Framework for data quality management in government
Framework for data quality management in government
By Matthew White, Senior Business Development
We are delighted that the UK government recently published a new framework for data quality management. The framework has been developed in collaboration with the Government Digital Service, using best practice drawn from organisations across government.
At the Government Data Quality Hub launch event on 3rd December the framework was announced, focusing on why it’s important, and what it sets out to achieve. Getting data quality right means a lot more than just data cleansing. It means understanding the quality of data, sharing that information with others, and taking the right action to address problems. The framework contains principles and approaches that can help people to better manage data quality in their organisations. They are designed to help establish better data quality management through evidence-based, proactive assessments and effective, targeted improvements.
Our approach to embracing this framework
The approach we take and the tools we offer for embracing this data quality framework in government follows learning by doing, overcoming by sharing and succeeding together through data collaboration, data automation, data transformation, data validation and data visualisation principles.
- Data Collaboration – working with employees and stakeholders internally and externally to share data quality management responsibilities using 1Data Gateway and Location Master Data Management tools.
- Data Automation – operationalising automated data workflows for data quality management using 1Data Gateway, 1Integrate, FME and Geocortex
- Data Transformation – making data fit for purpose by transferring data between data source(s) and target interface(s) using Safe Software’s FME
- Data Validation – ensuring data is both correct and complete using 1Integrate.
- Data Visualisation – making quality data accessible using VertiGIS’s Geocortex
Data lifecycle
We are supporting organisations across government to embrace data quality management. This is achieved by adopting and evolving the above principles for “the data lifecycle”, as set out in the framework. The data lifecycle is a way of describing the different stages that data will go through, from collection to dissemination and archival/destruction.
Data lifecycle - Framework for data quality management
We have expertise across the data lifecycle to improve the use of data as information through better data quality, shorter cycle times and effective data management.
Data Management Association (DAMA)
1Spatial are a member of the Data Management Association of the UK (DAMA UK). DAMA UK provided input into the development of this Data Quality Framework.
Find out more
If you would like to find out more about how we work with government organisations to embrace data quality management, please contact us.