What is meant by data cleaning?

Data cleaning refers to the process of ensuring that data is accurate, consistent, and reliable for analysis or reporting. The primary goal of data cleaning is to remove any unnecessary, redundant, or irrelevant data that could skew results or lead to incorrect conclusions. By eliminating such data, the overall quality and integrity of the dataset are improved, making it more useful for decision-making and analytics.

In this context, removing unnecessary data is fundamental because it helps to streamline the dataset, making analyses more efficient and effective. Data cleaning can also involve identifying and correcting errors, standardizing formats, and reformatting values, but the essence of the process centers around the removal of irrelevant or redundant information that serves no purpose for the analysis at hand. This ensures that the dataset used is focused, coherent, and ready for meaningful insights.