What Is Process Mining?

Definition

Process mining is an analytical technique that uses event log data from IT systems to discover, monitor, and improve business processes. By extracting timestamps and activity sequences from enterprise tools — ERP, CRM, workflow platforms, ticketing systems — process mining creates visual process maps that reveal bottlenecks, deviations from standard procedures, compliance violations, and optimization opportunities.

The discipline was pioneered by Wil van der Aalst at Eindhoven University of Technology in the late 1990s. Celonis, founded in 2011 in Munich, commercialized the technology and remains the market leader as of 2026.

How Process Mining Works

Process mining requires event logs with three minimum data fields:

• Case ID: A unique identifier linking all events in one process instance (e.g., order number, ticket ID)
• Activity name: The specific step performed (e.g., "Invoice Received," "Payment Approved")
• Timestamp: When the activity occurred

The software ingests these logs and reconstructs the actual process flow as it happened in practice — not as it was documented on paper. This reveals the true execution patterns, including rework loops, parallel activities, and exception paths that manual process documentation misses.

Three Types of Process Mining

Key Vendors (as of 2026)

Relationship to Automation

Process mining connects to automation in three ways:

1. Discovery: Identifies which processes are the best candidates for automation based on volume, repetitiveness, and standardization levels
2. ROI measurement: Quantifies before-and-after impact by comparing process metrics (cycle time, error rate, cost per case) pre- and post-automation
3. Continuous monitoring: Provides ongoing surveillance of automated processes to detect exceptions, performance degradation, and drift from expected behavior

Task Mining

Task mining is a related discipline that records desktop-level user activities (mouse clicks, keystrokes, application switches) to discover user-level process steps invisible in system event logs. Task mining fills the gap between system-recorded events, revealing what humans do between the timestamps in ERP and CRM logs. UiPath, Celonis, and Automation Anywhere all offer task mining capabilities.

Limitations

• Data quality dependency: Output is only as reliable as the event logs ingested. Incomplete logging, inconsistent activity naming, and missing timestamps degrade results significantly
• Privacy concerns: Event logs may contain personally identifiable information requiring anonymization before analysis
• Multi-system complexity: Processes spanning multiple IT systems require event log correlation across different data formats and case identifiers
• Implementation effort: Connecting process mining tools to source systems and cleaning event data typically requires 4-8 weeks of initial setup

The process mining market reached approximately $2.1 billion in 2025, growing at an estimated 35% compound annual growth rate.

Editor's Note: We used process mining on a retail client's order-to-cash cycle and discovered that 31% of orders hit a manual approval step that added 2.3 days on average — despite being auto-approved 96% of the time. Removing that bottleneck with a rules-based threshold saved them an estimated $180K annually. The process mining implementation itself cost $45K (Celonis license + 3 weeks of consultant time), so payback was under 4 months.