In the context of data-driven decision making, what occurs during the process step?

During the process step in data-driven decision making, the primary focus is on transforming raw data into a structured and usable form, which is effectively achieved through data cleansing. This step involves identifying and correcting errors, inconsistencies, and inaccuracies in the data. By ensuring that data is accurate and reliable, organizations can derive meaningful insights and make informed decisions based on clean, high-quality data.

Data cleansing can involve several tasks, such as removing duplicate entries, filling in missing values, correcting formatting issues, and standardizing data types. It is a crucial foundational aspect because the integrity of the analysis that follows depends heavily on the quality of the data used.

Understanding the importance of data cleansing emphasizes the need for high-quality input before relying on data storage, maintenance, or analysis, which are all subsequent steps in the data management and decision-making process. Without proper cleansing, any analysis conducted would likely yield misleading results, undermining the entire decision-making framework.