What is the main purpose of data cleaning?

The main purpose of data cleaning is to prepare data for analysis. Data cleaning involves identifying and correcting errors, inconsistencies, and inaccuracies in the dataset to ensure the data is accurate, reliable, and suitable for analytical processes. This step is crucial because high-quality data leads to more accurate and meaningful analysis, which can impact decision-making and results significantly.

In preparation for analysis, data cleaning might include removing duplicates, filling in missing values, correcting typos, standardizing formats, and ensuring that the data adheres to relevant constraints. Properly cleaned data increases the validity of the analysis, as it enables analysts and decision-makers to draw conclusions based on well-defined and precise information.

Storing data in a database does not inherently involve correcting or improving the data quality; it is more about organization and retrieval. Visualizing data focuses on presenting the data graphically, which is a step that typically comes after the data has been cleaned and analyzed. Archiving data relates to storing data for long-term retention, which also does not directly address the quality or preparation of data for immediate use in analysis.