Chapter 1: The current data sharing and linkage landscape across government

An emerging theme from our stakeholders was the overall willingness to share and link data across government and public bodies. The benefits and value of doing this are widely recognised. There is, however, still a wariness around the legality and ethics of data sharing and linking as well as many different processes and ideas around how it should be achieved, which are causing delays.

The picture is not the same in every area of the government. Some areas have moved faster than others and we have found that culture and people are key determinants of progress. Throughout this report, we highlight examples of data sharing and linkage that demonstrate a positive impact for the public good. Through sharing these examples, we hope to enable others to see how barriers can be overcome to take positive action.

To bring out the findings from our interviews, we focus on ‘themes’ and how these themes were spoken about in the context of both barriers and opportunities. This helps bring out different views and opinions and means there is more opportunity for capturing the complex nature of the landscape. The themes we identified focus on:

  • Public engagement and social licence: The importance of obtaining a social licence for data sharing and linkage and how public engagement can help build understanding of whether/how much social licence exists and how it could be strengthened. We also explore the role data security plays here.
  • People: The risk appetite and leadership of key decision makers, and the skills and availability of staff.
  • Processes: The non-technical processes that govern how data sharing and linkage happens across government.
  • Technical: The technical specifics of datasets, as well as the infrastructure to support data sharing and linkage.

Public engagement and social licence

One of the biggest topics mentioned throughout our interviews was the need for more public engagement about data sharing and linkage. Many of our interviewees made a connection between lack of understanding of public perception and the nervousness that still exists around data sharing among some senior leaders, who are concerned by the potential for public resistance. The Office for Statistics Regulation (OSR) agrees with the need for further public engagement, seeing that public engagement serves two important purposes: firstly, to understand and, secondly, to potentially increase the amount of social licence a data sharing or linkage project has.

We have also found through our interviews that the data owners link their perception of social licence for a data sharing or linkage project to data security. In this section, both public engagement and data security will be discussed in the context of social licence. While we sometimes refer to ‘the public’, we acknowledge that there are different groups that sit underneath this, and it is often useful (and indeed necessary) to engage with specific groups depending on the topic area and intended outcomes of individual pieces of research.


Public engagement

Our interviews revealed a consensus that those working on data sharing and linkage need to prioritise public engagement in their work, to improve both transparency of work that is being carried out, and public confidence in data sharing and linkage more generally. Within this, there was recognition that consideration should be given to which groups of society are most important to engage with for specific projects or initiatives. There was acknowledgement, however, that there can be a lack of understanding about how to do public engagement effectively, especially among academic communities.

We also heard how, currently, agreement to data sharing by members of the public can be context-specific and dependent on who is sharing data, as well as data use. This was also reported in the Public attitudes to data and AI: Tracker survey published in March 2022 by the Centre for Data, Ethics and Innovation (CDEI). The CDEI found that organisations working on health, particularly the NHS, were most likely to be trusted by those sharing their data, with government and third sector organisations also generally preferred over private companies, where concern over data use can be greater. The purpose of data use was found to influence people’s decision-making in all use-cases. This relates to another point raised with us during our interviews about miscommunication, and how it is often not made clear that data sharing for research largely involves de-identified data and that data are not going to be sold for commercial purposes. These findings and reflections demonstrate that clear and consistent communication about what is being used, and for what public benefit, are vital to gaining buy-in and avoiding a negative public response.

Finally, we heard that there is an expectation by some within the public that their data are already being shared across the public sector for the public good. This was highlighted recently in a blog by Data and Analytics Research Environments UK (DARE UK) (see Box 3) which explores the growing evidence that people want and expect data to be used for good when it is done securely and transparently. DARE UK reference their public dialogue work as evidence of this, and further examples of this type of work can be found in the case studies below. This growing evidence shows that by not sharing data, the government may be doing the opposite of achieving public good in the eyes of the public themselves – at least in certain use cases. This was also demonstrated in our own research. To understand more about public views on how public good can be served by data for research and statistics, OSR collaborated with Administrative Data Research UK (ADR UK) to carry out qualitative public dialogues with members of the public across the UK. The findings from the 68 people who participated showed strong support for data sharing, provided that best practice safeguarding is used, and participants had concerns that the missed use of data, from not sharing data, could be harmful to the public good.

Given the need for public engagement and the remaining challenges outlined above, we welcome the work of the Public Engagement in Data Research Initiative (PEDRI). PEDRI is a new sector-wide partnership looking to bring together organisations who work with data and statistics to collaborate and embed meaningful public involvement across the data ecosystem. One of its first areas of focus is to embed best practice guidance and principles for public involvement and engagement that are specific and fit for purpose for those working in data research and statistics. This initiative could strengthen the public engagement landscape, sitting alongside other existing centres/initiatives that already support specific communities. These include the National Co-ordinating Centre for Public Engagement, which provides guidance for universities on how to plan, fund and deliver public engagement activities, and the work being taken forward by Department for Health and Social Care (DHSC), outlined in its Data Saves Lives policy paper, to “develop a standard for public engagement, setting out best practice for health and care organisations, and any other body using NHS data, to engage appropriately with the public and staff across the system on data programmes and issues”.

The case studies below illustrate examples of where public engagement is being done well within the public sector and how it can inform greater understanding of social licence.

