What critical step is involved in data cleansing?

The critical step involved in data cleansing is identifying and correcting inaccuracies in data sets. Data cleansing is essential for ensuring the integrity and quality of data within an organization. This process entails discovering errors, inconsistencies, and missing values in the data, and then taking steps to fix or remove these issues. This improves the accuracy and reliability of data used for analysis and decision-making.

When inaccuracies in data are not addressed, they can lead to misguided decisions based on faulty information. As organizations rely heavily on data for strategic planning, reporting, and operational efficiency, maintaining high data quality through cleansing is crucial. Identifying inaccuracies can include tasks such as correcting typos, validating formats, resolving duplicates, and ensuring that data entries conform to predefined standards.

The other options, while relevant to data management, do not specifically capture the essence of the data cleansing process. Creating new datasets, retaining obsolete data, and consolidating databases may play significant roles in overall data management and governance strategies, but they do not directly address the specific objective of improving data accuracy through the cleansing process.