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. The framework is relevant for anyone working directly or indirectly with data in the public sector. This includes data practitioners, policy-makers, operational staff, analysts, and others producing data-informed insight. Senior leaders should be advocates for the framework in their departments, and should encourage staff to adopt the practices in their roles. All civil servants should familiarise themselves with the data quality principles and, where relevant, apply them in their context. assess data quality at every stage and take proactive measures to improve quality when issues arise adhere to agreed data principles, such as those being developed as part of the National Data Strategy

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. 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? develop effective communication channels with and between stakeholders to ensure a broad understanding of data quality 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 problemsWe 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.

During 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 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. To provide information about best practices, roles and responsibilities, deliverables and metrics, and maturity models for Data ManagementThe 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

be transparent about the quality assurance approach taken and communicate data quality issues clearly to users Data quality action plans, used to identify practical steps to assess data quality and make targeted improvementsThe first part of the framework provides a structure for organisations and individuals to frame their thinking around: