Process Mining

What is Process Mining?

Process mining is a set of techniques that use specialized algorithms to analyze event log data from business systems, helping organizations understand and improve how their operations actually run. Unlike traditional process analysis, which tends to rely on interviews or assumptions about how work gets done, process mining gives you an objective picture based on real data pulled from ERP, CRM, and other enterprise software.

What makes process mining so useful is that it looks at actual data: case IDs, activity descriptions, timestamps, user actions, and resource information. This reveals exactly how work flows through an organization. Some industry folks call it an "MRI" for business processes because it shows what's really happening rather than what people think or hope is happening.

The concept traces back to 1999, when Dutch computer scientist Wil van der Aalst pioneered the research at Eindhoven University. Since then, it's grown into a mainstream technology. The market is projected to hit $2.3 billion by 2025, growing at about 33% annually.

Key Characteristics of Process Mining

• Data-Driven Analysis: Works with actual event logs that contain case IDs, activities, timestamps, and resources. No guesswork or subjective observations involved.
• Three Main Techniques: Includes process discovery (mapping real workflows), conformance checking (spotting gaps between actual and ideal processes), and process enhancement (finding optimization opportunities).
• Real-Time Visualization: Produces visual process maps showing the actual sequence of events, where bottlenecks form, and where workflows deviate from expectations. This supports process mapping initiatives with hard data.
• Continuous Monitoring: Supports ongoing tracking of process performance, measuring things like cycle times, costs, and efficiency across different teams or regions.

Process Mining Examples

Example 1: Banking Loan Applications

Piraeus Bank turned to process mining to figure out what was slowing down their loan applications. The analysis pinpointed specific bottlenecks and their root causes, which led to targeted automation. The result? Processing time dropped from 35 minutes to just 5 minutes per application.

Example 2: Manufacturing Delivery Optimization

A manufacturing company operating multiple factories used process mining to get a clear view of processes across every region. They analyzed costs, KPIs, and duration at each step. Having this fact-based picture helped them streamline delivery timelines and cut operational costs at all their locations.

Example 3: Invoice Processing

Organizations frequently apply process mining to invoicing and payment workflows to uncover hidden bottlenecks. One analysis showed that process mining can reduce duplicate customer payments by 67% and significantly lower invoice costs by revealing automation opportunities that weren't obvious before.

Process Mining vs Business Process Management

Process mining and Business Process Management (BPM) work well together, but they serve different purposes in process improvement efforts.

How Glitter AI Helps with Process Mining

Process mining tools are great at revealing what's happening and where bottlenecks exist. But Glitter AI helps with what often comes next: fixing documentation gaps and knowledge transfer problems. Process mining frequently uncovers variations in how different teams execute the same process, which usually points to a lack of standardized documentation. This is where process optimization really begins.

Glitter AI makes it easy to create visual, step-by-step documentation of the optimized processes you discover through process mining. You can capture screens, add annotations, and generate clear work instructions. This helps standardize how work gets done across teams and ensures that process improvements don't just get identified but actually get documented and adopted.