What Is Workflow Mining (Process Mining)?

Definition

Workflow mining (also called process mining) is the analysis of event log data from IT systems to automatically discover, monitor, and optimize business processes. Rather than relying on interviews, workshops, or documentation to understand how work flows through an organization, workflow mining reconstructs the actual process from system data, revealing how tasks are performed in practice rather than how they are assumed to be performed.

The discipline was formalized by Wil van der Aalst at Eindhoven University of Technology in the early 2000s. It combines data mining techniques with process modeling to extract process maps directly from event logs generated by ERP systems, CRM platforms, ticketing systems, and other business software.

Three Types of Process Mining

• Discovery: Automatically generating a process model from event log data without any prior process documentation. The algorithm analyzes timestamps, activity names, and case identifiers to reconstruct the actual flow of work, including all variations, loops, and exceptions.
• Conformance checking: Comparing the discovered process model against an intended or documented process to identify deviations. This reveals where employees bypass prescribed steps, where process bottlenecks force workarounds, and where the documented process does not match reality.
• Enhancement: Using event log data to improve an existing process model. Enhancement adds performance data (cycle times, wait times, throughput) to the process map, enabling data-driven optimization of specific process steps.

How Workflow Mining Works

Workflow mining requires event log data with three minimum fields per event:

1. Case ID: A unique identifier for each process instance (e.g., order number, ticket ID, invoice number).
2. Activity name: The specific step performed (e.g., "Create Purchase Order," "Approve Invoice," "Ship Order").
3. Timestamp: When the activity occurred.

The mining algorithm groups events by case ID, orders them by timestamp, and constructs a directed graph showing the most common paths through the process. Variant analysis identifies the most frequent process paths and highlights deviations. For example, in an order-to-cash process, the standard path might have 8 steps, but workflow mining might reveal 47 different variants, including loops, skipped steps, and rework patterns.

Process Mining Tools (as of March 2026)

Celonis holds the dominant market position with an estimated 30-35% market share among enterprise process mining platforms as of 2025, according to Everest Group. UiPath Process Mining is the fastest-growing due to its bundled offering with UiPath's RPA platform.

Relationship to Automation

Workflow mining is increasingly used as the front end of automation initiatives. The workflow is: (1) mine existing processes to identify automation candidates, (2) prioritize based on volume, cost, and complexity, (3) build automations using RPA or workflow platforms, and (4) continuously monitor the automated process through ongoing mining.

This "mine-automate-monitor" cycle is central to the hyperautomation strategy advocated by Gartner, which positions process mining as a prerequisite for intelligent automation rather than an afterthought.