Which type of data process involves analyzing historical performance for future predictions?

Trend analysis is the type of data process that involves examining historical performance to identify patterns or trends that can inform future predictions. This approach relies on the evaluation of data collected over time to detect any consistent movements or directions in the data sets. By recognizing these trends, analysts can make informed forecasts about future events or behaviors, which is particularly valuable in fields like aviation where understanding past performance can significantly aid in planning and decision-making.

For instance, in the context of airline operations, trend analysis may involve looking at historical flight delay data to anticipate potential delays in future flights, allowing for improved scheduling and resource allocation.

The other options do not focus on the analysis of historical performance for predictions. Eligibility assessment typically involves determining whether certain criteria are met, data mapping refers to the process of connecting data fields from one database to another, and data cleansing is concerned with correcting or removing inaccurate records from a dataset. Each of these processes serves distinct purposes and does not include the predictive aspect inherent in trend analysis.