Data cleaning, also known as data scrubbing, is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in datasets to improve their quality and reliability for analysis, reporting, and other data-driven tasks. This process involves various techniques and methods to ensure that data is accurate, complete, and consistent. Data cleaning is a crucial step in data preparation and is essential for making informed decisions and deriving meaningful insights from data.
Key tasks include removing duplicates, handling missing values, correcting data formats, standardizing data, addressing outliers, validating data, removing irrelevant information, and transforming data for analysis.
Effective data cleaning is crucial for ensuring reliable analysis results and preventing errors that could lead to incorrect decisions. It is a fundamental step in data preparation before analysis, machine learning, or other data-driven tasks.