Catalog
concept#Data#Analytics#Process Mining

Process Mining

Process mining is a technique for analyzing and improving business processes based on actual recorded data.

Process mining allows organizations to gain transparent insights into their workflows, identify inefficient steps, and optimize process performance.
Established
Medium

Classification

  • Medium
  • Technical
  • Design
  • Advanced

Technical context

ERP systemsCRM platformsData analysis tools

Principles & goals

Data-driven decision makingTransparent processesContinuous improvement
Iterate
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Incorrect data leads to incorrect results
  • Resistance to change
  • Dependence on experts
  • Regular data review
  • Encourage interdepartmental collaboration
  • Continuous training and education

I/O & resources

  • Event logs
  • Process description
  • Data sources
  • Optimized process standards
  • Report on process performance
  • Improvement recommendations

Description

Process mining allows organizations to gain transparent insights into their workflows, identify inefficient steps, and optimize process performance. This is achieved by analyzing event logs captured within information systems.

✔

  • Increased efficiency
  • Better decision-making
  • Reduced turnaround times

✖

  • Data availability may be limited
  • High initial implementation costs
  • Complexity of integration

  • Turnaround Time

    The time taken to complete a process.

  • Process Error Rate

    The proportion of errors per process step.

  • Resource Utilization

    The utilization of available resources in the process.

Optimization at Company X

Company X used process mining to improve turnaround times and increase efficiency.

Case Study at Company Y

Company Y was able to reduce bottlenecks in manufacturing through process mining.

Implementation at Company Z

Company Z implemented process mining to ensure compliance with regulations.

1

Collect event logs

2

Perform data analysis

3

Identify optimization opportunities

⚠️ Technical debt & bottlenecks

  • Outdated software solutions
  • Insufficient documentation
  • Insufficient infrastructure for data storage
Bottleneck in data availabilityProcess bottleneckTechnological bottleneck
  • Using data from insecure sources
  • Conducting process analysis without expertise
  • Publishing results without validation
  • Over-analyzing data with too many metrics
  • Ignoring user feedback
  • Setting unrealistic expectations
Knowledge in data analysisUnderstanding of business processesTechnical skills in software
Integration with existing systemsData security and privacyScalability of the solution
  • • Compliance with data protection regulations
  • • Specific software usage required
  • • Availability of data sources