What Is Data Cleaning?
Data cleaning, also known as data cleansing, is the process of identifying and rectifying errors, inconsistencies, and inaccuracies within a dataset. This vital step ensures that the data is accurate, complete, and reliable for analysis. Common tasks in data cleaning include removing duplicates, filling in missing values, correcting errors, and standardizing formats. By addressing these issues, organizations can improve the quality of their data, leading to more informed decision-making and insights. Data cleaning often complements data profiling, working hand-in-hand to prepare data for analysis and maximize its value for business intelligence.