What process can help in making raw data more understandable and usable?

Parsing is a crucial process in data handling that involves breaking down raw data into more manageable and understandable components. It refers to the technique of analyzing data structures and extracting useful pieces of information based on predetermined rules. For instance, when dealing with a complex dataset, parsing can simplify it by separating individual data fields, allowing for easier analysis and interpretation.

This process lays the groundwork for further data manipulation and is essential for cleaning and organizing data so it can be effectively used for analysis. By transforming raw data into a structured format, parsing enhances the usability of the data and prepares it for further processes such as aggregation or normalization. All these factors contribute to making the raw data more understandable for data scientists and analysts, ultimately leading to better insights and decisions.

While transformation, aggregation, and normalizing are all important concepts within the data preparation domain, parsing specifically focuses on the initial breakdown of raw data, making it the most direct answer to the question posed.