Data quality management is the set of processes, systems, and techniques that a business puts in place to ensure all data is accurate, complete, and always up-to-date. Data quality management sits under the data governance umbrella – these are the internal security standards and processes that a business implements to ensure data is gathered and stored properly and governs how that data is accessed and by who (you can find out more about data governance, with our article "Data Governance 101" here).
In essence, data quality management is all about maintaining high-quality data. If a business can’t inherently trust its data, it can have a detrimental impact on overall operations and performance. Data informs actionable insights for every business decision, big or small, if that data is incorrect, wrong decisions are made.
Data quality issues can happen at every stage of a company’s lifecycle and for various reasons, such as incomplete data, unstructured data, different data formats, duplicate data, or difficulty accessing data in the first place. Other problems such as a lack of resources and time, the right tools, and untrained staff can also significantly contribute to poor data quality. While some of these issues might be addressed at a single point in time (e.g., through regular audits) others may require continuous improvement over several years (e.g., implementing new technology).