Process Modeling and Process Mining: Better Together

Process modeling describes how work should happen. Process mining reconstructs how it actually happens from event data. Use both to compare your target state with real execution, identify meaningful gaps, and decide what to change. Conformance checking makes that comparison explicit.

The Two Halves of Process Management

Process modeling and process mining answer different questions about the same process. Modeling starts with people’s knowledge and design decisions. Mining starts with event logs from the systems that record the work.

Modeling gives your team a shared description of the intended process. Mining uses recorded events to show the paths cases took, including variations that may not appear in documentation. Neither replaces the other: a model does not prove that work follows it, and mined data does not define what the process should be.

What Process Modeling Gives You

A process model makes decisions about the work visible and reviewable. It can give you:

• A shared language. A diagram gives people a concrete reference when they disagree about a handoff, decision, or step.
• A target state. Mining can show the current process, but it cannot decide which path your organization wants to follow. A model records that intent.
• Training and onboarding material. A clear diagram can help people understand responsibilities and sequence without relying on informal explanations.
• A basis for automation design. Before you consider automating a step, you need to define what it does, what information it uses, and what counts as completion. Modeling helps make those decisions explicit.

Process modeling methods range from informal sketches to structured BPMN diagrams. The right level of detail depends on the audience and the decision the model needs to support. When teams need a precise, shared notation, BPMN 2.0 provides a standard way to represent process flows.

What Process Mining Gives You

Process mining uses event data, typically including a case identifier, an activity, and a timestamp, to reconstruct process execution. It can show you:

• The paths cases actually took. See recorded sequences, including cases that skipped a step, went back, or needed rework.
• Process variants. Compare the different paths cases follow instead of assuming every case follows the documented route.
• Bottlenecks and waiting time. Measure time between activities to see where cases spend time waiting.
• Evidence for comparison. Once you have a model of the intended process, you can check how recorded execution differs from it.

Mining does not decide whether a deviation is wrong. It gives you evidence to investigate. Your process knowledge and the model help you interpret what the data shows.

The Gap Between Them and Why It Costs Money

A documented process can differ from the process people and systems actually execute. When teams plan staffing, changes, or automation around an outdated model, they may focus on work that does not happen as expected. Meanwhile, workarounds and rework can remain invisible.

Common gaps include:

• A step in the model that is not performed. A planned review may be skipped in practice. Designing around it without checking execution can lead to the wrong change.
• A step that exists only in practice. A workaround may have become routine without being documented or assigned an owner.
• An unmodeled rework loop. The diagram may show a direct path, while event data reveals cases returning to an earlier activity.
• A slow handoff. A model can show where responsibility changes, but mining can measure how long cases wait at that point.

Modeling alone cannot show whether the documented process matches execution. Mining alone cannot tell you whether observed behavior is acceptable. The gap between the two is why comparing them matters.

How They Work Together

The improvement loop connects the intended process to evidence from execution. Here is a three-step example using an invoice approval process:

1. Mine the current process. Use event data to see the paths invoices take, how long they wait between activities, and where cases return for corrections. This gives you a view of current execution, not just the documented route.
2. Model the target state. Agree on the approval steps, decision points, and handoffs you want. Record that target in a process model so the team has a clear reference.
3. Check conformance. Compare the model with recorded cases. Review where invoices skipped an approval, followed a different sequence, or took an unexpected path. Decide whether each difference points to a problem in execution, an exception the model should allow, or a target state that needs revisiting.

Then use what you learn to revise the process or its model, and repeat the comparison when you have new evidence. The loop is not a claim that every deviation should be removed. It gives you a structured way to decide which differences matter.

You can start with mining when you do not yet know which part of the process needs attention. You can start with modeling when you already have an agreed target state to check. Either way, treat the first model or analysis as a starting point, then update it as you learn more.

Run the first pass on one process to see what your event data can show.

Conformance Checking: Where the Two Meet

Conformance checking compares an intended process model with recorded execution. It can highlight cases where activities were skipped, the order differed, or a path in the model was not observed.

A difference is a prompt to investigate, not proof of a defect. The process may allow a valid exception, the model may be incomplete, or the event data may not capture the relevant activity. Start with a process and model that have clear scope, then review the findings with people who understand the work.

See how conformance checking works in ProcessMind. For a view of the different paths cases follow, see process variants.

The Traditional Approach: Pros and Cons

Keeping process models and mining results in separate tools can suit teams with established modeling or analytics workflows. Each tool can serve its specific purpose, and teams can choose the notation and analysis environment that fit their needs.

The tradeoff is the work required to connect them. You may need to export models, prepare analysis results, align activity names, and reconcile versions by hand. If that comparison takes too much effort, teams may do it once for a project and not repeat it. The model and the evidence then drift apart again.

Separate tools can still work when you have a reliable way to keep models and data aligned, clear ownership, and a review process that people follow. The important question is whether your team can repeat the comparison as the process changes.

A Process Modeling Guide in Practice

A useful process modeling guide starts with the decisions your model needs to support. Before you draw, agree on the process boundary, who will use the model, and how much detail they need.

1. Set a clear scope. Choose one process with a defined start and end. A broad area such as order to cash may need to be broken into smaller processes before you can model it usefully.
2. Choose a level of detail. Keep a diagram focused on one level. Mixing high-level stages with detailed system actions can make it hard to follow.
3. Use familiar names. Name activities and roles in the language your team uses. Clear labels make the model easier to review and easier to compare with event data.
4. Show the main path, then exceptions. Make the expected flow readable before adding less common routes. Include exceptions that affect decisions or responsibilities.
5. Assign ownership and version the model. Give someone responsibility for review and keep a record of changes. Without an owner or history, it is harder to know whether the model is current.
6. Set a review cadence. Revisit the model when the process changes and at agreed review points. A diagram that no one maintains will eventually lose the team’s trust.

The notation is only part of the work. Learn about BPMN 2.0 for the symbols and conventions. If you need to document processes in ProcessMind, see the BPMN modeling overview. For a broader introduction to mapping approaches, read about process mapping.

Turning the Loop into Continuous Improvement

The loop becomes useful when you repeat it as the process changes. Give each pass a clear owner and a specific question to answer. For example, you might investigate a long wait, check whether a change affected the paths cases take, or review whether the model still reflects the target state.

Keep the review practical:

• Make a focused change. Choose a specific issue rather than trying to redesign the entire process at once.
• Check the evidence. Use process mining to review recorded execution and, where appropriate, process simulation to explore possible scenarios.
• Update the model. If the target state changes, revise the model so it remains a useful reference.
• Review exceptions. Decide whether a deviation is an accepted route, a data issue, or a problem that needs attention.

For a structured improvement cycle, see the guide to data-driven process improvement.

Where ProcessMind Fits

ProcessMind brings process modeling and process mining together in one platform. You can document a target state, analyze recorded execution, and compare the two without relying on a manual handoff between separate tools. Process simulation is also available to explore scenarios.

Keeping the model and analysis together can make the comparison easier to repeat. It does not decide which deviations matter or make process changes for you. Your team remains responsible for interpreting the evidence and deciding what to do next.

If you want to understand the mining side first, read what process mining is. To see how the two connect in ProcessMind, explore BPMN modeling and process mining.

Close the loop on one process

Choose one process, mine its current execution, model the target state, and review the differences with the people who know the work. You can run that first pass in the trial.