Information Box 3: Data and Analytics Research Environments UK (DARE UK)

DARE UK is a programme which aims to design and deliver a coordinated and trustworthy national data research infrastructure to support cross-topic linkage and analysis for public good. DARE UK is funded by UK Research and Innovation and puts public engagement at the heart of its work.

Case Studies: Public engagement for the public good

Secure Anonymised Information Linkage (SAIL) Databank

About: SAIL Databank is a trusted research environment (TRE) that enables research communities to access, link and analyse routinely collected population and health data within a safe and secure remote access environment.

Public engagement: SAIL Databank make regular use of their Consumer Panel which is made up of members of the public. All users of SAIL Databank can access the Consumer Panel to explore any questions they have, including feedback on research ideas, views on data protection issues and ideas for presenting findings to a public audience. Crucially, the public are involved from design to output stage of a project process for projects that are likely to have high public interest. One such project is the evaluation of the Carmarthenshire social housing initiative. This was a complex longitudinal study of the impact of improving social housing on health outcomes conducted in collaboration with Carmarthenshire County Council and members of the tenants association. Members of the SAIL Consumer Panel and local tenants associations were recruited at the beginning of the research, helped with its design and implementation and were involved in the dissemination of the results to local and national groups, including sharing a stage with Wales’ First Minister.

When assessing research proposals, Research Ethics Committees (REC) look favourably upon proposals that incorporate public involvement at an early stage as this is good evidence that the research is ethically sound and in the public interest.

Thames Valley Together Project

About: The Thames Valley Violent Reduction Unit (TVVRU) was created in 2019, with funding from the Home Office (HO), to tackle the root cause of serious violence across the Thames Valley region through earlier intervention and prevention. A priority focus is the development of the first multi-agency data-sharing and analytical platform, called Thames Valley Together. It is a collaborative solution with a focus on data sharing across local authorities, health, education, policing and third sector organisations to enable response to risk factors at an individual and population level.

Public engagement: The TVVRU team have done great community engagement with young people across Oxfordshire. They held a first data ethics deliberative forum in November 2022 to consult young people on whether data should be used and, if so, how it could be used to make earlier interventions to prevent violence. The students had to consider the pros and cons of using data – thinking about issues such as privacy versus safeguarding, the need to support the most vulnerable, consent and why different agencies may need to share information with each other.

Better Outcomes through Linked Data (BOLD) Programme

About: BOLD, led by the Ministry of Justice (MoJ), is a three-year cross-government data-linking programme which aims to improve the connectedness of government data in England and Wales. It was created to demonstrate how people with complex needs can be better supported by linking and improving the government data held on them in a safe and secure way.

Public engagement: The MoJ’s BOLD programme partnered with the Centre for Data Ethics and Innovation (CDEI), to undertake extensive engagement with affected groups. This included focus groups with people with complex needs, to inform the development and governance of the programme. The full report on this work can be found here.

During this work they found that participants had concerns about how data linking will be done safely and appropriately but this eased when anonymisation of personal data was explained. Participants also wanted clarity around areas such as consent, and assurances that their data will be kept safe and anonymous in the future. Both findings demonstrate the importance of transparency and of providing a thorough explanation to data subjects of what is being done with their data and exactly what data will be used.

The same research also found that participants could see how data sharing could improve public services, which they felt was a worthwhile aim.

Recommendation 1: Social Licence

The government needs to be aware of the public’s views on data sharing and linkage, and to understand existing or emerging concerns. Public surveys such as the ‘Public attitudes to data and AI: Tracker survey’ by the Centre for Data, Ethics and Innovation (CDEI) provide valuable insight. They should be maintained and enhanced, for example to include data linking.

Recommendation 2: Guidelines and Support

When teams or organisations are undertaking data sharing and linkage projects, there is a growing practice of engaging with members of the public to help identify concerns, risks and benefits. To help teams or organisations who are undertaking public engagement work, best practice guidelines should be produced, and support made available to help plan and coordinate work. This should be produced collaboratively by organisations with experience of this work for different types of data and use cases and brought together under one partnership for ease of use. We consider that, given its current aims, the Public Engagement in Data Research Initiative (PEDRI) could be well placed to play this role.

Data Security

As shown in the public engagement for the public good case studies in the previous section, ensuring and demonstrating data security is important to gaining social licence for data sharing and linkage. When we spoke to organisations and individuals working to ensure data security three topics were prominent in the discussions: trusted research environments (TREs), the Five Safes Framework and Privacy Enhancing Technologies (PETs).

Currently, a common way to make data accessible for sharing is to put it into, and make it accessible from, one or more TREs. TREs are highly secure and controlled computing environments that allow accredited researchers access to data securely stored on their servers. Government data may be held in one, none or many of these. Given data within TREs are only accessible to accredited researchers and the data flows in and out are strictly controlled, the level of security within a TRE is high. DARE UK is currently working to establish the next generation of TREs, which aim to enable fast, safe and efficient sharing, linkage and advanced analysis of data. The Integrated Data Service (IDS) is another example of a TRE currently in development across government.

