Is Process Mining the Same as Business Intelligence?
The Reality
At first glance, BI and Process Mining may look similar. Both analyze data within business management and both use visualizations to make insights easier to understand.
The difference lies in focus. BI traditionally tracks and monitors specific KPIs, providing snapshots of business performance whereas Process Mining takes a broader view, analyzing end-to-end processes to uncover rework, waste, and bottlenecks - and to understand the real flow of operations versus how they were designed.
In other words, BI assumes you already know your processes, while Process Mining reveals how those processes actually run in practice. This makes it especially powerful for identifying optimization potential, setting improvement priorities, and monitoring the KPIs that truly matter.
Better Together
At first glance, BI and Process Mining may look similar. Both analyze data within business management and both use visualizations to make insights easier to understand.
Process Mining and BI are not competitors - they complement each other. With modern Process Mining tools, the boundaries between BI, data mining, and process analysis are becoming increasingly blurred. Many BI tools now integrate Process Mining features, but their core strengths remain distinct.
A key reason is the type of data they use. Process Mining relies on event log data with three required elements: Case ID, Timestamp, and Activity Name. This unique format enables full end-to-end visibility, compliance and conformance checks, and root cause analysis. BI is stronger when working with other types of data, making it a natural companion to Process Mining rather than a replacement.
Bottom Line
BI and Process Mining are stronger together, offering both high-level performance tracking and deep process insight.
Why Process Mining isn’t Just Data Mining?
While both share the idea of “mining” data for insights, Process Mining is not the same as traditional Data Mining. Process Mining has its roots in business process management and was developed specifically to understand and improve how processes actually run.
Data Mining methods typically look for abstract patterns - like correlations, rules, or decision trees. Process Mining, on the other hand, builds complete process models and uses them to pinpoint inefficiencies, bottlenecks, and rework.
The key difference is perspective: Data Mining searches broadly for patterns in data, while Process Mining focuses on the process itself. It treats each case (for example, a customer order) as a sequence of activities, allowing you to see the real flow of work from start to finish.
Some Data Mining techniques do look at processes, but they rarely provide the same end-to-end visibility that Process Mining does. By leveraging the detailed event logs that IT systems record - what was done, when, and by whom - Process Mining produces fact-based models of the actual “as-is” process, making it easier to uncover improvement opportunities.
Process Mining vs Process Modeling: Ideal vs Real
At first glance, both Process Modeling and Process Mining give you a picture of how a process works. But they serve very different purposes.
Process Modeling is based on workshops, interviews, and assumptions. It describes how a process is supposed to work and is great for creating a shared understanding, documenting best practices, or training new employees. The challenge is that real-world processes change constantly - so models can quickly become outdated or overly simplified.
Process Mining, by contrast, uses actual data from your systems to automatically generate a flow of your as-is process. Instead of showing how things should work, it reveals how they really happen. This makes Process Mining an incredibly powerful tool for analysis, discovery, and continuous improvement.
The resulting picture from Process Mining often looks much more complex than a tidy process model - but that’s the point. It’s objective, data-driven, and captures every variation and detail. From there, you can:
Identify your most common process path and adopt it as a new “ideal” model.
Spot process variants that work just as well as the standard.
Detect problematic deviations that create inefficiencies and need fixing.
In short: Process Modeling shows intentions. Process Mining shows reality.
