Data analytics: Data in motion tends to stay in motion

This is the second article in a two-part series.

In the first part of the series, “Developing a data analytics–enabled compliance program for the real world,” authors Matt Reeder and John Kim offered a three-step method for building data analytics–enabled compliance systems incrementally upon existing data sets and capabilities. Now, the authors have turned their attention to how data analytics can enhance compliance programs that have inventoried their current capabilities, identified useful data sets, and mobilized their resources to execute an established plan.

This article describes three data analytics techniques that will empower a mature, data-enabled compliance program to apply data analytics more effectively. They are rules-based tests, statistical and trend analyses, and machine learning. Understanding these techniques will allow compliance professionals to work toward incrementally adopting a multitiered approach to data analytics.