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.

Data has a huge role to play in delivering net zero by 2050.

Reliable data is vital for verifying that organisations are meeting their sustainability commitments. Investors depend on it to shift their investments towards greener companies, while innovation in energy production will hinge on the smart use of data.

But despite generating huge quantities of data every day, we’re not making the most of it. Take data about companies’ emissions. It’s languishing in spreadsheets, carbon calculators, smart meters and other siloes. Even when organisations do share their emissions data, it’s generally seen as an exercise in after-the-fact reporting.

Databases of low quality, out-of-date information are not a foundation for developing new products or technologies, or unlocking new markets.

When it comes to net zero, data is everywhere, just not where we need it.

At IB1, we don’t try to bring ‘all the data into one place’, as others attempt. Nor do we host data or seek to provide analysis services using it.

Instead, we [orchestrate] [schemes] of [data governance] that enable groups of organisations to share continuous flows of well-structured, assurable data with one another.

Governance

Our focus on [data governance] is driven by the view that making data work harder for net zero isn’t a technology challenge.

Rather than a deficit of data or technologies to manage it, it’s a deficit of effective processes for groups of organisations to come together, cooperate on and set the terms of data sharing that’s really holding us back.

“Incentives in our organisations and society prompt us to beaver away on our own. Collaboration is the catalyst of innovation, [but] we often struggle to practice it when it comes to overcoming complex challenges and making efforts towards positive social progress”. – Miranda Dixon, Brink

As with other collective action problems, only good governance can align fragmented interests, enable collaboration and facilitate shared investment.

In our work, governance is an ongoing process. The execution of this process produces decisions that enable data sharing to take place. In practice, this involves establishing principles, defining clear roles and responsibilities, and agreeing priorities and tasks. It also involves collaborating to create artefacts to express and enforce these decisions, such as legal agreements and technical standards.

We have a particular approach to organising data governance at IB1. We use a tiered system of Steering, Advisory and Working Groups to bring organisations together. These groups work together to agree and adopt:

• User needs & impact: commercial priorities, business cases, and prospective new products and services.
• Technical infrastructure: shared ontologies, APIs, schemas and standards to support data exchange. 
• Licensing & legal: data sharing agreements, modes of redress and liability frameworks.
• Engagement & communications: common language, stakeholder engagement and recruitment.
• Policy: alignment with corporate policy and industry regulations.

Participation in this process can be either voluntary (initiated by the market), or mandatory (demanded by regulators). 

Our approach is inspired by the UK’s Open Banking ecosystem, which enabled data to be shared in new ways across banks and other financial services. It now has 10 million users and is projected to sustain a $12bn market of data-driven products and services. This change has been achieved not by building a big, centralised database of customer banking data, but by governing who should access it and how it should flow. 

Schemes

As well as neglecting governance, attempts to build databases of net zero data fail because they try to be all things to all people.

In a 2024 talk, Building scalable public data sets for scientific innovation, John Wilbanks described how effective data systems generally begin life by addressing a small set of very specific primary uses, before evolving to enable more over time:

“No one has ever built a complex data system by setting out to build a complex data system [from day one]. You build one by answering five questions at a time, using a standards based approach… And then when you’re able to answer twenty, you’ll have a functioning complex data system”.

We agree that specificity is a necessary condition for effective data sharing. We enable groups of organisations to come together around tightly-focused challenges or use cases related to net zero, which we refer to as [schemes].

Our flagship scheme, Perseus, enables small-and-medium sized businesses to share granular emissions data from their smart meter systems with banks and other lenders. By providing lenders with the accurate and assurable data they need, the scheme enables participating businesses to access loans and other finance to help reduce their emissions.

Perseus isn’t trying to cast a net around all sustainability data, or work for every company. It demonstrates how good governance—anchored around a very specific goal —can unlock data from the real economy and put it to use for net zero.

Orchestration

We don’t have a monopoly on this view of data governance. But we think groups of organisations can go further, more quickly with our [orchestration].

We provide and maintain the following Trust Services to enable schemes like Perseus to function:

1. A machine-readable rulebook that codifies how data can be shared within the scheme.
2. An approach for verifying which organisations can take part in the scheme.
3. An open directory of the organisations that have been verified to take part in the scheme.
4. An approach for monitoring and assuring that access to data within the scheme adheres to the agreed rulebook.
5. An open catalogue of the data that is made available within the scheme.

