The Office for Statistics Regulation (OSR) is the independent regulator of official statistics produced in the UK. Our vision is that statistics should serve the public good. Ensuring public confidence in the quality of statistics is an essential part of achieving this.

In this paper we set out how we in OSR think about quality, the challenges producers face when communicating quality, and the environment and behaviours within organisations that support quality. Finally, we outline how our thinking feeds into our current regulatory approach.

The context in which OSR considers Quality: TQV

Our understanding of quality is captured in the Code of Practice for Statistics, which sets the standards that producers of official statistics should commit to.

Quality relies on having data and methods that produce assured statistics. This means that statistics fit their intended uses, are based on appropriate data and methods, and are not materially misleading. It requires skilled professional judgement about collecting, preparing, analysing and publishing statistics and data in ways that meet the needs of those who want to use the statistics.

However, there are three pillars in the Code: Quality sits between Trustworthiness, representing the confidence users can have in the people and organisations that produce data and statistics, and Value, ensuring that statistics support society’s needs for information. All three pillars are essential for achieving statistics that serve the public good. They each provide a particular lens on key areas of statistical practice that complement each other.

Quality is not independent of Trustworthiness and Value. A producer cannot deliver high quality statistics without well-built and functioning systems and skilled staff. It cannot produce statistics that are fit for their intended uses without first understanding the uses and the needs of users. This interface between quality, its institutional context and statistical purpose are also reflected in quality assurance frameworks, including the European Statistical System’s QAF and the International Monetary Fund’s DQAF. The Code of Practice is consistent with these frameworks and with the UN Fundamental Principles of Official Statistics.

The Code sets out the standards that producers should follow to ensure that the public can have confidence in their statistics. It is not a rule book but a guide; considering any situation through the prism of the three pillars provides the basis for answering the challenges that producers and analysts face. It relies on having a mindset open to how the statistics can be wrong and a culture that prioritises quality management.

Quality is not a static characteristic but dynamic: producers need to remain active in their monitoring of sources and quality indicators, consciously looking for changes to systems, methods, policies, legislation, and other factors that can have an impact on the nature of the data and statistics and change over time.

Breaking down the concepts of ‘Quality’

The three principles within the Quality pillar are:

  • Suitable data sources
  • Sound methods
  • Assured quality

The following explanations explore what we are looking for when we regulate statistics, to ensure quality, and public confidence in quality.

Suitable data sources

Suitable data sources means that statistics should be based on the most appropriate data to meet intended uses. The impact of any data limitations for use should be assessed, minimised and explained.

It can be easy when producing statistics to trust what you are given; but rather than unquestioningly accepting data, selecting and using a data source should be an active decision. The principle of Suitable data sources reminds producers to gain a good understanding of the nature of data they are using (or planning to use), and to establish and maintain good relationships with supply partners where possible. This is a particular challenge for large producer organisations, requiring good metadata systems, collaboration, and cross-team ways of working that allow mutual sharing of insight about data.

Producers should also ensure that the data are an acceptable match for what is required. They need to be clear sighted and open on any limitations, and work to minimise the impact of these limitations. Suitable data sources emphasises the ongoing need to understand the suitability of the data, rather than viewing the selection as fixed.

Sound methods

Sound methods calls on producers to use the best available methods and recognised standards, and to be open about the reasons for their decisions.

The method can reflect the most advanced local practices or established international agreement. In the absence of these, it calls for methods to have a scientific foundation or, at the very least, established professional consensus. ‘Best’ doesn’t mean ‘perfect’, but it does have to have a sound basis.

The principle emphasises the importance of statistical harmonisation and transparency about methods. Producers should be open on what they are providing and why, being clear on whether they are coherent or not with related statistics and classifications. They should give a steer on how the statistics can be used and, if necessary, how they can’t. They should provide a proportionate explanation of the limitations and uncertainty in the statistics, helping users understand the nature and implications of potential sources of bias. More complex situations and methods will require more explanation than straightforward approaches: it is not a one-size fits all approach but should be tailored to need and to the nature of the audiences.

Statistical methods should also remain a live choice, and not be seen as immutable. Producers should be actively reviewing method choices and alert to emerging alternatives and possibilities. Working closely with external experts and other producers provides rich opportunities for learning new approaches or identifying potential issues.

Assured quality

Assured quality requires producers to explain clearly how they assure themselves that statistics and data are accurate, reliable, coherent and timely. The emphasis is on ensuring producers’ and users’ confidence in the quality of the statistics, that the statistics are fit for their intended uses.

Quality assurance is not an add-on, just a final stage of checking tick boxes before publication. Instead, it should be seen as an ongoing process throughout the development and production of statistics to build producers’ own understanding and confidence, which can then in turn reassure users. Organisations that effectively manage the quality of the data they produce and use have well-designed and managed systems and processes, well-established relationships between partners, and actively promote consistent quality standards and values. The operation and credibility of any statistical organisation is risked when quality management of data is not prioritised.

Assured quality is about identifying, anticipating and avoiding the problems that can arise from data inputs or the processes used to calculate statistics in an effective and efficient manner. It should be proportionate to the nature of the quality issues and the importance of the statistics in serving the public good, but all statistics producers need to be curious, and not take data at face value.

It is helpful to consider the relevant DAMA quality dimensions when testing the input data, such as completeness, uniqueness, consistency, timeliness, validity, accuracy, and user needs and trade-offs. It’s also helpful to assure the statistical output against the ESS quality dimensions: relevance, accuracy and reliability, timeliness and punctuality, coherence and comparability, and accessibility and clarity. (The Code of Practice places relevance and accessibility and clarity within its Value pillar as we see these as key to considering user needs and ensuring the statistics are useful and usable.)

Communicating Quality

Transparency and effective communication are common threads running through the three principles outlined above.

One of the key challenges producers face is how to effectively communicate about the quality of their statistics. Often, producers provide extensive information about methods and sources, but then do not say how good (or not good) are the statistics in relation to their expected uses. Users can be left unclear on what the information provided means for using the statistics. Some users have told us during our designation review that they would like prominent, succinct quality information, which helps them decide whether the statistics are suitable for their own use.

There can be a wariness among analysts to being open about quality issues, believing that this will undermine the confidence of users in the statistics and the producers. Low quality doesn’t equate to poor performance (although that can occur). We would like to see producers being more confident in showing the great work they have done to produce useful statistics in areas that would otherwise miss out.

As highlighted in our recent review of approaches to presenting uncertainty in the statistics system, giving straightforward information about uncertainty, and being open when things go wrong, builds confidence in you as a trustworthy organisation. While there are examples of statistical producers communicating uncertainty in a way that is clear and understandable to non-expert users, our 2021/22 State of the Statistical System report, which looked across findings from all our regulator work over this period, emphasised that there is more that could be done by many producers to communicate uncertainty around estimates in a way that brings effective insight.

Users often say any data is better than no data. We see value in having more timely data that may be of lower accuracy when there is a clear public interest that can be met. This can be a source of anxiety for analysts who feel the compromise undermines the integrity of the statistics. It is a judgement call for producers to make on how to ensure that quality is sufficient for the appropriate use of the statistics. Balancing timeliness and accuracy rely on good engagement with users. Being clear about your confidence in the quality and value of the statistics and how you have come to your choice can provide reassurance to those with concerns and an opportunity for healthy challenge.

Publishing data and statistics that materially mislead is unacceptable. If the statistics do not bear the weight of the decisions made using them, producers should immediately review whether they should continue to publish them or give clearer guidance to protect against inappropriate use. Statistics can become materially misleading estimates of what they aim to measure when they are based on unsound sources, use inappropriate methods, or where producers don’t have appropriate quality checks.

A culture that supports Quality

The wider organisational context, or ‘quality culture’, in which statistical producers work will always impact their ability to produce statistics that are of appropriate quality.

