Data Cleaning

Data cleaning (also called data cleansing) is the process of identifying and correcting errors, inconsistencies, and incomplete records in a dataset. It is typically the most time-consuming step in any data project - commonly estimated to consume 60-80% of a data professional's time.

Common cleaning tasks include removing duplicate records, filling or flagging missing values, standardizing formats (dates, currencies, addresses), correcting encoding errors, and filtering out records that fall outside valid ranges.

Building automated cleaning into your pipeline - with validation rules, anomaly detection, and quality monitoring - is more sustainable than one-time manual cleaning. Data quality degrades over time as source systems change and edge cases accumulate; automated cleaning catches and handles new issues as they appear.