Administrative data

This framework aims to help you assess the quality of administrative data for use in the production of official statistics.

Administrative data are data which have been collected during the operations of an organisation. Government produces a very large amount of administrative data, providing a valuable resource if it can be used correctly. There are legal gateways which can allow accredited and approved researchers to access administrative data for research and statistical purposes. There are certain criteria to meet to ensure this can happen, including the assurance that a person’s identity cannot be identified in the information disclosed for research and statistics.

Administrative data are generally not collected for the sole purpose of producing statistics. This can lead to challenges when using it for this reason, a summary of which can be found in David Hand’s paper, “Statistical challenges of administrative and transaction data”.


We are currently conducting work to review and, where possible, improve the accessibility of this guidance.

As part of this, we welcome any feedback on accessibility. If you find any problems, please contact us by emailing . Please also get in touch if you are unable to access any part of this guidance, or require the content in a different format. We will consider your request and aim to get back to you within five working days.

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Please note these features do not necessarily apply to the documents linked in the framework.

What is quality?

According to the Code of Practice for Statistics, “quality means that statistics fit their intended uses, are based on appropriate data and methods, and are not materially misleading.”

In essence, quality centres around a consideration of fitness for purpose:

Quality assessments are important in all contexts, to ensure that the data and resulting outputs meet your needs. In an administrative data context however, as the data are often used for a purpose that is different from the reason for initial collection, there are some unique considerations to make.

The first step in any quality assessment is deciding what quality looks like for your scenario. What do the data need to do and have, to ensure you can produce what you need?

These decisions should not only factor in what high quality is to you and your user, but also the time and information you have, and any costs associated with conducting assessments or improving aspects of quality. Proportionality is a core principle of quality assessment (discussed more below).

Quality assessment

Quality is a complex area, and these are not simple decisions to make. There is not one single tool that provides every answer; we recommend developing a quality assessment procedure that draws together guidance from various packages of work.

This framework is one piece of the puzzle, but there are also other tools that can help you understand quality and the higher-level principles associated with quality assessment. Some of these are outlined below.

Code of Practice for Statistics (CoP):

Quality Assurance of Administrative Data (QAAD):

Generic Statistical Business Process Model (GSBPM):

Planned developments to this framework

We plan to add to this framework over time with other useful resources, tools, and guidance that we find and develop, and to better integrate with the other existing tools. Current planned developments are outlined below: