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

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Quality assessment and assurance should take place at each stage of the lifecycle. The measures used will change at each stage. More detailed information is available in the data lifecycle section of the framework. 3.1 Assess data quality at all stages of the lifecycle 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. dedicate time and resource to building capability in assessing, improving and communicating data quality through training and sharing best practice

failure to carry out risk-based assessment on whether to use data because of poor understanding of data quality The following case studies provide examples of how three organisations have implemented the data quality principles: The representation of the student’s D.O.B. – whilst valid in its US context – means that in the UK the age was not derived correctly, and the value recorded was consequently not accurate. User needs and trade-offs

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balance the conflicting needs of users where possible and prioritise improvements which have the greatest impact 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 To provide a vendor-neutral overview of management practices and potential alternatives for specific situations 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: Looking to the future - a review of data linking methods The data lifecycle What is the data lifecycle? 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. adhere to agreed data principles, such as those being developed as part of the National Data Strategy Through improved management of data, government can achieve the high quality data needed to deliver better outcomes for society. For many organisations, this is a journey that will take time and commitment. We ask that all government departments endorse and adopt this framework, and work to align their approach to data quality with these principles. To serve as a functional framework for the implementation of these practices in any business context

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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. This may result in trade-offs between different dimensions of data quality, depending on the needs and priorities of your users. You should prioritise the data quality dimensions that align with your user and business needs. 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. An introduction to data maturity models, for those who want to take a holistic approach to assessing and improving data quality 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