DAMA-DMBOK: Data Management Body of Knowledge: 2nd Edition

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At this stage data is prepared for storage, formatted for use at further stages in the data lifecycle and maintained for use within the organisation. Consistent standards should be applied to the data and where necessary, the data should be anonymised. Where possible, data should also be cleaned and linked with other records in organisational data stores. This can help to reduce quality problems such as duplication and issues of consistency. assess data quality at every stage and take proactive measures to improve quality when issues arise

proactively engage with data providers to ensure a clear understanding of data quality requirements Once data is no longer in active use the data owner should determine whether it should be archived (available and secure) or destroyed. Information about the quality should be stored with the data. Potential data quality problems Data quality action plans, used to identify practical steps to assess data quality and make targeted improvements The framework complements existing ambitions to improve the quality of government data and analysis, such as those in the Government Analysis Functional Standard and the UK Statistics Authority five year strategy. It draws on international best practice in quality management – such as the International Organization for Standardization’s Quality Management Principles – and translates this into the context of government data. How to use the data quality framework

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Data may then be integrated into the organisational data stores. Practitioners ensure the data is stored appropriately and provide the access necessary to business users. Any data that is subject to change should be regularly monitored for its data quality to ensure it continues to be fit for purpose. Potential data quality problems

The data lifecycle illustrated here is not intended to be prescriptive. It is designed to illustrate the journey that data will take through most organisations and identify points at which data quality problems could happen. The actual data lifecycle for an organisation will be specific to the organisation and its processes.The representation of the student’s D.O.B. – whilst valid in its US context – means that in the UK the age was not derived correctly, and the value recorded was consequently not accurate. User needs and trade-offs Yet concerns have been raised over the quality of data collected, created and used by government. Poor quality data in government leads to failings in services provided, poor decision-making, and an inability to understand how to improve. The 2019 Public Accounts Committee Report (PDF, 303KB) showed that data has not been treated as an asset, and how it has become normal to ‘work around’ poor-quality, disorganised data.

We would also like to thank the Data Standards Authority for their input and the Cabinet Office, Home Office, Office for National Statistics, NHS Digital, Environment Agency and Government Digital Service for contributing case studies. Why do we need a data quality framework?The data lifecycle is a way of describing the different stages that data will go through, from collection to dissemination and archival/destruction. The purpose of the data and its lifecycle should be well understood by anyone who handles the data, from its collection to the eventual output. For example, if the timeliness of a data set is the most important dimension for the user, this may come at the expense of the data set’s completeness, and vice versa. The ask to adopt the framework is directed at central government. Many of the concepts and approaches are broadly applicable, however, and the framework serves as a useful guide for anyone wanting to improve data quality. Data quality principles An introduction to data maturity models, for those who want to take a holistic approach to assessing and improving data quality The first part of the framework provides a structure for organisations and individuals to frame their thinking around: