What Is Process Mining? See How Work Really Happens

What Is Process Mining?

What is process mining? It is a way to use event data from IT systems to discover, analyze, and improve how processes actually run. By linking activities to cases and timestamps, process mining reveals the paths work takes, how long steps take, and where the process differs from expectations.

An event log records activities, timestamps, and case identifiers. Process mining groups those events into cases and reconstructs the process flow. You can use the results to investigate bottlenecks, rework, and conformance with a reference process. The analysis provides evidence; your team decides what to do with it.

For a closer look at process mining software, see our guide to process mining tools.

Wil van der Aalst, a pioneer of process mining, has compared it to an MRI for a business: it can reveal what is happening beneath the surface. That visibility can support process improvement and digital transformation. See our strategic guide to data-driven process improvement.

Imagine a customer order moving from receipt and approval to shipment. Each step may leave a digital record. Process mining connects those records to show the route each order took, including detours and exceptions. Instead of relying only on interviews or assumptions, you can examine the paths recorded in the data.

The results depend on the quality of the event data and the context behind it. A process map can show the intended route; process mining helps you see what the records say happened.

How Does Process Mining Work?

Process mining starts with event data from your IT systems. Each event typically needs three fields:

• Case ID: The identifier for a process instance, such as an order or support ticket.
• Activity: The step that took place.
• Timestamp: When that step happened.

The software groups events by Case ID and orders them by timestamp. It then reconstructs the paths cases took and presents them as a process model. You can compare paths, examine durations, and look for patterns such as repeated steps or long waits.

The quality of the result depends on the data. Missing timestamps, inconsistent activity names, or incorrect case identifiers can distort the analysis. Learn where to find process data and which data formats are supported.

Three Types of Process Mining

The three types of process mining answer different questions:

1. Process Discovery creates a process model from event log data. It helps you see the paths cases actually take, including variations you may not have documented.

2. Conformance Checking compares actual process behavior with a reference model. It helps you find where cases follow, skip, or deviate from the expected steps.

3. Process Enhancement adds information from event logs to an existing process model. For example, you can add performance measures such as waiting time or activity duration to understand how the process performs.

A project often starts with discovery, then uses conformance checking to investigate differences from the reference process. Enhancement can add performance context to the model. Choose the approach that fits the question you need to answer.

Anyone Can Use Process Mining

You do not need to be a data scientist to start exploring process data. Analysts, process owners, operations teams, and IT teams can work together to ask a question, prepare an event log, and review what the analysis shows.

A practical starting point is to:

1. Choose a process and a specific question.
2. Identify the system data that records its activities.
3. Prepare an event log with case IDs, activity names, and timestamps.
4. Review the discovered flow and investigate the paths that matter.
5. Validate the findings with people who understand the work.

The 5 Steps of Process Mining

Collect Data

Gather event data from your systems, such as records of each step in an order-to-cash process. An Excel file can be enough to start.

Discover the Process

Use process mining software to connect events by Case ID and time, then review the process flow shown by the data.

Check Conformance

Compare the discovered process with a reference model, such as a BPMN model, to investigate differences.

Analyze Findings

Use process dashboards to examine bottlenecks, rework, and inefficiencies, then decide what to investigate or change.

Monitor Changes

Revisit the process after changes to see how its performance and paths have shifted.

Open the getting-started path to prepare your first analysis.

Process Mining vs. Process Mapping

Process mining and process mapping answer related but different questions. Mining uses event data to show how a process ran. Mapping uses people’s knowledge and process design methods to document or plan how work should happen.

The two approaches work well together. Use process mining to investigate the process shown by the data, then use process mapping or BPMN modeling to document or design changes. ProcessMind supports process mining and process mapping in one platform. For more on how they complement each other, read about the combined value of process modeling and process mining.

How the Mining Algorithms Actually Work

Process mining algorithms turn event data into process models. Published research describes several families of them: the alpha algorithm derives relations between activities and is the classic starting point, the heuristic miner tolerates noise and incomplete logs, the fuzzy miner handles unstructured behaviour, and the inductive miner builds a process tree that is guaranteed to be sound. Each one is defined for a kind of log, and each returns the model its definition promises.

