Which process is primarily concerned with ensuring data accuracy?

The correct answer focuses on Data Cleanup as the process that primarily ensures data accuracy. Data Cleanup involves identifying and correcting inaccuracies or inconsistencies in data, thereby enhancing its reliability. This process typically includes removing duplicate entries, rectifying misspelled words, and addressing formatting issues, all of which contribute to the overall quality of the dataset.

By ensuring that data is accurate and up-to-date, organizations can make informed decisions based on reliable information, which is crucial for various business functions, including reporting and analysis. In contrast, processes like Data Modeling, Data Storage, and Data Analysis do not specifically address the integrity of the data itself. Data Modeling is more about designing how data elements relate to one another, Data Storage deals with how data is saved and retrieved, and Data Analysis involves interpreting data to extract insights. Ultimately, while all these processes are important in the data management lifecycle, Data Cleanup is specifically tailored to enhance and ensure the accuracy of the data used.