Policy paper

Data asset management policy in government

Published 27 May 2026

The UK government wants to use data to its full potential to:

• deliver better public services
• improve operational efficiency
• enable economic growth

Public sector organisations must recognise data as valuable assets for government and the UK. To do this, we must first understand what data we have, where it sits, and ensure it is appropriately managed.

This data asset management policy sets out the requirements for UK government departments and their arm’s length bodies (ALBs) to identify, record and centrally report data assets. The policy also sets out minimum requirements for quality, discoverability and interoperability, as well as defining clear data ownership.

Together, these requirements introduce a cross-government framework to identify critical data assets and bring them under minimum standards. These standards are essential to enable safe data reuse, support AI readiness, improve quality and reduce costly duplication across departments.

Realising our data ambitions

Better use and management of data is essential to improving public services, boosting economic growth, underpinning groundbreaking research and harnessing the opportunities of AI. 

This policy establishes the ownership, standards and life cycle controls needed to treat data as a strategic asset in a unifying way, supporting all strategic outcomes. Treating data as an asset means recognising it as core public sector infrastructure that must be properly valued, invested in, managed, shared and reused at scale. Clear ownership, stewardship, quality assurance and risk controls enable a smarter state, joined-up public services, and support better decisions about investment.  

This policy enables the Government Digital Service (GDS) to be a strong centre that works collaboratively with the public sector to set the guidance and standards to: 

• increase value of data to government through improved documentation, interoperability and knowledge of data sources across the public sector, reducing the barriers to sharing and use of data across government 
• improve the quality and interoperability of data to increase ease of use across the public sector, opening up new opportunities to use and combine data sets, thereby increasing the value of data 
• consistently identify and report government data assets as the foundation for developing better data infrastructure and platforms, which will make it easier for the public sector to find the data sources they need and reduce costly duplication of data-related activities across government, such as data collection 
• reduce transaction costs in public sector data by tackling the root causes of its frictions, such as finding data, standardised metadata to understand the data, and tackling data quality issues to reduce the need for cleansing data 
• facilitate the deployment of more effective and more advanced AI tools which can ingest and analyse data from multiple sources to deliver better outcomes 
• facilitate service transformation across government, allowing services to avoid repetitive collection of data already held in the public sector, allowing forms to be automatically populated, and enabling different systems to talk to each other to allow for more comprehensive service redesign 
• enable better understanding of our critical data assets and cross-public sector dependencies so we can better manage risks 

The policy creates the conditions for safe reuse at scale, more efficient service delivery, improved AI readiness and a more coordinated, system-wide approach to delivering public value. 

Defining a data asset  

The term ‘data asset’ refers to a collection of one or more data sets and, where relevant, the data services used to access them. 

A data set is a structured collection of data selected and organised to meet a defined outcome or need. A data set does not need to include all data held in a system or database; it may represent a defined subset of data where that subset forms the meaningful asset for use or reuse.  

A data service provides a controlled means of accessing one or more data sets (for example via an interface, API or secure access mechanism). For example, a government department may hold extensive operational data on businesses for licensing, compliance and enforcement purposes. This information can include hundreds of fields, many of which are used only internally for case management. From this broader collection, the department defines a specific subset – such as a list of businesses with their unique identifiers, licence status, renewal dates and authorised activities – as a data asset. 

This subset is published through a controlled data service and used by other departments and regulators to support risk-based decision making and reduce duplicate checks. Only the fields required for that cross-government purpose form the data asset, even though they originate from a much larger internal system. 

Data assets in scope 

This policy defines clear criteria for organisations to identify the assets within their scope, with a focus on those that are considered critical across government. The identifying and recording critical data assets guidance sets out a detailed definition of data assets in scope and will be reviewed and updated regularly to align with evolving government objectives. 

Only data assets classified at OFFICIAL fall within the scope of this policy, including additional markings and descriptors (for example ‘personal’). Assets classified as SECRET or TOP SECRET are excluded. 

This policy does not: 

• force an organisation to share data 
• remove any legal, data protection or regulatory obligations and safeguards related to data sharing and access 
• require the data provider to give any guarantees about ongoing availability and supply or quality of the specified data beyond terms previously agreed 
• apply to data beyond the scope of the provider’s data ownership accountabilities and responsibilities

Identifying and reporting data assets 

All UK government departments and ALBs (it is optional for public corporations) are required to identify and manage metadata about the data assets in scope of this policy within their data catalogues as well as report them to GDS. 

Departments must also provide metadata about their identified data assets and coordinate and assure returns for their ALBs which must be maintained and reviewed on an annual basis.  

The recording metadata to describe critical data assets guidance sets out the core metadata requirements. GDS will develop a service to make metadata about data assets discoverable within government.  

Data ownership 

All identified data assets must have a named data owner to ensure that there is an appropriate individual accountable for making decisions about the data as mapped in the data ownership model. The model maps out and compares the roles and accountabilities for data owners and information asset owners, and provides options for how to comply with the model.  

Data quality action plans 

All identified data assets must have a data quality action plan (DQAP), or equivalent, to ensure there is a mechanism in place for managing its quality. Understanding data quality is a crucial component for AI readiness, and is a critical component for the government and departments to realise value from AI adoption.  

The Government Data Quality Framework recommends using DQAPs as a tool to identify and understand the strengths and limitations of a data asset, and to support prioritisation decisions when deciding where to invest resource to improve quality.  

To support implementation, GDS has developed a DQAP implementation guide . The guide sets out 7 steps for creating and maintaining a DQAP throughout a data asset’s life cycle, and provides templates that can be used and tailored according to need. A data quality issues framework  is also available to support resource allocation decisions. Where departments already have a mechanism in place to manage the quality of their critical data, they should review this to ensure the process covers all steps laid out in the DQAP implementation guide. 

Organisational and administrative scope 

This policy applies to all UK government departments (not including councils) and ALBs (it is optional for public corporations). These include: 

• executive agencies 
• non-departmental public bodies 
• non-ministerial departments 

Government departments are responsible for coordinating returns from their ALBs. Departments should work in partnership with their ALBs to monitor the completion of, and assure returns to, GDS. Departments and their ALBs are aligned in departmental plans, delivery of services and shared objectives. This means it is important that they coordinate and are consistent in the identification of data assets. 

Due to devolution, the Northern Ireland Executive, Scottish Government and Welsh Government have their own approaches to data sharing governance. The implementation of this policy is optional for local government. There are many benefits to aligning data sharing governance across the UK. Administrations of the UK must continue to share good practice and learn from each other – particularly because delivering public services depends on the underlying process of sharing data. 

Data is required for essential purposes between the devolved administrations and the UK government. Data provided by central government departments and their agencies to government organisations outside of scope, but where other criteria are met should have the designation applied. For example, where data is provided by a  government department to a devolved administration, and where this data aligns with the criteria in the identifying critical data assets guidance, the data asset should be in scope of this policy. 

Oversight and implementation of the policy 

The Chief Data Officer Council will have oversight for the implementation of the policy. The Chief Data Officer for each organisation in scope will be responsible for the implementation of the policy in their organisation. The Minister for Digital Government and Data will write to ministers on an annual basis on the progress being made to ensure compliance with this policy is appropriately prioritised and driven at the highest levels of leadership within government bodies. 

Recognising the variances in data maturity across organisations and resourcing challenges they face, GDS will work in partnership with the organisations in scope to prioritise the identification of data assets with proven reuse value and provide the opportunity to discuss and target further support from GDS.