 
• Licence compatibility – creation of preemptive multilateral contracts/agreements, including appropriate permissioning where required
• Human- and machine-readable representations of scheme rules
• Adoption of common open web standards as the default (unless insufficient) to allow for widest possible number of technologists to understand
• Open publication of new specifications (legal, procedural and technical) that may be adopted by other schemes to aid interoperability
• The use of consistent tooling that is well understood by stakeholders
• Appropriate proven security standards
• The use of open source 
• Conceptual alignment on what metadata means (better yet  – technical compatibility), and aligning around standards

Within this governance function, there must be adequate consideration of the amount of communications and time needed to convene, design, implement and develop consumer messaging for schemes.

To enable interoperability, IB1 recommends considering how Schemes will interact. Key aspects of this are:

Identity. IB1 suggests this should not be a centralised identity, but a mechanism which can enable cross scheme identity verification. This is a key area of research with further needs around how a federated identity system may work. IB1 is exploring this within Perseus, to enable an identity interaction with Open Banking’s identity establishment. 

Access, licensing and permissions. There is a need to invest in research into this, as uncertainty in rights to access, use, combine, sell or share data is a drag on innovation and introduces unnecessary cost. Different regulatory environments can lead to additional confusion for cross-sector data use. There is potential to develop permissioning and purpose representations that can be understood readily by data users and their customers, but interpreted at scale by machines.

Assurance: Schemes need to address the assurance needs of data users in order to deliver value. Considerations include provenance, quality, processes, auditability, liability and redress. Protections for scheme participants (companies) and the customers they serve must be clear. A common language and machine-readable representation for these aspects of data sharing enables confident use of data and accelerates adoption.

Potential cross-sector innovation support, or data or regulatory sandbox services, and how they are designed 

IB1 recommends investment in common tooling to develop public digital infrastructure and open source support which can be re-used across schemes. 

There is a potential role for the National Data Library to curate common standards for scheme rules and their representations and convene the working groups that define them.

The places and methods through which competition should be enabled or promoted in the smart data system, and the pros and cons involved

Scheme development will be a part of the public digital infrastructure development, with appropriate governance oversight to avoid anticompetitive practices, and to guard against cartels to ensure it is a fair place to do business. IB1 thinks of this as “collaborate on the [data sharing] rules, compete on the [services] game.” It is part of the governance process to delineate what is considered pre-competitive and to have short term targeted projects (e.g. mapping stakeholders who must be consulted when developing a specific area of pre-competitive activity).

IB1 also recommends to include value-mapping guidance in the handbook (recommended approaches to do it for a scheme) and to identify and caution against perverse incentives.

Underlying trust services (for example identity, verification, compliance monitoring, permission management, version-controlled registries of scheme rules) must have open standards, ideally with Open Source reference implementations. Scheme operators should have a competitive market of trust service providers to choose from, whose services comply with these standards. The aim is to create a market that operates along the same lines as the HTTP web standard and web hosting providers. 

Methods and forums for engagement with those outside government and join-up between sector-level and cross-sector developments (such as the guidebook)

• Opportunity to capitalise on existing data sharing governance forums:
  • Perseus
  • Open Energy 
  • Stream
  • https://ib1.org/sops/governance-schemes/
• Any coordinating entity must be accountable to its stakeholders. We suggest this is supported by the following:
  • Openness policies enabling scrutiny (e.g. of methodologies, processes, minutes, reports)
  • Where required (for security purposes), clear rules defining how scrutiny will be undertaken among closed audiences
  • Defined process for dispute resolution integrated with existing sector mechanisms
  • Clear processes for change management
  • Defined avenues for external involvement in participatory processes
• Wider engagement than just the incumbents and/or regulated entities within a sector (e.g. in the energy sector this must include actors beyond the roles licensed by Ofgem)
• Cross sector convening needs to be around coherent use cases with a wide range of stakeholders representing the different roles and stakeholders within the data value chain

Join-up between smart data and other data policy, and with international partners

There are developing debates in sectors such as energy and property as to what is considered under the realm of smart data, versus what is considered ‘system data’  There is potential for some issues emerging there and in other sectors which need to be considered and worked through with the relevant stakeholders. Definitions established under the Data Use and Access Act must be respected where relevant.

It is worth noting that not all data is smart data but will need to interact with other data which could/should be shared for key use cases. We caution against excluding ‘non-smart’ data stakeholders when convening around smart data and other data policy. 

Our most prominent international partner – the EU – has invested heavily in technical infrastructure via its Gaia-X initiative. Outcomes have been mixed, due in part to an apparent assumption that “if we build it they will come”. Recent work by the Data Spaces Support Centre on design principles and governance has the promise to encourage more use cases to be brought forward and be implemented. The UK should have a goal of alignment with EU developments on data spaces, but to aim for eventual harmonisation (as with the advice on interoperability within the UK above) as opposed to full technical interoperability at an early stage. As with all data sharing work, the use case is key here. If a use case requires interoperability with EU dataspaces, or interoperability drives very high value, then it is worth the investment to align and connect. Many use cases will not require this, at least in their initial phases.

Links between smart data and AI adoption and innovation, either within the Industrial Strategy sectors or more widely across the economy. 

AI is moving rapidly from performing tasks for people (“summarise this document in under 300 words”, “tell me the top considerations when buying a new fridge”) to performing tasks on behalf of people (“deploy this software”, “find and book a reasonably-priced vegetarian restaurant in Soho for me and 3 others next Thursday evening”). To perform these tasks, agents will need to access the instigator’s personal data, and to exercise delegated authority to act on their behalf. Both of these may implicate multiple providers, using data and access that the instigator didn’t foresee.

AI and smart data intersect in governance and assurance, enabling trust in AI operation by answering questions such as: 

• Where is personal data stored and processed, and to whose benefit?
• Where did the data the model is using come from? (Both for training and for retrieval-augmented generation)
• What personal data did the model use?
• How much reliance can the user put on the inference?
• How are permissions delegated to AI, and how are consumers protected?
• How does the agent ensure that personal information is protected under GDPR when shared?

Relevant materials

Please see other relevant IB1 call for evidence responses:

• DSIT’s Smart Data call for evidence
• DSIT’s Data intermediaries call for evidence
• DESNZ energy smart data scheme call for evidence
• IB1’s response to Ofgem’s Energy digitalisation governance: architectural coordination letter

General principles

Additional comments:

• Reusability: the methodology for exploring and getting Schemes off the ground can have generic/reusable items. But the Schemes themselves must have capacity for tailoring.
• Minimisation: Schemes should do the minimum possible that enables the use case to be addressed.