The Five Safes Framework is a set of principles employed by data services, such as TREs, that enable them to provide safe research access to data. The principles of safe data, safe projects, safe people, safe settings and safe outputs are voluntarily adopted by most of the TREs and this Framework was highly praised by most we spoke to as an effective tool for ensuring the security of a data service. We did, however, hear the view that, since the Framework was developed twenty years ago, assurance that it is still able to deliver the appropriate level of security would be welcome, considering the new technologies being used to share and link data, and the increased complexity of data linkage that is occurring. There was also acknowledgement that use of the Framework is self-regulated by organisations employing it, with no overall regulator, which was concerning for some.

PETs are newer technologies that can help organisations share and use people’s data responsibly, lawfully and securely. This could be by minimising the amount of data used, or by encrypting or anonymising personal information. A recent report on PETs published by the Royal Society in conjunction with the Alan Turing Institute, identifies steps to realise their benefits and their role within collaborative analysis and data governance. It describes PETs as “an emerging set of technologies and approaches that enable the derivation of useful results from data without providing full access to the data”. Synthetic data are one example of a PET. Synthetic data are data created from the original data but changed in a way that preserves the characteristics of the original data while protecting the personal or sensitive information present within them.

There is growing interest in PETs and the potential benefits their use across government (and internationally) could bring. Together with the US, the UK (led by CDEI and Innovate UK) has recently announced winners of the first PETs Prize Challenges; the challenges inspired innovators in the UK and the US to build solutions that enable the collaborative development of artificial intelligence (AI) models while keeping sensitive information private. The United Nations has also established the UN PET Lab, a collaboration of National Statistical Offices (including the Office for National Statistics (ONS), represented by the Data Science Campus) and technology experts exploring how PETs can make fully compliant data sharing between international organisations possible. In November 2022, the ONS Data Science Campus came in the top three at a UN Pet Lab hackathon that was devised to increase awareness of PETs and their potential for use by organisations to allow data access for tackling important societal and economic questions.

In his recent Pro-innovation Regulation of Technologies Review: Digital Technologies, Sir Patrick Vallance, the then Government Chief Scientific Adviser, recommends that “Government should also consider the potential use of other privacy enhancing technologies or data intermediaries as low risk options for the exchange of data…”; OSR supports this recommendation.

Recommendation 3: The Five Safes Framework

Since the Five Safes Framework was developed twenty years ago, new technologies to share and link data have been introduced and data linkage of increased complexity is occurring. As the Five Safes Framework is so widely used across data access platforms, we recommend that UK Statistics Authority review the framework to consider whether there are any elements or supporting material that could be usefully updated.

Recommendation 4: Privacy Enhancing Technologies

To enable wider sharing of data in a secure way, government should continue to explore the potential for Privacy Enhancing Technologies (PETs) to be used to enhance security and protect privacy where data are personally identifiable. The Office for National Statistics (ONS) Data Science Campus is well placed to lead and coordinate this work.


At every step of the pathway to share and link data, the people involved, and their skills and expertise, are instrumental to determining whether projects succeed or fail. We heard examples of departmental barriers becoming unblocked when new people arrive showing how many can be overcome simply with a new motivation, knowledge or skill. For this reason, the topics raised in this section influence many of the other topics discussed.

Within the UK government, every department has an Accounting Officer who is responsible for its day to day running as well as the department’s budget. For most departments, this role is taken by the permanent secretary, but this is not the case for non-ministerial departments or non-government organisations. For this reason, we will refer to the most senior member of an organisation as the ‘Accounting Officer’.


We found that the biggest barrier to data sharing and linkage for some organisations is whether it is a priority for the Accounting Officer. priorities outlined for an organisation by its Accounting Officer are extremely influential as they determine the priorities of other leaders within the organisation and those responsible for enacting data sharing. We heard that different Accounting Officers have different risk appetites for data sharing, which feed into these priorities. Risk appetite can be influenced by the difference between the potential benefits and potential costs of data sharing and linkage, while the benefits may be diffuse, if something goes wrong, the effects can be very close to hand and potentially very difficult for individual organisations. This links to the view we heard that very senior leaders are more likely to focus on the risks associated with sharing data, rather than the risks of not sharing data, and points to the need for a more centralised assurance approach, to help overcome reservations of individual agents.

As well as Accounting Officers, we also heard that other people within organisations who are responsible for data access, such as data owners, can have varying levels of understanding of the challenges and opportunities associated with making more data available and accessible via data linkage. This can hamper efforts made from those working at other levels to convince senior leaders that data sharing is in the public good and make the process a lottery depending on the prior experience of those in leadership.

Those in very senior data and analytical roles across government, such as Chief Data Officers and Directors of Analysis, have a big role here in championing the public benefit of sharing and linking data to their Accounting Officers and other senior leaders, and in identifying areas of success both within and outside their organisations, which can be used to demonstrate feasibility. Chief Data Officers have a particular responsibility to fulfil this role and take decisions in a way that finds balance between maintaining a focus on data security and not urging unnecessary caution. Data Protection Officers can support them in this.

A Chief Data Officer (CDO) is normally responsible for organisation-wide governance and use of information as an asset.

Data Protection Officers (DPOs) assist public authorities or bodies to monitor internal compliance, inform and advise on data protection obligations, provide advice regarding Data Protection Impact Assessments (DPIAs) and act as a contact point for data subjects and the Information Commissioner’s Office (ICO).

Making secure data sharing and linkage a strategic priority at the level of the Accounting Officer in more organisations would enable better joined up approaches across government. For this to happen, an appreciation of the potential benefits of data sharing and linkage for the public good needs to be more widely held across Accounting Officers. Sir Patrick Vallance, previously the Government Chief Scientific Adviser, provides an example of what strong leadership can look like: in his review Pro-innovation Regulation of Technologies Review he urges leaders to “prioritise wider data sharing and linkage across the public sector, to help deliver the government’s public services transformation programme.” It would also be useful to have a clear arbitration process to help resolve differences in opinion between organisations about whether data can or should be shared, due to differences in risk appetite, priorities or understanding.

Recommendation 5: Data Literacy in Government

To gain the skills to create and support a data-aware culture, it is important for senior leaders to have awareness of and exposure to data issues. One way to raise awareness and exposure would be for senior leaders to ensure that they participate in the Data Masterclass delivered by the ONS Data Science Campus in partnership with the 10 Downing Street (No10) Data Science Team.

Recommendation 6: Data Masterclass Content

The Data Masterclass could expand its topics to include sections specifically on awareness of data linkage methodologies, the benefits of data sharing and linkage and awareness of different forms of data. This would fit well under the Masterclass topics of ‘Communicating compelling narratives through data’ or ‘Data-driven decision-making and policy-making’.

Recommendation 7: Arbitration Process

To facilitate greater data sharing among organisations within government, a clear arbitration process, potentially involving ministers, should be developed for situations in which organisations cannot agree on whether data shares can or should occur. Developing such an arbitration process could be taken on by the Cabinet Office, commissioned by the Cabinet Secretary and delivered working with partners such as No10 and ONS.

The Digital Economy Act amended the Statistics and Registration Service Act 2007 to provide the UK Statistics Authority (and ONS as its executive office) with greater and easier access to data held within the public and private sectors to support the statutory functions of the Statistics Authority in the production of official statistics and statistical research. As such, ONS is well placed to help deliver Recommendation 7.

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Skills, knowledge and recognition

Across the UK, there is a huge demand for data roles such as data engineers and data analysts, not just in the public sector where the National Data Strategy is promoting a world-leading data economy, but also in the private sector. Recruiting people with the skills needed to link, maintain and analyse data was a significant challenge raised by many of our interviewees. Demand is outstripping supply of data skills in the UK and this is seen to be worse in the public sector as pay is often not as attractive as the private sector.

As well as recruitment, there is also a problem with retention. Retention is a particular problem for data linkage as specialist knowledge of a dataset is often held by one or two individuals, which then takes time for new staff members to learn. We have heard that staff regularly move between government departments for the opportunity of better pay as civil service pay scales differ from one department to the next for the same grade. This is exacerbated by the uptake of additional pay rewards for certain roles, such as the Digital, Data and Technology (DDaT) Pay Approach, by some departments but not others. This can cause further pay inequality across government as it allows some departments to pay bonuses on top of standard pay. Pay is not the only reason for retention issues, however, we heard that career development in data roles is not always prioritised within government, which can force those wanting to build their career to leave government altogether. We are aware of at least two career frameworks for data roles within government, the DDaT career framework and the Government Analysis Function career framework. Both frameworks list what is necessary for a data role, but it is not clear how the two align and there is a lack of consistency in their use across government. This may make it hard for those working in data roles to know what skills to focus on for their development. This can be further complicated when people are members of other analytical professions as well.

The following example brings home the importance of having and retaining specialist data knowledge for the success of data sharing and linkage programmes. We heard from the UK Longitudinal Linkage Collaboration that their specialist TRE has been successful specifically because it is a collaboration of both those in the longitudinal data community and infrastructure experts. What was emphasised the most was that the specialist knowledge of the community was crucial to the development of the TRE and it would not have been as successful had it not involved industry professionals.

Recommendation 8: Career Frameworks

To enable more effective and visible support for the careers of people who work on data sharing and linkage, those responsible for existing career frameworks under which these roles can sit, such as the Digital Data and Technology (DDaT) career framework and the Analytical Career Framework, should ensure skills that relate to data and data linkage are consistently reflected. They should also stay engaged with analysts and professionals across government to ensure the frameworks are fit for purpose. These frameworks should be used when advertising for data and analytical roles and adopted consistently so that career progression is clear.


When an external researcher or government analyst wishes to access data that are held internally by government there are several high-level steps they naturally follow. Firstly, they must know the data they wish to access and where they are held. Secondly, they must establish the legal route to the data depending on the level of access required (e.g. identifiable vs non-identifiable data) and, finally, they must gain access to that data, sometimes through a TRE or possibly another route. They also need to secure the funding and/or resource to carry out their desired data project. We found that for each of these steps there are barriers which can cause significant delays.

The legal basis for data sharing was frequently raised during our interviews and views were polarised over whether relevant legislation is a barrier in itself, or whether misinterpretation of the legislation by some data holders creates a barrier. We were told that there is variation across government over how much data holders and researchers understand the process necessary to share data under their respective legal bases. If some of those data owners granting access to data are not understanding the process this can exacerbate risk aversion.

With the introduction of the Digital Economy Act (DEA) in 2017, data sharing across the public sector was further enabled under certain circumstances. These circumstances include sharing of de-identified data to produce statistics and for research purposes that are in the ‘public interest’ (for the purposes of this report, we interpret public interest as having the same meaning as public good and use the terms interchangeably). For research purposes, a research project needs to show that its primary purpose fits into the broad criteria listed in the DEA Research Code of Practice and Accreditation Criteria. While there is a definition of what constitutes the public interest in the DEA Research Code of Practice, under principle 4, we heard that there can still be differences in how ‘being in the public interest’ is interpreted, which can lead similar projects to be treated differently by the data holders responsible for granting access.

