What is the intended outcome of the transform stage in the ETL process?

The intended outcome of the transform stage in the ETL (Extract, Transform, Load) process is to create useful information from raw data. During this stage, data is processed and transformed to fit operational needs or analytical goals. This can involve a variety of operations, such as cleansing data to eliminate errors, formatting it correctly, aggregating data, applying business rules, and enriching it with additional context.

By transforming the data, organizations can ensure that the information they work with is accurate, consistent, and relevant to decision-making processes. This ultimately enhances the quality and usability of the data when it is eventually loaded into a data warehouse or other data storage system.

While the other choices mention related activities—like deleting obsolete data or securing access—these do not capture the central focus of the transform stage, which is primarily oriented toward the modification of data to extract meaningful insights. Storing data efficiently and managing access are parts of the overall data management strategy but are not specific to the transformative purpose of this stage in the ETL process.