Data quality is important because without it, you cannot effectively make decisions about program operations, accountability, or improvement. In fact, poor data quality has significant business costs in terms of accuracy, time, and effort. Data quality encompasses many attributes, such as reliability, validity, completeness, and timeliness. These attributes are supported by—and require—people, processes, and data systems dedicated to quality assurance at both local and state levels and throughout the data life cycle.
Select from the tabs above for more information, resources, and tools to support state and local professionals as they strive to assess and address data quality.
Published August 2017.