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

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Data is shared where it is appropriate for processing for secondary purposes. Where the data is suitable for publication, data should be quality assured, anonymised and made available with appropriate documentation including details on its quality. Open data published by public authorities should be released in consistent and accessible formats, to improve its utility. Potential data quality problems dedicate time and resource to building capability in assessing, improving and communicating data quality through training and sharing best practice 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 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. 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

The second provides guidance on practical tools and techniques which can be applied to assess, communicate and improve data quality: 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. 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 governanceThe 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. These principles should lie at the heart of your approach to data quality and be supported by the application of the products within the framework. Each principle is accompanied by a set of practices which support their adoption. build strong relationships with suppliers of external data to identify data quality problems at source A parent from the USA completes the Date of Birth (D.O.B) on the application in the US date format, MM/DD/YYYY rather than DD/MM/YYYY format, with the days and months reversed.

proactively engage with data providers to ensure a clear understanding of data quality requirements be transparent about the quality assurance approach taken and communicate data quality issues clearly to users According to the Data Management Association (DAMA), data quality dimensions are “measurable features or characteristics of data”. They can be used to make assessments of data quality and identify data quality issues. They should be used alongside data quality action plans to assess and improve the quality of your data. adopt appropriate assessment measures at each stage rather than applying a one-size-fits-all approach to quality assuranceCommunicate quality to users regularly and clearly to ensure data is used appropriately. 4.1 Communicate data quality to users A school receives applications for its annual September intake and requires students to be aged 5 before 31 August of the intake year. 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. 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 Users are the teams, businesses, services and people that will be making use of your data. For example, they may have business needs that rely on fit for purpose data from a trusted source, or they may be an enquiring member of the public looking to understand more about their local area.