Which feature of Datorama allows users to automatically adjust data fields for future streams?

Data Fusion is a robust feature within Datorama that focuses on the integration and normalization of data from various sources. One of its significant capabilities is to automatically adjust data fields for future data streams. This ensures that as new data comes in, it aligns seamlessly with the existing datasets, maintaining consistency and accuracy.

This feature is particularly beneficial for marketing teams that regularly introduce new data sources or make changes to existing ones. By using Data Fusion, users can automate the process of mapping and transforming these data fields, which minimizes the time and effort needed for manual adjustments. As a result, organizations can ensure that their data remains coherent and usable for reporting and analysis, leading to more insightful decision-making.

The other options, while useful in their own right, do not possess the same automatic adjustment functionality specifically for future data streams. For example, Custom Classification focuses on categorizing data based on specific criteria, Calculated Metrics is used for deriving new metrics based on existing data, and Data Wrangling involves manually transforming raw data into a more organized format. None of these directly address the automatic adjustments for incoming data streams that Data Fusion provides.