What type of data is typically enhanced during the data enrichment process?

Data enrichment primarily involves enhancing raw data by adding relevant information from external sources to improve its quality and usefulness. Raw data, which often lacks context and structure, can benefit significantly from this enhancement process, leading to more insightful analysis and better decision-making.

During data enrichment, various techniques are applied to raw data, such as adding demographic information, geographical data, or other relevant attributes. This added information helps organizations gain deeper insights, identify trends, and improve their overall data strategy. The enhancement transforms raw data into a more valuable asset for analytics and other applications.

In contrast, historical data is often already processed and may not require the same kind of enhancement as raw data. Encrypted data is protected for security purposes and cannot be enriched without first being decrypted. Redundant data, which consists of unnecessary duplicates, doesn't provide new value to the analysis and typically isn't the focus of enrichment efforts. Thus, raw data is the type that undergoes enhancement during data enrichment processes.