What is the primary purpose of data cleaning?

The primary purpose of data cleaning is to remove unwanted data and fix structural issues. This process is essential because it ensures the quality and reliability of the data before any analysis or decision-making is conducted. Unwanted data can include duplicates, errors, or irrelevant information that can skew results or lead to incorrect conclusions. Structural issues may include problems such as inconsistent formats, missing values, or incorrect data types that hinder proper data analysis. By addressing these issues, data cleaning helps maintain the integrity of the dataset, thereby enabling more accurate insights and better overall performance in data-driven scenarios.

Enhancing data visualization, integrating different data formats, and reducing storage capacity, while relevant to data management, are secondary effects rather than the primary objective of data cleaning itself. Data cleaning focuses on preparing data for analysis by ensuring it is clean and well-structured, which is foundational for any further use, including data visualization or integration into larger datasets.