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

Product Information

build strong relationships with suppliers of external data to identify data quality problems at source 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. Data quality action plans, used to identify practical steps to assess data quality and make targeted improvements proactively engage with data providers to ensure a clear understanding of data quality requirements insufficient information about the data that has been received (for example from producers of administrative data)

At a high-level, data quality can be thought of as ‘fitness for purpose’ – is this data set good enough for what I want to use it for? The level of quality required will vary depending on the purpose, but will often consider several dimensions. Data quality is more than just data cleaning. 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.

DICTIONARY OF TERMS

Data leaders should guide an organisation or team’s strategic direction by ensuring awareness and improvement of data quality The first part of the framework provides a structure for organisations and individuals to frame their thinking around: To serve as a functional framework for the implementation of these practices in any business context To provide a vendor-neutral overview of management practices and potential alternatives for specific situations 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

include best practice in data quality management (such as the data quality dimensions) as part of training materialsdedicate time and resource to building capability in assessing, improving and communicating data quality through training and sharing best practice