Health came up repeatedly as an area where it can be harder to share or access data due to legal restrictions. Among those we spoke to, there was a widely held conception that the DEA currently does not cover the sharing of health data for research. Speaking with the UK Statistics Authority enabled us to clarify that the restriction is slightly more nuanced: Section 64 of the DEA provides a legal route for accredited researchers to access data held by most public authorities, but it does not enable access to data held by bodies with health service functions. Other legal bases exist that allow access to data for research and statistics purposes in specific circumstances, dependent on who holds the data, what it is going to be used for and the type of data it is. But these other gateways can also come with restrictions. For example, some routes to access health data require the research purpose to have a specific and defined health benefit. New guidance developed by the National Data Guardian in 2022, which draws on insights from the public, seeks to clarify and improve public benefit evaluations by substantiating the meaning of public benefit, where health or care data is used for secondary purposes beyond care delivery.

Although not mandatory, we heard that data sharing agreements are a popular tool between two or more organisations to help navigate the legal process. We also heard that in some cases, data sharing agreements can be restrictive by only allowing a very clearly defined use case. Although not a barrier to those with defined projects, it does create a barrier to projects that aim to explore data and uncover all public good benefits the data could offer. It is also a barrier to delivering the UK Government’s National Data Strategy, which has a focus around “re-using and better co-ordinating data between civil society organisations” to “create a better understanding of societal issues”.

Although there are many challenges here, there is work already being done to try to make data sharing processes quicker and easier to navigate. The Central Digital and Data Office (CDDO) told us they are looking at how they can work across government to make data sharing easier and quicker while still in line with legislation. One example is setting up Memoranda of Understanding (MOUs) between departments, which agreements for data shares could then be set-up underneath when needed. The Information Commissioner’s Office (ICO) also have a code of practice on data sharing which provides helpful guidance and ADR UK have produced a helpful guide on the legal framework for using administrative data for research purposes. There are also steps being taken to open up access to health and administrative data for research purposes. For example, in November 2022, an amendment was made to Section 261 of the 2012 Health & Social Care Act (which only applies to England) to substitute “the purposes of…” with “purposes connected with (a) the provision of health care or adult social care, or (b) the promotion of health” [emphasis added]. The explanatory notes for the Act clarify that this new wording is intended to put beyond doubt the Health and Social Care Information Centre’s (NHS England) power to share data in connection with health care or adult social care. This includes for research for purposes which benefit or are relevant to the provision of health or adult social care and developing approaches to the delivery of health and adult social care (see note 847).

We heard that the IDS is also looking at the feasibility of using a broad agreement around data sharing so that users do not have to apply for data every time they want to use it. This will also help to streamline the application process, which is a further barrier considered under ‘Processes’.

Recommendation 9: Overview of Legislation

To help researchers understand the legislation relevant to data sharing and linkage and when it is appropriate to use each one, a single organisation in each nation should produce an overview of legislation that relates to data sharing, access and linkage, which explains when different pieces of legislation are relevant and where to find more information. This organisation does not need to be expert in all legislation but to be able to point people to those that are. The Office for Statistics Regulation (OSR) will help convene those in this space to understand more about who might be best placed to take this on.

Recommendation 10: Broader use cases for data

To support re-use of data where appropriate, those creating data sharing agreements should consider whether restricting data access to a specific use case is essential or whether researchers could be allowed to explore other beneficial use cases, aiming to broaden the use case were possible.

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Data Access

When it comes to gaining access to data, the barriers we have heard come under three main themes – data applications, finding and involving the right people, and lack of consistency and clarity. We discuss each of these separately below.

Data applications

We heard that when applying for data through a secure data platform, such as a TRE, the process is often lengthy and overly burdensome. Researchers expressed that application feedback by TREs can be drip-fed, which makes the application process longer; waits of a year or more can occur.

Researchers outside of government also spoke about being asked questions that are not relevant to the security or overall use of the output they intend to produce, but instead relate to the specific statistical methods that will be used. Researchers expressed that it is not only difficult but often impossible to know the methods that will be used until they have explored the data and understood its properties. Speaking to the UK Statistics Authority team involved in DEA accreditation, we were told that this information is needed so that projects can be assessed against the principles and conditions in the Research Code of Practice. However, given our discussions, we judge that it could be made clearer to researchers how questions relate to DEA requirements. The UK Statistics Authority team also made us aware that there is an exploratory route available under the DEA, which allows a researcher to apply for exploratory analysis to enable them to understand the strengths and limitations of data and inform the development of more detailed research proposals. Details of this process are in the UK Statistics Authority’s Research Project Accreditation Application Guidance.

Increased use of synthetic data could allow researchers to better explore data and decide how they might want to use it, so they are in a better position to make applications for the actual data with specific use cases. Research Data Scotland is already exploring using synthetic data in this way, allowing researchers to familiarise themselves with a dataset while waiting for approvals to use real data: this can aid understanding of broad cohort sizes, deriving variables of interest, and developing code, which in turn can speed up time needed for analysis once permissions have been received.

Finally, we heard there is also a frustration from government analysts that it should be easier for them to access data owned by government. As it currently stands, for most data platforms the application process is the same for both external researchers and government analysts, even though the latter have usually undergone security checks for their role. To help with this, we were told that the IDS is working with the UK Statistics Authority on a more streamlined application process for analysts when it comes to getting access to data from the Service through the DEA.

Recommendation 11. Communication

To ensure data application processes are fit-for purpose and well understood, those overseeing accreditation and access to data held in secure environments should prioritise ongoing communication with users, data owners and the public to explain and refine the information required. Wherever possible, they should offer face-to-face or virtual discussions with those applying to access data early in the process, to ensure clarity around both the data required and the process to access it.

Case study: Efficient data access – SAIL Databank

Background: SAIL Databank is a TRE, based in Swansea University, that enables research communities to access, link and analyse population and health data within a safe and secure remote access environment.

Data access processes: For SAIL, the time between data request and data access is roughly 3 months, although it can be as little as 1.5 months. For data access within a TRE, this is considered a quick turnaround time. We spoke to SAIL in depth about their process and found there are three key enablers to this:

An efficient and consistent process

The same data application process is followed each time and those that form part of the process know what is expected of them and when. A researcher who is looking to gain access to data for a research project is first put in contact with the analyst who understands the data to help them scope their project idea. Having this contact upfront helps to remove barriers around uncertainty of the data content, and its use. Their scoping form is then taken to internal review before being taken to their Independent Governance Review Panel, which considers the project from a privacy and public interest perspective. The panel take an average of 19 days to respond and the push back rate is low due to the prior internal review process. In parallel, the researcher gets their safe researcher accreditation, so when the project is approved, they are ready to access the data through the secure environment.

The longest part of this process is the first scoping phase, especially if the researcher is not clear on what they want or need. After this, the process is neat and well-defined.

A primary focus on public interest and privacy protection

Throughout the process the focus from the internal review and the approvals panel is to ensure privacy of the data subjects and to gauge the public interest in the project. In addition, the scoping phase exists to understand what support the researcher might need and to develop the research question. There are no questions that deviate from this, such as questions around methodologies used.

Reducing the need for repetitive tasks

We heard two examples of this:

  1. To speed up the process of getting sign-off from data owners, SAIL have a pre-approved list of uses from the data owners. This means that if a project falls within one of the pre-approved categories it does not need to go to the data owner and potentially await their organisation’s approval process.
  2. Separate processes are joined up wherever possible. For example, SAIL researchers can apply for DEA data and if so, the process is split internally by SAIL so that it goes to two panels. The decision from these two panels then come together internally, so there no need for separate applications.

Finding and involving the right people

Once it is determined that data can and should be shared it is vital that the right people are involved upfront and that those people can be identified and prioritise the process needed.

For every data share there will be many teams involved such as analytical, ethical and technical teams and these can be within the same organisation or from many different ones. We have heard that not getting these teams together at the very start can cause major delays to data sharing as each team will need the time to deliver their role. It also may be counterproductive – for example, analytical teams may start to shape a dataset only to find that it includes data that either cannot be shared or are technically difficult to share.

When researchers have a question about a dataset or process it can be a challenge to find the right person within a department or team who can help. We heard how researchers can be passed between statistical and research teams without much consistency or process, which has been described to us as extremely frustrating.

We also found that there is uncertainty around data ownership and where Information Asset Owners (IAO) sit within a department. There is no national requirement for the IAO to be a certain seniority level or to sit in a certain topic area, so even when a researcher knows what data they need, finding the name and contact details of the owner can be very difficult, especially for external researchers. Taking this one step further, those needing access to multiple datasets across government expressed that it would be easier to co-ordinate data ownership at the government level with one body to oversee the process. This was a common suggestion but there are still several barriers to overcome, and it would be difficult to achieve in the short term. We hope this would be a longer-term solution that could be achieved once it is easier to navigate at the organisation level. There has been some progress already as the IDS team have set up a task and finish group to understand the challenges other organisations face when sharing data and are looking to set-up a single data sharing model.

Finally, when the right people are in place, they need to be engaged and proactive, which relates to their risk appetite, prioritisation and the time they have available. Not getting engagement was a common barrier raised, and we acknowledge it is interlinked with other barriers such as resources, leadership and understanding of the legislation.

Recommendation 12: Checklists

To ensure all necessary teams are involved at the outset of a data sharing and linking project, organisations should consider the use of a checklist for those initiating data sharing. The checklist should contain all contacts and teams within their organisation who need to be consulted to avoid last minute delays.

Lack of consistency and clarity

As alluded to in the first two themes, the process of gaining access to data is made more complex by there being different processes for different organisations and data access platforms as well as different organisational set-ups. It would be unrealistic to recommend that all these processes become consistent with each other, but it is currently very difficult for both government and non-government researchers to know how to approach data access. In addition to this, we have heard that some departments are not aware of their own processes for data access or if any process exists. This means that each time that organisation is asked for data a person can be sent down a different route or the request is denied due to lack of knowledge on where to go or who to turn to for a decision. This lack of process is time consuming and the road to finding the correct information is repetitive. This issue has also been highlighted by CDDO in its Data Sharing Governance Framework. The second principle in the framework, named ‘Make it easy to start data sharing’ talks about creating a point of contact in an organisation to triage requests and queries for data sharing and access. We support this, particularly the ambition to make the point of contact easy to locate and/or to have a generic email address published on the organisation’s website. CDDO, in conjunction with Government Digital Service (GDS), is also developing the Data Marketplace, which aims to provide a central place for government officials to find and understand how to access data held in other parts of government that underpin government services. Within the Data Marketplace, users will be able to ‘discover’ data via a Government Data Catalogue, helping to improve the discoverability of data held within government.

Recommendation 13: Transparency

Every organisation within government should be transparent about how the data they hold can be accessed and the process to follow. This guidance should be presented clearly and be available in the public domain with a support inbox or service for questions relating to the process.


When it comes to resource, we found there are two big barriers: funding and people. They are closely linked as without funding it can be impossible for staff to get the time to work on the data they need. But without upfront staffing commitments, it can be hard to show benefit and feasibility for funding to be granted.

Almost all sharing and linkage projects are collaborations between two or more government departments or external bodies. Funding structures across government are set-up so that each department controls its own spend making successful funding highly dependent on having aligned priorities and vision within each department. This siloed approach to funding ultimately affects all teams involved in the sharing and linkage process and can result in the process breaking down if just one team is unable or unwilling to get the backing needed. This is even more pronounced on projects that require sustained funding of two years or more. Spending review cycles are often tight and have strict requirements where tangible benefit needs to be shown at every decision point. For projects which are complex or require many different datasets, it may not always be possible to show benefit or meet the deadlines involved.

This siloed approach is hampering efforts of collaboration and is a primary reason why projects with external funders are often much more successful – as seen in our case study example about Administrative Data Research UK (ADR UK) and the Ministry of Justice (MoJ)’s Data First programme below. One such fund, which has helped break this siloed approach is the Shared Outcomes Fund funded by HM Treasury. The fund is available to support pilot projects to test innovative ways of working across the public sector and government. In the 2019 spending round the fund supported a range of projects, including on drug enforcement and treatment, online harms and improving early years experiences, and all projects include collaboration and data from many organisations. The success of these projects show how sustained and ring-fenced funding can overcome barriers to data sharing and linkage.

It is worth noting that, for the third round of funding from the Shared Outcomes Fund, there will be a funding condition placed on bids. This condition will stipulate that, should an initiative seek to share data or seek funding for an analytical platform, they will have to contact and seek to partner with the IDS to achieve their goals. This is a big confidence vote in the IDS and it will need to be ready to respond to bid requests to avoid a further process barrier.

Case study: Effective funding – ADR UK and the MoJ Data First programme

Background: ADR UK is a UK data partnership with a mission to transform the way researchers access the UK’s wealth of public sector data. The MoJ Data First Programme is an ambitious project with the aim of unlocking the insight stored within administrative datasets across the justice system.

Funding consideration: Recognising the potential benefits of linking data from across the justice system, MoJ contacted ADR UK to help unlock the potential of over 50 administrative datasets from across the justice system and make them accessible to accredited researchers in a secure and responsible way. Doing so would help answer questions that have immense public good implications such as: ‘Are there individuals in the criminal courts who are also present in the family courts?’

Although MoJ tried to fund this work through its normal spending review cycle, the cycle was incredibly tight, and it needed to have something impactful to show at every decision point. This was not possible due to the size and scale of the data so instead MoJ applied to ADR UK for long-term, ring-fenced funding.

Ring fencing the funding in this way was crucial to success and meant external factors such as the pandemic and cost of living crisis did not disrupt progress. There was also an academic embedded in the team, which helped bring the knowledge of the research community into the development. It is important to note that prior to approaching ADR UK, MoJ had support from its leadership team, a motivation for public good outcomes and the drive needed to succeed in its partnership with ADR UK.

The Data First Programme has produced some high impact datasets that allow researchers to understand the extent and nature of repeat users of the magistrates and Crown courts including the type of offences committed and to explore how children’s education and social care factors in England relate to offending behaviours.

Recommendation 14. Funding Structure

To allow every organisation a consistent funding stream for their projects, a centralised government funding structure for data collaboration projects across government, such as the Shared Outcome Fund, should be maintained and expanded.


Finally, we discuss the technical elements of data and data linkage that were raised during our review. We heard it can be a real challenge for those linking data to get enough information about the data they are working with to provide a high-quality linked output with a measurable rate of error. Regarding data linkage methodologies, while we heard many positive reflections on the effectiveness of current methodologies, and the way that these are being developed, it was also acknowledged that methodological challenges do still exist, which can also themselves lead to issues with the quality of linked data. Finally, we heard how variation in the data standards and definitions used across government is making linking harder. These three areas are discussed in more detail below.

The quality of metadata

Metadata is a set of information which describes data. When using data for linkage purposes it is important to have access to as much metadata as possible as it contains information about where the data have come from and how they were collected. This is important because linking data often relies on matching cases by making assumptions about the data based on characteristics, in a process also known as ‘fuzzy matching’.

We heard that data held within government, at the level of both the dataset and the data descriptors, are not well documented making it difficult for a researcher to know if a project is feasible. A typical linkage research project requires the following:

  1. A research question of interest
  2. Understanding of what data would be needed to answer the research question
  3. Knowledge of whether the right data exists (description of dataset, description of the variables, coverage of the data)
  4. Knowledge of who owns the data (department and owner)
  5. Knowledge of how those variables were collected for linkage

Stages three, four and five are where a lack of metadata can cause significant problems to a data linkage project.

Firstly, researchers stated that they often do not know what data are held within the government. They described how it can be impossible to know if a dataset exists and if so, if the variables contained within it will be useful to them. We have heard examples of how both academics and government analysts have resorted to applying for data based on a brief description on the off chance that it might contain the variables they need. When they receive the data they then find that there is no further information provided about what the data are to help understand how it can be linked with other data. Given these data are then un-usable this has wasted both their time, and the time of everyone involved in the data access process.

Secondly, when a lack of documentation exists, people need to resort to the knowledge of people who work on or own the data. The difficulty of finding data owners within a department has been mentioned previously but academics have expressed how it is also difficult to know in which department the data might sit. Without the information contained in the metadata or being able to contact someone who knows about the data, researchers can be left unable to conduct their research.

There are also considerations around how not having this metadata affects the quality of a linked output. We heard a lot about quality when talking to those who work in specialist linkage teams within government. The main concern was that a lack of information about how data were collected and processed and the limitations they contain prior to conducting linkage would undoubtedly cause errors and inconsistencies in the linkage process. These errors can then be exacerbated by the linking of linked datasets. Quality assurance is also affected as it is hard to understand how the linking has been done which makes reproducibility impossible.

Lack of metadata is not usually a result of poor upfront planning but because most data were not originally collected for the purpose of linking and therefore it was not considered a necessity. We heard that some departments are better at providing metadata than others, and this is usually because they have been allocated resource to make their data more understandable. There is optimism that moving more data to platforms that are DEA accredited will encourage more departments to think about improving their metadata.

There is work being done in this area to help overcome this barrier to data linkage. As part of the Data Marketplace previously mentioned, CDDO is developing a Metadata Requirements Specification for government, which builds on existing guidance on metadata standards and the IDS is working closely with CDDO to set-up a metadata standard model, so that all government organisations contributing to the platform can follow one standard. ADR UK have also developed a data catalogue to help with the discoverability of data that has been made available for public good research. The catalogue contains information on the department which holds the data, a description of the data and, in some cases, the data dictionary of variable descriptions. This catalogue brings together metadata from the four nations of the UK, which was only previously available by searching four separate catalogues. This catalogue is publicly accessible and should help researchers overcome some of the barriers described above, particularly around knowing what data exists within government.

Recommendation 15. Sufficient resources

To enable effective, efficient, and good quality data linking across government, senior leaders should ensure there are sufficient resources allocated to developing quality metadata and documentation for data held within their organisations.

Data linkage methodologies

Throughout our interviews we heard that there are lots of different data linking methodologies currently in use across government, each of which have their own strengths and limitations with respect to the quality of the linked data they produce. We in OSR are not technical experts in data linkage, and therefore we are not best placed to make judgements on what the ‘correct’ or ‘best’ ways to link data are. As the regulator for Official Statistics, we advocate for ensuring these methods are developed and deployed in a way that supports public confidence in them and any resulting analysis or research. This means a focus on transparency, openness, and collaboration. Teams and support groups working across government to support data linkage have a big role to play in enabling improvements in linkage methodologies and their application. The National Statistician’s guidance on joined up data in government, which also highlighted the need for the data linkage community to work together, provides links to community groups that could support those working on linkage projects across government, as well as a series of peer-reviewed articles from academia, government and the third sector on linkage methodologies. We also heard about the Government Data Architecture community, which exists to share ideas, experiences and methods, in an effort to standardise the way organisations work with data and ease communication between government departments.

Data standards and definitions

Data standards are accepted agreements on the format, structure, definition, and manipulation of data. When everyone follows the same data standards can make it easier to share data securely, to understand how data can be linked and for departments to automate their processes for data linkage, which saves resource.

Data standards in government are currently not consistent across departments or within departments over time. We heard how this lack of a ‘common language’ and awareness of how other departments manage their data is causing repetition of work which could have been standardised. This also highlights the issues of poorly aligned systems whereby non-standardised data encourages the use of many different systems, which make data exchange more difficult. Currently, this barrier is mainly a problem to those working on large cross-government linkage projects as this is where the most time is lost to repetition. However, many of those we spoke to expressed concern that this could pose a big challenge to the collaborative and accessible future for data sharing and linking that is envisaged by the UK government.

There is work being done across government to help align data standards. Mission 1 of the National Data Strategy has a priority to “promote the development and use of good data standards so that data is held, processed and shared according to the FAIR principles” and the Department for Science, Innovation and Technology is mapping current data standards across government. The GDS is working to develop the Government Data Exchange, which aims to provide infrastructure for sharing data between government departments. Finally, the Data Standards Authority, led by the CDDO, is working to improve data standards across central government.

Recommendation 16: Standardisation

Many departments are looking to standardise government data and definitions, but it is unclear whether or how these initiatives are working together. Those working to standardise the adoption of consistent data standards across government should come together to agree, in as much as is possible for the data in question, one approach to standardisation which is clear and transparent. Given the work done by the Data Standards Authority, led by the Central Digital and Data Office (CDDO), the CDDO may be best placed to bring this work together.

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