Data leaders should guide an organisation or team’s strategic direction by ensuring awareness and improvement of data quality

assess data quality at every stage and take proactive measures to improve quality when issues ariseAt 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

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 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 More detailed information on users can be found in the GOV.UK Service Manual and, in the context of users of Official Statistics, in the forthcoming User Engagement Strategy for Statistics. 2.1 Research your users and understand their quality needs 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. Data is fundamental to effective, evidence-based decision-making. It underpins everything from major policy decisions to routine operational process. Often, however, our data is of unknown or questionable quality. This presents huge challenges. Poor or unknown quality data weakens evidence, undermines trust, and ultimately leads to poor outcomes. It makes organisations less efficient, and impedes effective decision-making. To make better decisions, we need better quality data.Data practitioners should ensure that measuring, communicating and improving data quality is at the forefront of activities relating to data

Office for National Statistics: The ONS Data Service Lifecycle Data quality dimensions – how to measure your data qualityThe framework focuses primarily on assessing and improving the quality of input data rather than the quality assurance of analytical outputs. The HM Treasury Aqua Book provides guidance on quality in the production of analysis, while the Code of Practice for Statistics sets out the principles to ensure that statistics are fit for their intended purpose. 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.

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 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. communicate trade-offs in data quality clearly to aid understanding of the data’s strengths and weaknesses 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 problemsThe ask to adopt the framework is directed at central government. Many of the concepts and approaches are broadly applicable, however, and the framework serves as a useful guide for anyone wanting to improve data quality. Data quality principles 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.