Process mining

Process mining reconstructs how a business process actually behaves by analyzing the timestamped event logs that systems like ERP, CRM or ticketing tools already record, rather than relying on a flowchart of how the process is supposed to work. Each log entry ties a case, an activity, and a timestamp together, for example one purchase order moving through approval, ordering, and receipt.

Software algorithmically replays these logs to reconstruct the process as a flow diagram, showing every variant actually taken, how often each one occurs, where cases sit waiting the longest, and where work loops back on itself. This is a form of data visualization applied specifically to process flow, and it often surfaces steps or rework loops nobody had documented.

Process mining matters because it exposes the gap between the process as designed and the process as executed, which is where most efficiency gains and compliance risks hide, and it can feed the same kind of business intelligence reporting used elsewhere. It also pairs naturally with anomaly detection to flag cases that deviate sharply from the normal path. The main pitfalls are event log quality, since missing or mistimed timestamps distort the reconstructed process, and treating every deviation as a problem without domain review to confirm it actually is one.