What does data cleaning involve?

Data cleaning is a crucial step in the data analytics process that focuses on ensuring the quality and integrity of a dataset. This process involves identifying and correcting errors, inconsistencies, or inaccuracies in the data. It can also include removing any data entries that are incomplete or irrelevant to ensure that the dataset is reliable and ready for analysis.

When inaccuracies exist—such as typographical errors, duplicate entries, or incorrect formatting—these can lead to misleading insights or incorrect conclusions if not addressed. Therefore, thoroughly cleaning the data is essential to achieve valid and actionable results in any analytical work. This ensures the reliability of subsequent analyses and insights drawn from the dataset.

The process does not involve creating new datasets from various sources, collecting data, or analyzing existing datasets for insights, as those activities come before or after cleaning. Data cleaning specifically targets the improvement of the dataset itself by rectifying existing problems within it.