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

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Create a sense of accountability for data quality across your team or organisation, and make a commitment to the ongoing assessment, improvement and reporting of data quality. 1.1 Embed effective data management and governance

Understanding user needs is important when measuring the quality of your data. Perfect data quality may not always be achievable and therefore focus should be given to ensuring the data is as fit for purpose as it can be. More detailed information on users can be found in the GOV.UK Service Manual and, in the context of users of Official Statistics, in the forthcoming User Engagement Strategy for Statistics. 2.1 Research your users and understand their quality needs Communicate quality to users regularly and clearly to ensure data is used appropriately. 4.1 Communicate data quality to users 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.

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failure to carry out risk-based assessment on whether to use data because of poor understanding of data quality You may have more than one type of user of your data. Different users’ needs may conflict, so it is important to balance these needs and prioritise having fit for purpose data. It is unlikely that data will be equally fit for all purposes. Data quality action plans, used to identify practical steps to assess data quality and make targeted improvements For a data set to be complete, all records are included, and the most important data is present in those records. This means that the data set contains all the records that it should and all essential values in a record are populated.

document and share metadata to minimise ambiguity and enhance opportunity for data access and reuse

DAMA-DMBOK2

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 provide clear definitions of terminology used and not presume a high level of user understanding of data quality This stage is where an organisation or team intending to collect, store and use data must plan their processes and data storage. The planning stage involves determining business needs, identifying what data exists already and what needs to be collected or acquired. It also involves designing how this data will be collected and managed. The Government Data Quality Hub would like to thank the Data Management Association of the UK ( DAMA UK) for their input into the development of this Data Quality Framework. The framework draws heavily on the Data Management Body of Knowledge (DMBoK) and DAMA UK’s Data Quality Dimensions white paper. To serve as a functional framework for the implementation of these practices in any business context

build strong relationships with suppliers of external data to identify data quality problems at source A guide to the data lifecycle to help organisations to identify and mitigate potential data quality issues at all stages

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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. Office for National Statistics: Looking to the future - a review of data linking methods The data lifecycle What is the data lifecycle? The extent of the data quality problem within government is poorly understood. Work on data quality is often reactive and not evidence-based. Where quality problems have been identified, the symptoms are often treated instead of the cause, leading to ineffective improvements and wasted resources.