The Code encourages organisations producing official statistics to be open about their commitment to quality. This means actively promoting appropriate quality standards and values, reflecting their approach to quality management. They should use well-designed and managed systems and processes, appropriate tools, and have well-established relationships between partners.

We recommend producers encourage a mindset that emphasises a focus on quality, that is open to seeing how their statistics could be wrong without blame. A culture of quality encourages honesty and openness to learn from errors and near misses to strengthen producers’ systems. It supports innovation and creativity in finding new sources and solutions, to produce statistics that are relevant and useful. It builds professional judgement and confidence, to provide clear quality statements that users need. It focuses on delivering statistics in which users can have confidence.

To meet this principle, and thereby to enable effective management of risks to quality, the roles and responsibilities of all those involved in the production of statistics should be clearly defined. Managers who provide clear expectations with respect to quality, as well as guidance and support, will better enable junior producers to understand and carry out their roles, minimising the risk of error. Senior leaders should prioritise quality management, investing the necessary resources to promote and support developments and innovations that can enhance the quality of official statistics. They also need to act as advocates for official statistics to ensure that they are valued across their organisation.

How does our perspective influence OSR’s regulatory approach?

Our regulatory approach

We use assessments and compliance checks to judge compliance with the Code of Practice for Statistics for individual sets of statistics or small groups of related statistics and data (for example, covering the same topics across the UK). Whether we use an assessment or compliance check will often be determined by balancing the value of investigating a specific issue (through a compliance check) versus the need to cover the full scope of the Code of Practice (through an assessment).

There is no ‘typical’ assessment or compliance check – each project is scoped and designed to reflect its needs. An assessment will always be used when it concerns a new National Statistics designation and will also be used to undertake in-depth reviews of the highest profile, highest value statistics, especially where potentially critical issues have been identified.

There is no absolute level of quality, especially accuracy, that distinguishes National Statistics from other statistics. This depends heavily on the use made of the statistics, the availability of other similar data on the same topic that can be used alongside the official statistics, and possibly other factors. It is therefore not for OSR to judge a level of accuracy that is ‘acceptable’. Instead, our judgements are based on hearing directly from producer teams on how they produce the statistics and their views about the challenges and opportunities they face. We consider evidence from users and other stakeholders, particularly in relation to any views about the suitability of the methods and presentation and how well information needs are met. We are also alert to the primary reasons for the collection and release of the statistics, as well as their wider public benefits. We are currently evolving our regulatory approach with a ‘Quality-heavy’ assessment, that provides an in-depth review against the Quality pillar.

OSR guidance

We have some useful guidance that can assist producers in their quality management. We published a guide Thinking about quality when producing statistics following our in-depth review of quality management in HMRC. We released a blog to accompany our uncertainty report that provides a useful collection of answers to questions posed by analysts working in government in our recent webinar on uncertainty. It highlights some important resources, top among them the Data Quality Hub guidance on presenting uncertainty. Our Quality assurance of administrative data (QAAD) framework is a useful tool to reassure users about the quality of the data sources.

New developments

To support statistics leaders in developing a strategic approach to applying the Code pillars and a quality culture, we have developed a maturity model, ‘Improving Practice’. It provides a business tool to evaluate the statistical organisation against the three Code pillars and helps producers identify the current level of practice achievement and their desired level, and to formulate an action plan to address the priority areas for improvement for the year ahead. This tool is currently being piloted by five producer bodies.

We are also currently developing a quality grade tool to support producers in rating the quality of their statistics and in providing a succinct statement for users.

And we are continuing to promote a Code culture that supports producers opening themselves to check and challenge as they embed Trustworthiness, Quality and Value, because in combination, the three pillars provide the most effective means to deliver relevant and robust statistics that the public can use with confidence.

Our understanding about quality continues to evolve as we carry out our regulatory work and we would welcome hearing your thoughts – please share any reflections by emailing