ProcessMind offers three miners of its own. You pick one in the Mine Model dialog, next to the gateway policies that decide how strict the drawn result is, as the mining docs describe.

The AI Miner is ours, and it is not a published algorithm reimplemented. It reads the enrichment data alongside the event log, detects parallel regions and decomposes the graph hierarchically, so the model keeps the structure the log implies instead of flattening it. That is where it earns its place: the published definitions assume a clean, complete log, and most organizations are working with an export that is neither. The AI Miner is the one we built to be useful on the data that exists, rather than on the data the definition asks for. It is also the only one of the three that needs enrichment, so on a fresh log the Directly-Followed-By miner is the honest place to start and compare.

After a miner creates the structure, a layout algorithm arranges it so the flow is readable. ProcessMind’s Clarity Engine handles that step.

Why You Should Care About Process Mining

Process mining gives you a way to examine how work runs across recorded cases, rather than relying only on a process description or a few examples. It can help you:

• See process variations: Compare the paths cases take and identify common or unusual routes.
• Investigate delays: Examine where cases wait and which activities take longer.
• Find rework: Look for repeated activities and loops that may point to errors or unclear requirements.
• Check conformance: Compare actual behavior with a reference process.
• Track performance: Use dashboards and KPIs to review process measures over time.

These findings help you ask better questions. They do not prove why a delay or deviation happened. Talk with the people involved and check the relevant context before deciding what to change.

See how process dashboards and KPIs help you review process performance.

The Good, the Bad, and the Ugly

Process mining is strongest on the evidence in the log, and weakest on everything the log leaves out. Source: ProcessMind

The good

Mining gives you an objective view of work that nobody has to reconstruct from memory:

• It shows behaviour across every case in the log, not the handful anyone remembers.
• It shortens the diagnosis, because the flow, the delays and the rework are drawn for you.
• It applies to any process that leaves structured event data in an IT system.
• Process dashboards put that evidence in front of business teams, not only analysts.

The bad

The evidence is only as good as the log, and the log is work:

• Poor-quality data produces a confident, wrong answer. The analysis is only as reliable as the event log.
• Preparing and checking the data is often the most demanding part of a project.
• Mining does not fix a process. It shows you what to investigate; people still decide and change.
• A complex model is hard to read. Start from one clear question and the paths that matter to it.

The ugly

Used carelessly, the same evidence does damage:

• Using process data to blame individuals creates fear and resistance. Aim at the process conditions instead.
• Misreading the data leads to wrong conclusions, so validate findings with people who know the work.
• Some tools need substantial IT support and investment. Check what the setup and the skills will cost before you commit.
• Starting without a clear question leaves you with more data than insight, and no decision to make with it.

How Not to Use Process Mining

Process mining is most useful when you have a clear question, reliable data, and people who can interpret the results. These are the mistakes that cost projects their credibility.

Don’t start without a clear goal

An open-ended search leads to analysis without a decision. Start from a question such as “Why do invoice approvals take more than ten days?” or “Where do customers experience delays during onboarding?” A specific question tells you which process, which data and which measures to look at.

A goal that holds up names three things: the process, the period, and the decision the analysis is meant to support. Write it down before you load anything, and agree on it with the person who owns the process. That agreement is what keeps a pilot from growing until it covers everything and answers nothing.

If you are not sure where to begin, a process improvement guide is already written as a scope. It names the process, the systems, the fields to export and the activities to capture, so the first question arrives with the data needed to answer it.

Don’t expect a tool to fix the process

Mining examines process behavior; it is not an automatic solution. It may show that approvals take too long, but the reason and the change are still yours to find. Review the findings, discuss them with the people involved, and measure the process again after a change.

Don’t work in isolation

Data needs context. Analyze a process alone, send out a report, and you may miss why a detour exists or whether it serves a valid business need. Work with the people who manage the process, and involve IT when you need help accessing or validating data. Their knowledge is what explains what the log can and cannot show.

Bring people in at the start rather than at the review, and tell them what the analysis is for. Someone who knows the question in advance can explain a detour, a temporary workaround, or a step that exists only in practice.

The people doing the work also know which steps leave no trace in the system. Those gaps are what make a discovered process look incomplete, and in ProcessMind you can mark them as data flow through so they stay visible on the model without inventing events that were never recorded.

