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

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The following case studies provide examples of how three organisations have implemented the data quality principles: 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. dedicate time and resource to building capability in assessing, improving and communicating data quality through training and sharing best practice

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 a vendor-neutral overview of management practices and potential alternatives for specific situations Communicating quality guidance, including suggested approaches for clearly communicating quality to users 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 needsQuality assessment and assurance should take place at each stage of the lifecycle. The measures used will change at each stage.

The 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 Data is shared where it is appropriate for processing for secondary purposes. Where the data is suitable for publication, data should be quality assured, anonymised and made available with appropriate documentation including details on its quality. Open data published by public authorities should be released in consistent and accessible formats, to improve its utility. Potential data quality problems For example, if the timeliness of a data set is the most important dimension for the user, this may come at the expense of the data set’s completeness, and vice versa.Office for National Statistics: Looking to the future - a review of data linking methods The data lifecycle What is the data lifecycle? This stage is where an organisation or team intending to collect, store and use data must plan their processes and data storage. The planning stage involves determining business needs, identifying what data exists already and what needs to be collected or acquired. It also involves designing how this data will be collected and managed. 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 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. The first part of the framework provides a structure for organisations and individuals to frame their thinking around: