During which step in the data-driven decision-making process does data cleansing occur?

Data cleansing is a crucial activity that falls within the process of preparing data for analysis. During the process step of the data-driven decision-making cycle, raw data is collected and transformed into a usable format. This includes identifying and correcting errors, removing duplicates, handling missing values, and standardizing formats.

By performing data cleansing during this stage, analysts ensure that the data is accurate, complete, and relevant before it is subjected to further analysis. This step is foundational, as the quality of the data directly impacts the validity and reliability of any insights derived from it in the subsequent analysis phase.

Hence, identifying the correct step where data cleansing takes place is vital for understanding how to maintain data integrity throughout the decision-making process.