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

Product Information

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 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.

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? Data leaders should guide an organisation or team’s strategic direction by ensuring awareness and improvement of data quality 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 problemsA school receives applications for its annual September intake and requires students to be aged 5 before 31 August of the intake year. be transparent about the quality assurance approach taken and communicate data quality issues clearly to users insufficient information about the data that has been received (for example from producers of administrative data) 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.

At a high-level, data quality can be thought of as ‘fitness for purpose’ – is this data set good enough for what I want to use it for? The level of quality required will vary depending on the purpose, but will often consider several dimensions. Data quality is more than just data cleaning. There are six core data quality dimensions, as defined by DAMA UK. This is not a prescriptive list and may vary depending upon your data and your users’ needs. For example, a seventh dimension may be added to measure the quality of any specialist data, or you may not consider certain dimensions relevant in your context. Other organisations define quality dimensions slightly differently. The European Statistical System, for example, defines a set of quality dimensions for statistical outputs in its Quality Assurance Framework (PDF, 915KB). Core data quality dimensionsprovide clear definitions of terminology used and not presume a high level of user understanding of data quality 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. Office for National Statistics: The ONS Data Service Lifecycle Data quality dimensions – how to measure your data quality