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

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develop effective communication channels with and between stakeholders to ensure a broad 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.

A parent from the USA completes the Date of Birth (D.O.B) on the application in the US date format, MM/DD/YYYY rather than DD/MM/YYYY format, with the days and months reversed.

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At this stage data is prepared for storage, formatted for use at further stages in the data lifecycle and maintained for use within the organisation. Consistent standards should be applied to the data and where necessary, the data should be anonymised. Where possible, data should also be cleaned and linked with other records in organisational data stores. This can help to reduce quality problems such as duplication and issues of consistency.

dedicate time and resource to building capability in assessing, improving and communicating data quality through training and sharing best practiceTo serve as a functional framework for the implementation of these practices in any business context

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

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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. 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.

Office for National Statistics: Looking to the future - a review of data linking methods The data lifecycle What is the data lifecycle? document and share metadata to minimise ambiguity and enhance opportunity for data access and reuse Quality assessment and assurance should take place at each stage of the lifecycle. The measures used will change at each stage. Communicating quality guidance, including suggested approaches for clearly communicating quality to usersassess data quality at every stage and take proactive measures to improve quality when issues arise There are six core data quality dimensions, as defined by DAMA UK. This is not a prescriptive list and may vary depending upon your data and your users’ needs. For example, a seventh dimension may be added to measure the quality of any specialist data, or you may not consider certain dimensions relevant in your context. Other organisations define quality dimensions slightly differently. The European Statistical System, for example, defines a set of quality dimensions for statistical outputs in its Quality Assurance Framework (PDF, 915KB). Core data quality dimensions communicate trade-offs in data quality clearly to aid understanding of the data’s strengths and weaknesses provide clear definitions of terminology used and not presume a high level of user understanding of data quality An introduction to data maturity models, for those who want to take a holistic approach to assessing and improving data quality