Data cleansing is the effort to improve the overall quality of data by removing or correcting inaccurate, incomplete, or irrelevant data from a data system.
What do I need to know about data cleansing?
Data cleansing techniques are usually performed on data that is at rest rather than data that is being moved. It attempts to find and remove or correct data that detracts from the quality, and thus the usability, of data. The goal of data cleansing is to achieve consistent, complete, accurate, and uniform data.
How is data cleansing performed?
Data cleansing uses statistical analysis tools to read and audit data based on a list of predefined constraints. Data that violates these constraints is put into a workflow for exception data handling.
What are the benefits of data cleansing?
Data cleansing leads to high quality data. When data is of excellent quality, it can be easily processed and analyzed, leading to insights that help the organization make better decisions. High-quality data is essential to business intelligence efforts and other types of data analytics, as well as better overall operational efficiency.