What is cleansing?
Data cleansing (also called data cleaning) is the process of finding and fixing errors, inconsistencies, and duplicate information in a dataset so that the data is accurate, complete, and ready to be used.
Let's break it down
First, you look at the raw data and spot problems such as misspelled words, missing values, or duplicate rows. Next, you decide how to fix each issue-by correcting, filling in, or removing the bad data. Finally, you apply those fixes and verify that the cleaned data looks correct.
Why does it matter?
If you use dirty data, the results of analysis, reports, or automated decisions can be wrong. Clean data leads to better insights, more reliable software, and fewer costly mistakes later on.
Where is it used?
- Business intelligence dashboards
- Machine‑learning model training
- Customer relationship management (CRM) systems
- Financial reporting and compliance
- Any application that stores or processes large amounts of information
Good things about it
- Improves accuracy of decisions and predictions
- Reduces wasted time fixing errors later
- Increases trust in data among users and stakeholders
- Helps meet regulatory and quality standards
Not-so-good things
- Can be time‑consuming, especially with very large datasets
- Requires clear rules; wrong rules can delete good data
- May need specialized tools or expertise to automate effectively
- Ongoing maintenance is needed because new data can become dirty again.