Process Mining vs. Process Discovery
This article explores the difference between process mining and process discovery, two techniques for understanding business workflows. Process mining analyzes data from computer systems to identify process flows, while process discovery uses AI to observe how people interact with those systems. Process mining is well-suited for analyzing existing data, while process discovery requires no additional software and focuses on the human element of workflows.
Many of you who are interested in process mining and process discovery is often asking the question as to what are the differences between the mining of backend process logs and the front-end observation-based process discovery. Here goes a brief comparison of process mining versus process discovery. (We do acknowledge that there is merit in both forms and effective based on the circumstances.)
Process Mining
Process Discovery
| Traditional Process Mining (Application Log Analysis) | AI-Driven Process Discovery (Extracting process footprint through user interactions with systems.) | |
|---|---|---|
 What is it? | Process mining uses event commits and application logs to decipher a business process. | AI-powered process discovery uses computer vision and machine intelligence to observe users and uncover deep process variants from digital traces of human work. |
 How does it work? | After integrating with transaction systems (or through batch files of event logs), process mining software analyzes the events to create a process model including several variants. | A non-intrusive virtual process agent records human work (while providing for privacy and information security) and through decomposition of a stream of images – using computer vision, neural networks, and machine learning – create a process metamodel. |
 What’s unique? | Log based process mining works discreet points on the committed states of data. | An AI-based approach takes a more fluid and continuous approach of all ad hoc human digital interactions. |
 Deployment Considerations | Integration with backend transaction systems and/or data file uploads. | No integration necessary and deployment is limited to a small lightweight probe on user desktops. |
 Business Impact | Post-facto and periodic analysis of processes and metrics leading to process transparency. | Dynamic and ongoing AI supervisory facilitating process conformance. A fit-bit for the enterprise. |
Skan. Tognitive process discovery engine
