What process involves modifying data to fit specific requirements?

The process that involves modifying data to fit specific requirements is data transformation. This process is essential in preparing data for analysis, ensuring that it is in a suitable format or structure for the intended use. Data transformation can include various activities such as cleaning, normalizing, aggregating, and enriching data—essentially making data compatible with the needs of specific applications or analyses.

For instance, in a GIS context, a dataset may need to be transformed by converting coordinate systems or changing the data scale to provide more compatible analysis with other datasets. This step is crucial for ensuring the reliability and accuracy of analytical results, as it directly impacts how data can be used in decision-making processes.

While data integration refers to combining data from different sources into a cohesive dataset, and data format conversion focuses specifically on changing the format of data (such as from CSV to GeoJSON), data transformation encompasses a broader spectrum of modifications to meet diverse analytical needs. Data aggregation, on the other hand, involves summarizing or combining data in a way that provides meaningful insights but does not inherently include the wide range of transformations that may be required to meet specific standards or requirements.