Process Mining: X-Ray Vision for Your Business Processes

You think you know how your processes work. Process mining shows you how they really work.

What Is Process Mining?

Process mining analyzes event logs to:

Discover actual process flows
Compare reality vs. design
Find bottlenecks and variations
Identify automation opportunities

Traditional Process Analysis:
Interviews → Workshops → Assumptions → Often wrong

Process Mining:
Event Data → Algorithm Analysis → Actual Process Map

Why It Matters

The Reality Gap

Studies show:

60% of process documentation is outdated
40% of steps happen differently than designed
30% of exceptions are unknown to management

Process mining reveals the truth.

How It Works

Step 1: Data Collection

Extract event logs containing:

Case ID (order number, ticket ID, etc.)
Activity (what happened)
Timestamp (when)
Optional: resource, cost, etc.

Step 2: Process Discovery

Algorithm reconstructs process flow:

Order Created → Credit Check → [Approved] → Shipment → Invoice → Payment
                           → [Rejected] → Cancel

Step 3: Conformance Checking

Compare actual vs. designed:

Where do people deviate?
How often?
Why?

Step 4: Enhancement

Overlay additional data:

Time per step
Resources used
Costs incurred
Wait times

Key Metrics

Metric	What It Shows
Throughput time	Total process duration
Waiting time	Time between activities
Rework rate	How often steps repeat
Variant count	Process complexity
Automation rate	% already automated

Use Cases

Operational Excellence

Find bottlenecks
Reduce cycle times
Eliminate waste
Standardize variations

Compliance

Verify process adherence
Identify violations
Audit trail analysis
Risk assessment

Automation Planning

Prioritize automation candidates
Estimate ROI
Find automation blockers
Track automation impact

Digital Transformation

Current state documentation
Change impact analysis
Progress measurement
Continuous monitoring

Tools

Enterprise Platforms

Tool	Strengths
Celonis	Market leader, ERP integration
UiPath Process Mining	RPA integration
SAP Signavio	SAP ecosystem
Microsoft Process Mining	Power Platform

Others

Tool	Best For
Disco	Quick analysis
ProM	Research/academic
ARIS	Business process management

Implementation Steps

Phase 1: Identify Source Systems

Common sources:

ERP (SAP, Oracle)
CRM (Salesforce)
Ticketing (ServiceNow, Zendesk)
Custom databases

Phase 2: Extract Event Data

Build extraction:

Case identifier
Activity name
Timestamp
Additional attributes

Phase 3: Analyze

Discover process map
Identify variations
Measure performance
Find root causes

Phase 4: Act

Prioritize improvements
Implement changes
Monitor impact
Continuous improvement

Finding Automation Opportunities

Process mining reveals:

Finding	Automation Opportunity
High-volume, simple tasks	RPA
Decision points	AI/ML
Document handling	IDP
Integration points	API automation
Manual data entry	Form automation

ROI Example

Discovery Phase

Effort: 2 weeks, $20K consulting

Findings:

40% of orders require rework
Average wait time: 3 days (target: 1 day)
15 process variations (expected: 3)

Improvement Phase

Actions:

Automated quality check (RPA)
Parallel approval workflow
Eliminated 10 unnecessary variants

Results:

Rework reduced to 15%
Wait time reduced to 0.5 days
$500K annual savings

ROI: 25x return on mining investment

Best Practices

Start with one process - Learn before scaling
Ensure data quality - Garbage in, garbage out
Involve process owners - They know context
Focus on action - Mining without action is waste
Make it continuous - Monitor, don’t just snapshot

Common Pitfalls

Pitfall	Solution
Poor data quality	Clean and validate first
Analysis paralysis	Focus on actionable insights
No business context	Partner with process owners
One-time project	Build continuous capability
Siloed use	Share insights across teams

Ready to discover your real processes? Let’s start your process mining journey.