Data quality refers to the state of qualitative or quantitative pieces of information. There are many definitions of data quality, but data is generally considered high quality if it is "fit for [its] intended uses in operations, decision making and planning".[1][2][3] Moreover, data is deemed of high quality if it correctly represents the real-world construct to which it refers. Furthermore, apart from these definitions, as the number of data sources increases, the question of internal data consistency becomes significant, regardless of fitness for use for any particular external purpose. People's views on data quality can often be in disagreement, even when discussing the same set of data used for the same purpose. When this is the case, data governance is used to form agreed upon definitions and standards for data quality. In such cases, data cleansing, including standardization, may be required in order to ensure data quality.[4]
Having a standardized data governance program in place means cleaning up corrupted or duplicated data and providing users with clean, accurate data as a basis for line-of-business software applications and for decision support analytics in business intelligence (BI) applications.