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 Office for National Statistics: The ONS Data Service Lifecycle Data quality dimensions – how to measure your data quality The first part of the framework provides a structure for organisations and individuals to frame their thinking around:

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. These principles should lie at the heart of your approach to data quality and be supported by the application of the products within the framework. Each principle is accompanied by a set of practices which support their adoption. More detailed information on users can be found in the GOV.UK Service Manual and, in the context of users of Official Statistics, in the forthcoming User Engagement Strategy for Statistics. 2.1 Research your users and understand their quality needsThe 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. provide clear definitions of terminology used and not presume a high level of user understanding of data quality Data practitioners should ensure that measuring, communicating and improving data quality is at the forefront of activities relating to data The data lifecycle is a way of describing the different stages that data will go through, from collection to dissemination and archival/destruction. The purpose of the data and its lifecycle should be well understood by anyone who handles the data, from its collection to the eventual output.

To serve as a functional framework for the implementation of these practices in any business contextOnce 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 This section describes the six data quality dimensions as defined by DAMA UK, and provides examples of their application. These examples are taken (and sometimes adapted) from the DAMA UK Working Group “Defining Data Quality Dimensions” paper. Completeness 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. 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.

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. assess data quality at every stage and take proactive measures to improve quality when issues arise 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. You may have more than one type of user of your data. Different users’ needs may conflict, so it is important to balance these needs and prioritise having fit for purpose data. It is unlikely that data will be equally fit for all purposes.proactively engage with data providers to ensure a clear understanding of data quality requirements