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

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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. Planning is one of the most important stages in the data lifecycle. Good planning can prevent problems in data quality before they occur. Potential data quality problems include best practice in data quality management (such as the data quality dimensions) as part of training materials

The following case study provides an example of how an organisation has developed and implemented its own data lifecycle: build strong relationships with suppliers of external data to identify data quality problems at source An introduction to data maturity models, for those who want to take a holistic approach to assessing and improving data quality 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. Once 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

Quality assessment and assurance should take place at each stage of the lifecycle. The measures used will change at each stage. 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. 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. The second provides guidance on practical tools and techniques which can be applied to assess, communicate and improve data quality: Data practitioners may sometimes need to return to earlier stages in the lifecycle to correct data quality problems. The stages of the data lifecycle

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 data lifecycle is a way of describing the different stages the data will go through from design and collection to dissemination and archival/destruction. The flow of the data is not always sequential so you may need to return to previous stages to fix data quality issues.Data quality action plans, used to identify practical steps to assess data quality and make targeted improvements The following case studies provide examples of how three organisations have implemented the data quality principles: 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

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? 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 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 problemsfailure to carry out risk-based assessment on whether to use data because of poor understanding of data quality 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. 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 problems 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.

dedicate time and resource to building capability in assessing, improving and communicating data quality through training and sharing best practice assess data quality at every stage and take proactive measures to improve quality when issues arise

Communicate quality to users regularly and clearly to ensure data is used appropriately. 4.1 Communicate data quality to users Data practitioners should ensure that measuring, communicating and improving data quality is at the forefront of activities relating to data Data leaders should guide an organisation or team’s strategic direction by ensuring awareness and improvement of data quality Create a sense of accountability for data quality across your team or organisation, and make a commitment to the ongoing assessment, improvement and reporting of data quality. 1.1 Embed effective data management and governance

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