What is data cleansing?
Data cleansing or data cleaning is the process of identifying and correcting corrupt, incomplete, duplicated, incorrect, and irrelevant data from a reference set, table, or database.
Data issues typically arise through user entry errors, incomplete data capture, non-standard formats, and data integration issues.
Why is data cleansing important?
Data cleansing is an essential process for preparing data for further use whether in operational processes or downstream analysis. It can be performed best with data quality tools. These tools function in a variety of ways, from correcting simple typographical errors to validating values against a known true reference set.
Another common feature of data cleansing tools is data enrichment, where data is enhanced by adding known related information from reference sources. By transforming incomplete data into a cohesive data set, an organization can avoid erroneous operations, analysis and insights, and enhance its knowledge production and evaluation capabilities.
Several criteria exist for determining the quality of a dataset. These include validity, accuracy, completeness, consistency, and uniformity. Establishing business rules to measure these data quality dimensions is critical to validating data cleansing processes and providing ongoing monitoring that prevents new issues from emerging.
Data cleansing is part of a robust data governance framework. Once an organization successfully implements a data cleansing process, the next step is the maintenance of the cleansed data. Data cleansing is a data management best practice that can be implemented to optimize data utility but must be maintained to avoid costly re-cleansing of data.
Precisely's data cleansing, deduplication & enrichment tools, along with data quality monitoring, help improve the quality of your data.