One of the essential features of enterprise resource planning systems is the ability to provide the users and decision makers with reports on how the enterprise is running, and to enable the enterprise to provide the authorities the required legal reports.  By their nature these reports needs to operate on large amounts of data and the decision makers need the reports in a timely manner. To achieve acceptable performance of the programs that generates these reports, the data, the full transaction log, the programs operates on is kept in denormalized form. What we propose instead is to write the programs as they are operating on the full amount of data and then use automatic incrementalization for achieving acceptable performance. To study whether automatic incrementalization is practically feasible we introduce the reporting language FunSETL, which is a restricted ML dialect, a compiler for FunSETL that can perform automatic incrementalization, and we have collected a small suite of reporting programs written in FunSETL containing a real life report. We show that using incrementalization on our suite we obtain an asymptotic improvement of a linear factor in the running time compared to the non-incrementalized original programs.