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

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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 Communicate quality to users regularly and clearly to ensure data is used appropriately. 4.1 Communicate data quality to users 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. document and share metadata to minimise ambiguity and enhance opportunity for data access and reuse

Throughout the data lifecycle, those involved should be aware of future users of the data and possible onward uses of the data, and should ensure that data quality at each stage is documented and communicated clearly. Data quality action plans, used to identify practical steps to assess data quality and make targeted improvements To provide information about best practices, roles and responsibilities, deliverables and metrics, and maturity models for Data ManagementOnce 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 These principles are guidelines to aid the creation of a strong data quality culture in your team or organisation. They explain the best practice, procedures and attitudes that will be most helpful to ensuring your data is fit for purpose. 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. Office for National Statistics: The ONS Data Service Lifecycle Data quality dimensions – how to measure your data quality A school receives applications for its annual September intake and requires students to be aged 5 before 31 August of the intake year.

A guide to the data lifecycle to help organisations to identify and mitigate potential data quality issues at all stages 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. 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.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

At this stage of the data life cycle, data is processed and used for the specified business needs. This may involve exploration and analysis of the data, as well as production of outputs. Potential data quality problems 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. proactively engage with data providers to ensure a clear understanding of data quality requirements assess data quality at every stage and take proactive measures to improve quality when issues ariseTo provide a vendor-neutral overview of management practices and potential alternatives for specific situations include best practice in data quality management (such as the data quality dimensions) as part of training materials Data is fundamental to effective, evidence-based decision-making. It underpins everything from major policy decisions to routine operational process. Often, however, our data is of unknown or questionable quality. This presents huge challenges. Poor or unknown quality data weakens evidence, undermines trust, and ultimately leads to poor outcomes. It makes organisations less efficient, and impedes effective decision-making. To make better decisions, we need better quality data. 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.