Data Cleansing
Data Cleansing is the process of detecting, correcting, or removing inaccurate, incomplete, duplicate, or irrelevant data from a dataset to improve its quality and reliability. It ensures that data is accurate, consistent, and usable for analysis or decision-making.
Key Steps in Data Cleansing:
- Removing Duplicates – Identifying and eliminating duplicate records.
- Correcting Errors – Fixing typos, misspellings, and formatting issues.
- Handling Missing Data – Filling in missing values or removing incomplete records.
- Standardizing Data – Ensuring consistency in formats (e.g., date formats, currency).
- Validating Data – Checking for logical consistency and accuracy.
- Removing Irrelevant Data – Deleting outdated or unnecessary information.
Why do we need to do Data Cleansing?
- Improves Data Accuracy – Ensures reliable insights and decision-making.
- Enhances Efficiency – Reduces errors in reporting and analysis.
- Better Customer Insights – Provides cleaner customer information for marketing and sales.
- Prevents Compliance Issues – Ensures data meets regulatory standards.
