Process Mining

Identify and address bottlenecks in your business.

Discover how you can reduce costs, increase efficiency, and optimize processes.

What is Process Mining?

The goal of process mining is to capture insights and take action. Process mining is a key capability in the suite of tools needed to transform a business through hyperautomation. It helps companies assess and improve their operational processes, increasing the ROI of the organization.

Process mining is a data-driven approach that comes from the fields of process management and data science. It is designed to help organizations discover, monitor, and improve business processes. It uses event logs, which are lists of activities with start and end time-stamps from IT systems. Event logs can include activities such as when an order is received, product delivered, customer contacted, payment made, and more. This data-driven approach provides insight into what people, systems, and organizations are actually doing, as opposed to what they think they’re doing. The insights help identify bottlenecks and compliance issues to improve. Artificial intelligence (AI) is increasingly being applied to process mining to extract greater insights. 


Process Visualization Example

Process mining is composed of several techniques, including:

  • Process Visualization / Discovery - Enables the visualization of a process, which is typically generated automatically from event log data. The purpose is to provide data-driven insight into actual processes. 
  • Conformance Checking - Assesses an actual process against a reference model (target model) of that same process in order to identify deviations. Advanced process mining vendors can detect variations automatically.
  • Performance Analysis - Measures the efficacy of a given process by assessing factors such as cycle time or costs.
  • Root-Cause Analysis - Applies advanced artificial intelligence (AI) to identify patterns in your processes, enabling you to automatically identify the root cause of process issues. This data-led / fact-based approach to optimization eliminates perceptions that historically have biased analysis.
  • Prediction Analysis - Makes automated predictions about future process behaviors. Based on machine learning models that are trained on your processes.
  • Process Management Lifecycle - Enables continuous improvement and optimization of business processes. The lifecycle generally has six phases: Process Strategy, Process Documentation, Process Optimization, Process Implementation, Process Execution and Process Controlling.
  • KPI Monitoring - Monitors all relevant metrics pre- and post-analysis to track performance. Typically done via shared dashboards, which provide a central source of truth.

Process Mining Glossary

Conformance Checking    |     Cycle Time    |     Event Log    |     Machine Learning    |     Process Cycle    |    Process Discovery   |    Process Engine   |    Process Execution    |    Process Management Life Cycle    |    Wait Time

With digital process mining, you're automating traditional process analysis, providing a more objective view of what's actually happening in your organization.

Karina Buschsieweke, Process Mining Expert

A step-by-step guide to optimize your processes.

  1. Define
  2. Measure
  3. Analyze
  4. Improve
  5. Monitor

How process mining fits in with BPM.

Process mining is a complement to Business Process Management (BPM).  The simplest way to think about it is that process mining provides the insight, and BPM provides the ability to act on that insight.  When done together they take you from insight to action. This is why both process mining and BPM are considered critical capabilities in order for businesses to achieve hyperautomation. 

Appian AI-driven process mining is coming.

With complete automation from Appian, you can discover, automate, monitor, and optimize any end-to-end business process, all on a single platform.