Don’t treat it as a one-time project

A process that was improved keeps drifting. Review it again after the change, and compare results over time to see whether behavior has shifted. Monitoring is what turns a finding into a result.

Don’t use findings to blame people

Mining explains how work is structured, not who did something wrong. Share the purpose of the analysis and involve process owners and staff from the start. When the data shows that one group takes longer, ask what conditions explain the difference: workload, a system constraint, or a step that needs clarifying. The cause is usually structural, and the answer usually comes from the people doing the work.

Don’t use poor-quality data

The analysis depends on the event log. Missing timestamps, inconsistent activity names or incorrect case IDs make the process look different from what happened. Check what each field means, confirm that events belong to the right cases, and review a sample before analyzing the full dataset. Where to get process data covers the fields you need.

How to Get Started

A focused pilot helps you learn what process mining can show without trying to analyze everything at once.

Align on the Question

Explain what process mining can show and agree on what you want to learn. Bring together a small group with knowledge of the process, data, and business context.

Start with a question that matters to the team, such as where delays occur or why cases return to an earlier step.

Choose a Manageable Process

Choose a process with a clear scope and accessible data. A process with known delays or repeated work can be a useful starting point.

Limit the pilot to a business unit, product line, or other defined area. A focused scope makes it easier to validate what you find.

Prepare the Event Log

Gather the data needed for the pilot. A spreadsheet or CSV export may be enough to begin. Involve IT if you need help accessing or combining data.

Check that each event has a Case ID, activity name, and timestamp. Confirm that the fields are consistent and represent the process you want to study.

Explore the Process

Load the event log and review the process model. Check that the case count and time range make sense. If they do not, revisit the data preparation.

Look at the main paths, variations, and activity durations. Use filters to focus on the cases related to your question, then note patterns that need further investigation.

Validate and Plan

Review the results with analysts, process owners, and staff who know the work. Ask whether the patterns match their experience and what might explain them.

Agree on which issues to investigate or address, and define how you will measure a change.

Measure and Repeat

After your team makes a change, analyze the process again. Compare the results with your starting point to see what changed.

Use what you learn to refine the next question or expand the analysis to another process.

Where ProcessMind Fits In

Process Mining Use Cases

You can mine a process when its activities leave a digital record that can be linked into cases. Common examples include:

• Order-to-Cash (O2C): Examine the order lifecycle, from customer order to payment, and investigate delays in fulfillment, invoicing, or collection.
• Procure-to-Pay (P2P): Review procurement from requisition to supplier payment and look for approval delays or repeated work.
• IT Service Management (ITSM): Analyze service desk tickets to find slow resolution paths, escalations, and SLA breaches.
• Customer Onboarding: Review how new customers move through onboarding and where delays or drop-offs occur. Order management covers the same question on the fulfilment side.
• Claims Processing: Examine how insurance or healthcare claims move through review and handling.
• Manufacturing and Supply Chain: Review production and supply chain processes to investigate delays, quality issues, or resource constraints.

Event data may come from ERP systems such as SAP, Oracle, and Microsoft Dynamics; CRM platforms such as Salesforce and HubSpot; helpdesk tools such as ServiceNow and Zendesk; or custom applications. Whether a system’s data is suitable depends on whether you can identify cases, activities, and timestamps.

Browse the library by industry, department or transformation role, or start from a process improvement guide for your process and system. Each guide names the fields to export, the activities to capture, and the data template to fill from the source system.

Process Mining vs. Data Mining and Machine Learning

Process mining, data mining, and machine learning analyze data in different ways. Process mining focuses on how cases move through activities over time. Data mining looks for patterns in datasets. Machine learning uses data to make predictions or classifications.

The key difference is the question each method answers. Process mining keeps the sequence of activities in view. It can show that a case waited at a handoff or repeated an activity. Data mining may identify patterns across records, while machine learning can estimate the likelihood of an outcome.

These methods can complement each other. You can use process mining to understand the flow and identify variation, then apply other analytical methods to investigate patterns or predict outcomes. Predictions do not explain or change the process by themselves.

For more terms used in process analysis, see the ProcessMind glossary.