Data Cleansing Definition
Data Cleansing is the process of detecting and correcting errors, inconsistencies, and inaccuracies in a dataset so that the information is reliable and fit for use. In practical terms, it answers questions like: are values formatted consistently, are there duplicate or incomplete records, and does each field contain what it is supposed to contain. Typical activities include fixing typos, standardising formats, filling in missing values, removing duplicates, and correcting records that violate defined rules.
How does data cleansing relate to PIM or MDM?
Every PIM and MDM system depends on clean data to function correctly. Cleansing is what turns raw, messy input from multiple sources into consistent records that can be trusted and shared. Without it, poor-quality data spreads across connected systems, undermining the Golden Record and producing errors in every channel that consumes the data. Cleansing works alongside data quality, standardization, and deduplication, each of which addresses a specific part of the overall effort.
What is the difference between data cleansing and data enrichment?
The terms are related but distinct: data cleansing corrects what is already there, removing errors and inconsistencies to make existing data accurate, while data enrichment adds new information from external or supplementary sources to make the data more complete. Cleansing improves quality; enrichment increases value. A robust data process usually applies both, cleansing records first and then enriching them.
