You can view and analyze data anomalies contextually, within Analysis Workspace.
Anomaly Detection provides a statistical method to determine how a given metric has changed in relation to previous data.
Anomaly Detection allows you to separate "true signals" from "noise" and then identify potential factors that contributed to those signals or anomalies. In other words, it lets you identify which statistical fluctuations matter and which don't. You can then identify the root cause of a true anomaly. Furthermore, you can get reliable metric (KPI) forecasts.
Examples of anomalies you might investigate include:
Both Anomaly Detection and Contribution Analysis are core workflows in Analysis Workspace. You can run Contribution Analysis against any daily anomaly and embed the result in your Analysis Workspace project.
Analysis Workspace's anomaly detection algorithm includes