Leveraging the contextual information

With our data organized and/or enriched, the two primary ways we can leverage contextual information is via analysis splits and statistical influencers.

Analysis splits

We have already seen that an anomaly detection job can be split based on any categorical field. As such, we can individually model behavior separately for each instance of that field. This could be extremely valuable, especially in a case where each instance needs its own separate model.

Take, for example, the case where we have data for different regions of the world:

Figure 7.7 – Differing data behaviors based on region

Whatever data this is (sales KPIs, utilization metrics, and so on), clearly it has very distinctive patterns that are unique to each region. In this case, it makes sense to split any analysis we do with anomaly detection for each region to capitalize on this uniqueness. We would be able to detect anomalies in the behavior...