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

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This section of the framework describes the stages of the data lifecycle in more detail, and outlines quality issues that may occur at each stage. Quality across the data lifecycle be transparent about the quality assurance approach taken and communicate data quality issues clearly to users 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? We find ourselves living in a society rich with data and the opportunities presented by this. In such an age, it is essential that public bodies have confidence that the data they access and process is fit for its intended purpose. Government’s ambitions around digital transformation of public services and the UK becoming a world leader on AI are predicated on access to good quality data to inform decision-making and service delivery. 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.

insufficient information about the data that has been received (for example from producers of administrative data) 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.A school receives applications for its annual September intake and requires students to be aged 5 before 31 August of the intake year. build strong relationships with suppliers of external data to identify data quality problems at source

failure to carry out risk-based assessment on whether to use data because of poor understanding of data quality develop effective communication channels with and between stakeholders to ensure a broad understanding of data quality

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Data practitioners may sometimes need to return to earlier stages in the lifecycle to correct data quality problems. The stages of the data lifecycle 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. To serve as a functional framework for the implementation of these practices in any business context Communicate quality to users regularly and clearly to ensure data is used appropriately. 4.1 Communicate data quality to users proactively engage with data providers to ensure a clear understanding of data quality requirements