None of the services we provide rely on particular software or a singular technology vendor. What we deploy depends on the needs of the scheme. Data access can be enabled by API, more advanced privacy enhancing technologies… even fax machine. (Although we wouldn’t recommend the latter.) What’s important is that the solution meets our NOVA principles: a Networked, Open, Verifiable Architecture. 

Our non-profit status is another key element of this work. There’s a risk that the direction of data use will be dictated by commercial actors, if schemes are left to the market alone. Our approach at IB1 ensures that no individual or entity can take disproportionate control of net zero data, and that end user needs rather than organisational agendas drive progress.

We’re glad the importance of this orchestrating role is now being recognised. A recent analysis of ‘data spaces’ being built across the European Union found that successful efforts have an independent organisation at the centre:

“It is crucial to have a neutral orchestrator facilitating the exchanges between participants before the operations and governance of a data ecosystem solidifies.

The orchestrator should prioritise use cases, map business value creation, test business models, and set up governance models. During the operation phase, the focus will shift toward onboarding, enforcing the rules, ensuring the governance works as it should, and scaling up”.

Infrastructure for real progress

Making data work harder for net zero ultimately depends on trust, coordination, and infrastructure that works across organisations. 

Ever-bigger, centralised databases won’t get us there. What we urgently need are well-orchestrated schemes of data governance that enable decision grade data to flow.

But making this shift requires resources—and partners.

If you’re working along the same lines, or if you’re looking to fund the infrastructure that underpins real progress on net zero, join us.

Reach out via:  icebreaking@ib1.org

Given the many conversations I’m in at the moment I believe it very important to have as much debate as openly as possible, as this impacts everyone.

Here’s a question:

How should the UK implement its National Data Infrastructure so that it provides consistent control, sharing, and security for data, much like other public infrastructures such as roads and utilities?

“Everything should be as simple as it can be, but not simpler”
[often attributed to Einstein]

Having initiated ‘data as infrastructure’ at a political level in the UK in 2013, I’ve been watching and learning on this for a long time. We’ve made remarkable progress in some areas, less so in others.

Let’s explore, starting with some lessons learned.

One immediate reflection is that as over 60 countries moved to copy the Open Banking Standard, those who took only ‘parts’ of the system tended to experience less ideal outcomes (to the point that they often add them back in later to help course-correct — most notable are those who thought this was ‘just a tech problem’ and only took the open API principles).

We can look to past UK data initiatives like MiData and Open Banking and potentially infer that voluntary-only initiatives and centralisation do not work, and that the decentralised nature of Open Banking was more successful. However, these are shallow reflections.

We can point to three factors beyond centralised or voluntary which are dependent on the use case:

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 (regardless of central vs decentralised, mandate or not — mandates are also often ignored). Midata was mostly tech-led, Open Banking was use-case led.
2. Removal of friction: Removing transactional friction may seem like something everyone wants, but not if your current business model relies on it. 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 a ‘technology problem’ (e.g. absence of a data ontology).
3. Mandates: if the friction is ‘too high’, regulatory intervention is necessary to mandate participation and move the market. However, if the market can demonstrate self-initiation, then ‘endorsement’ from government can be sufficient to drive impact. Value creation must be visible in both cases.

I believe that we need to embrace these factors in our systems design.

We need to:

1. work out where the lines are drawn between central, pre-competitive and competitive areas;
2. ask what the role of regulation is, and at what granularity
3. query how things can be implemented in a way that humans (and machines) can and want to adopt
4. ensure those governing, operating and participating can be helped in their assessments of compatibility and interoperability
5. work out how to prioritise and create clear (and stable) roadmaps that enable investment to be made

I believe that we can build on the success of sector-specific approaches:

The Open Banking model’s success lies precisely in its sector-specific, incremental approach. It focused on the unique needs of banking, where security, standardisation, and customer trust were paramount.

Copying the Open Banking model into other sectors (e.g., energy, transport, health) isn’t about duplication but about applying tested frameworks tailored to each industry.

Without considering the market incentives for participation we risk techno-utopian thinking

Instead of trying to create a complex, overarching architecture that risks being too broad, a sectoral approach allows for flexibility and adaptation to industry-specific challenges. This carries a risk of fragmentation, and equally a potential to mandate cross-sector interoperability without defining exactly what this means in detail for everyone. The risk of push back on over-reach is material and catastrophic (in terms of adoption, if it fails). Further, market-incentives will be market-specific.

Avoiding Over-Standardisation

National Data Architecture risks over-standardisation and could stifle innovation instead of enhancing it. Different sectors have wildly varying levels of data maturity, hugely variable data governance needs and challenges, and a one-size-fits-all approach is highly likely to impose unnecessary constraints on sectors that are not as ready for them.

For example, imposing the level of data governance on the industrial sector that is required in the financial sector is (today) not realistic. We are, however, on the frontline of that journey with ESG reporting today, and it highlights the complexity of that real-financial sector bridging. Incremental improvements, like extending Open Banking principles to energy or transport can allow industries to evolve organically while keeping governance controls and security as a priority.

Pragmatism of the Incremental Approach

The incremental, domain-by-domain approach may seem less ambitious, but it offers a practical pathway to innovation that can be adopted today. Trying to design a top-down, comprehensive national architecture from the outset risks paralysis by analysis and over-planning. Learning from Open Banking and rolling out smart data initiatives sector-by-sector allows for continuous improvement and responsiveness to real-world feedback, which may be more effective than a sweeping, coordinated architecture.

Interoperability Requires Industry Engagement

Interoperability relies on industry engagement. This is best fostered through practical, sector-specific initiatives like Open Banking. Imposing an overarching system from above risks of alienating key industry stakeholders, who may resist changes that threaten their business models (this happens in every sector already, today). Open Banking’s success came from the balance of regulatory oversight and industry cooperation, a formula that can be replicated in other sectors.

Market Failures Can Be Addressed Incrementally

There are (always) risks of market failures such as data hoarding (castle & moat is still the prevalent investment model for data businesses). These issues should be tackled incrementally through sector-specific mandates rather than a top-down architecture, but can be accelerated with top-down principles.

The competition fostered by Open Banking and similar initiatives already shows promise in tackling data monopolies. An incremental, practical approach allows policymakers to address market failures as they emerge, rather than trying to solve all potential problems with a one-time top-down design.

Trust is Earned, Not Imposed

Trust in data systems cannot be imposed from the top. While Open Banking was catalysed by regulation, it earned user trust through transparency, security, and gradual adoption, not through a top-down imposition. Similarly, rolling out frameworks sector by sector allows consumers, businesses and citizens to see the benefits and gradually gain confidence in the approach. Attempting to ‘mandate trust’ top-down could backfire, especially in sectors like health, where data privacy concerns are particularly sensitive.

At IB1, our Trust Frameworks are one tool (of many) that can help foster multilateral collaboration, building on Open Banking principles.

Decentralisation Encourages Innovation

The decentralised, minimised and pre-competitive nature of Open Banking has led to its adoption and innovation. A large, centralised approach would have risked locking in certain standards or technological pathways that could hinder future innovations. Allowing sectors to develop their own interoperable standards fosters a more competitive, flexible landscape, where innovations in one sector can inform developments in another, without being dictated by a single framework.

Data governance should aim to balance impact on rights and security
while minimising its own footprint and reach

While the idea of a unified, overarching data framework may sound appealing in theory, in practice it would risk being too rigid, slow to adapt, and detached from the unique needs of individual sectors. A more pragmatic approach — building on the lessons of Open Banking and extending these principles to other industries — may offer the best balance of innovation, security, and user control.

Please feel free to comment, or message me (via LinkedIn or directly)

Disclosure: I sat on the MiData energy sector board (mostly tearing my hair out on these points); co-chaired the creation of the Open Banking Standard (through which I learned so many lessons it could fill a book or two); was founding CEO of the Open Data Institute; am co-Chair of the Smart Data Council, and run this non-profit (IB1.org) working on data governance at sector and national scale.

Thanks to Chris, Frank, Paul, Hadley and others for their feedback and inputs.

Useful links

Lessons learned from Gaia-X Data Spaces https://www.sitra.fi/en/articles/eight-lessons-from-building-data-spaces/

ODI on Data Institutions 
https://theodi.org/insights/projects/rd-data-institutions/

Icebreaker One on how it implements data sharing 
https://ib1.org/what-we-do

Open Banking (implementation entity)
https://openbanking.org.uk

Additional narrative on cities
https://agentgav.medium.com/the-porous-city-92ae986cd43c

Other opinions
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