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. include best practice in data quality management (such as the data quality dimensions) as part of training materials 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. Quality assessment and assurance should take place at each stage of the lifecycle. The measures used will change at each stage. The following case studies provide examples of how three organisations have implemented the data quality principles:

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 problems 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. 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 governanceDuring the collection and ingestion stage, an organisation or team will acquire data based on user needs. They can improve the quality at source through validation rules and capturing appropriate metadata. Potential data quality problems adhere to agreed data principles, such as those being developed as part of the National Data Strategy provide clear definitions of terminology used and not presume a high level of user understanding of 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. adopt appropriate assessment measures at each stage rather than applying a one-size-fits-all approach to quality assurance

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

DICTIONARY OF TERMS

Data practitioners should ensure that measuring, communicating and improving data quality is at the forefront of activities relating to data