Guide to Voluntary Application of The Code

A Commitment to Trustworthiness, Quality and Value

Society benefits from data and analysis produced by a range of different types of organisations, including the public sector, charities, academia, and commercial organisations.

Government departments (and other public bodies) publish a wide range of data and analysis to design and evaluate policy that are not part of their official statistics. And data are collected, reused, and analysed by many other groups and organisations outside government. This mix provides a rich range of data sources.

This guide is for any producer of data, statistics and analysis which are not official statistics, whether inside government or beyond, to help them produce analytical outputs that are high quality, useful for supporting decisions, and well respected.

Building public confidence in statistics

The pillars of Trustworthiness, Quality and Value (or TQV) describe the essence of what is required to ensure that the public can have confidence in data and statistics. They form the fundamental framework of the Code of Practice, the statutory code for official statistics produced by government bodies in the UK.

Why adopt and apply Trustworthiness, Quality and Value?

Trustworthiness, Quality and Value are helpful to anyone producing data, statistics and analysis, whether they are inside or outside government. They are relevant to all kinds of data, such as those published in statistical research, economic analysis and management information.

A commitment to Trustworthiness, Quality and Value offers the opportunity for an organisation to:

• Compare its processes, methods and outputs against the recognised standards that the Code requires of official statistics
• Demonstrate to the public its commitment to trustworthiness, quality and public value.

For example, a government department can use the pillars to help provide clear advice to ministers about evidence. A charity can use the pillars to provide reassurance to its donors about its fundraising and public services.

Making your commitment

The approach is flexible and entirely optional. Where an organisation chooses to adopt and apply the three pillars for all or some of its statistics or functions, we ask that it publish a statement about why it thinks users can be reassured that it achieves Trustworthiness, Quality and Value in its published statistics.

In leading up to that decision, it is helpful to understand the three pillars, review the approach to producing and publishing statistics in relation to the pillars, and consider if there are ways of improving practice.

Understand

  • Understand the Code pillars
  • Speak with OSR
  • Get key stakeholders in your organisation onboard

Review

  • Review your current processes
  • Assess how they demonstrate TQV
  • Understand the reasons why they may not

Consider

  • Consider if changes to processes can be made
  • Make changes or plan them for the future

Publish

  • Publish your commitment
  • Be clear about your approach to each pillar and why your users can have confidence

The key is to think through each of the pillars:

  • How are we trustworthy in the way that we are organised, and manage and use data?
  • What is the quality of the data and how robust are our methods?
  • How do we provide value in our information?

Use the underlying principles in the Code as a guide to support this thinking, but it is not necessary to consider the detailed practices in the Code, which apply to official statistics. The publish phase means being transparent. Transparency requires organisations to make information available. It is helpful to your users to be clear if there are aspects of the pillars that are not applied, and to describe your approach to Trustworthiness, Quality and Value. The very process of opening up working practices to external scrutiny and responding to feedback is at the heart of building confidence.

This is an ongoing commitment – not a one-off exercise. Continue to move through the process, reflecting on any changes that may affect your approach to the production and publication of statistics.

How does Office for Statistics Regulation regulate the voluntary application of the Code pillars?

The Office for Statistics Regulation (OSR) is the Authority’s regulatory arm. Our remit focuses on official statistics produced by government, but we also encourage all producers of data and analysis that are available to the public to think about how to ensure that their potential benefit to society is realised.

We manage and apply the Code of Practice. We do not have a formal role in regulating the voluntary application of the pillars. Our role as regulator is to oversee the production and release of official and National Statistics. However, if someone raises a concern with us, we will contact the organisation and review its statement and procedures. We are prepared to comment publicly on the voluntary application of the Code’s pillars.

Further guidance

More information on the Code’s pillars can be found on our ‘About the Code’ page. We also have a collection of case studies written by representatives from organisations that have voluntarily applied the Code.

For more information, please contact Penny Babb in the Policy and Standards team at the Office for Statistics Regulation.

Glossary

Data: Characteristics of facts or information, usually numerical, such as, observations, opinions, events or transactions, from which conclusions may be drawn. They are the product of a collection of information (source data). They can also be the subject of statistical processing (processed data).

Management information: The aggregation and summary of operational data as statistics, to inform business decisions.

Official statistics: are produced by crown bodies, those acting on behalf of crown bodies, or those bodies specified in statutory orders, as defined in section 6 of the Statistics and Registration Service Act 2007.

Statistics: A collection of measures about an attribute compiled from a set of data. Statistics are used for making generalisations or inferring conclusions about particular attributes, at an aggregate level, for examples, about a subset of the population.

(Building confidence in statistics: Voluntarily committing to Trustworthiness, Quality and Value- Edition 1.0 – First published in May 2018)