Many organizations design their business processes with strict rules, but often have no clear way to verify whether those rules are actually followed. Teams may rely on assumptions or surface-level reports without knowing where the real gaps or deviations are. Conformance checking solves this by comparing real process data with how the process was meant to run.
This article explains how conformance checking works, the techniques involved, real-world examples, key benefits, and the role of master data management in improving its accuracy.
Explore our article “What is Process Data Mining? Applications and Benefits” to discover the basics and how it can help your organization.
What is Conformance Checking?
Conformance checking is a method used in process data mining to compare how a process actually runs with how it is supposed to run. It checks if the real behavior of a process, recorded in event logs, matches the steps and rules defined in a process model. If the process follows the model, it is conformant. If not, the differences are marked as deviations.
In a business process, a model might describe the correct flow for handling a customer order or approving a payment. Each time these actions happen, they are recorded in an event log. Conformance checking takes this log and compares it with the model to find steps that were skipped, added, delayed, or done in the wrong order.
These deviations help teams understand whether processes are being followed correctly or if changes are happening without approval. Some deviations may point to mistakes, while others may show where the model needs to be updated. Either way, conformance checking helps reveal what is actually happening.
It is one of the main approaches in process data mining, along with process discovery and process enhancement. Conformance checking is especially useful for organizations that need to follow strict rules or want to improve how their processes are managed.
How Conformance Checking Works
1. Role of event logs and process models
Event logs are records of how processes are carried out in real systems. Each log includes events like task name, timestamp, user, and case ID.
A process model, on the other hand, shows how the process is supposed to work, often defined using BPMN or Petri nets.
Conformance checking needs both: the event logs show actual behavior, and the model sets the standard for comparison.
2. Match real data against the reference model
The event log is matched against the process model to trace each case through the expected flow.
If every step in the event log aligns with the steps and order in the model, the process is considered conformant.
This matching is done using algorithms that compare sequences of activities and timestamps to find where the flow breaks or changes.
3. Identify deviations or unexpected behavior
Any mismatch between the event log and the process model is called a deviation. This can include missing steps, added tasks, changes in order, or delays.
By identifying these deviations, conformance checking highlights where the process is not working as planned. These findings help in fixing control gaps, improving training, or updating the process model.
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Techniques Used in Conformance Checking
1. Token-based replay
Token-based replay uses tokens to simulate how a case moves through the process model. Tokens are created and consumed as activities are matched. If all tokens move through the model correctly and end in the right place, the case is conformant.
If tokens are missing, left behind, or added unexpectedly, this signals a deviation. This method is fast and gives a good overview, but it may not explain the exact point of failure.
2. Alignment-based checking
Alignment-based checking tries to find the best possible match between the event log and the process model, even if they differ. It aligns every step in the real trace with a step in the model using a cost-based approach.
For example, skipping a step or doing an extra step has a cost. This method shows the minimal set of changes needed to make the trace fit the model. It is accurate but slower and more resource-intensive than token-based replay.
3. Automata-based analysis
Automata-based methods convert the process model and event log into automata — a type of state machine. These automata are then compared to find mismatches.
This technique is good at detecting unusual paths and rare behavior, especially in complex or flexible processes.
It also supports partial matching and process variants, but can become complex to manage for large models.
4. Strengths and limitations of each method
Token-based replay is fast and simple, but may miss detailed explanations.
Alignment-based checking is precise and shows exact deviations, but uses more memory and processing time.
Automata-based analysis handles complex or flexible processes well but is harder to configure and interpret.
Choosing the right method depends on the process size, required detail, and available system resources.
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Examples of Conformance Checking Using Event Logs
1. Extra activity that shouldn’t occur
Sometimes, a step is performed in a process that isn’t part of the approved model. For example, an employee adds an extra approval step that slows down the process without adding value. Conformance checking flags such activities by comparing event logs with the original process model. These extras might suggest manual workarounds, compliance risks, or training gaps.
2. Missing required step in the process
If a required activity is skipped — like a quality check or approval — the event log will show a gap.
Conformance checking identifies this by spotting missing events in the sequence. This kind of deviation is serious because it can affect product quality, safety, or regulatory compliance.
3. Activities done in the wrong order
Order matters in most business processes. If a shipment is sent before payment is received, it can cause financial risk.
Conformance checking uses timestamps in the event log to detect whether the order of steps matches the model. Wrong sequencing often points to weak process controls or unclear instructions.
4. Delays between activities violate the rules
In some processes, certain steps must happen within a specific time window. For example, a support ticket must be assigned within 2 hours.
Conformance checking looks at time gaps between steps to find violations. These timing issues may suggest workload problems, system delays, or missing alerts.
5. Loops or unnecessary repetitions in tasks
Sometimes, a task gets repeated more than it should, like a document being reviewed multiple times without reason. These loops increase process time and reduce efficiency.
Conformance checking detects repeated patterns that don’t align with the expected process. This helps teams investigate whether the repetition is caused by unclear decisions, lack of authority, or broken automation.
Benefits of Conformance Checking
1. Detects non-compliance early
Conformance checking helps detect when real processes don’t follow set rules or policies. This early detection allows teams to correct problems before they grow, reducing risks related to compliance, audits, and legal issues.
2. Helps improve process efficiency
By finding delays, repeated steps, or extra activities, conformance checking shows where time and resources are being wasted. This helps businesses streamline their processes, reduce costs, and shorten cycle times.
3. Supports auditing and accountability
Since conformance checking uses detailed event logs, it creates a clear record of who did what and when. This traceability helps in internal and external audits and supports accountability in regulated industries.
4. Enables better decision-making through insights
The insights gained from deviations are not just about fixing errors. They help decision-makers understand where processes need to be redesigned or where tools and roles need to change.
5. Strengthens process model accuracy
When discrepancies are found, organizations can refine their process models to reflect real-world behavior better. This continuous feedback loop between the model and actual data improves the accuracy of future analysis and planning.
Conformance Checking in Process Data Mining
Conformance checking in process data mining is used to compare actual process executions with a predefined process model. The goal is to find out whether the real process followed the expected path or if it deviated.
In process data mining, event logs are collected from systems like ERP, CRM, or workflow software. These logs contain records of activities — what happened, when, and by whom. Conformance checking uses this log data and compares it with a reference model to detect mismatches. A reference model represents how the process is supposed to work based on rules or best practices.
There are different types of deviations that conformance checking can detect. For example, an activity might be missing, added, repeated too many times, or done in the wrong order. These deviations help teams understand where the process is breaking down or being bypassed.
Conformance checking is not just about finding errors.
It helps in identifying process risks, non-compliance issues, and performance bottlenecks. For example, if a mandatory approval step is skipped frequently, it could be a sign of weak internal controls.
In deeper applications, conformance checking is also used to evaluate how different branches or departments follow the same process. This helps in standardizing operations across the organization.
Conformance checking is one of the three main parts of process data mining, along with process discovery (finding the process model from data) and process enhancement (improving the model with new insights). Together, they give a complete view of how business processes work and where they need improvement.
Role of Master Data Management in Accurate Conformance Checking
Conformance checking compares real-life process data with a reference model to identify if a process is followed correctly. This comparison depends heavily on accurate and reliable event logs, which are built on master data, such as customers, suppliers, products, or employees.
If the master data is messy, outdated, duplicated, or incomplete, the event logs generated from it will also be flawed.
Master Data Management (MDM) is the process of organizing and maintaining consistent, correct, and connected data across all systems in an organization. In conformance checking, if one process refers to a supplier name “ABC Ltd.” and another uses “A.B.C. Limited,” the system might treat these as two separate entities.
This causes false deviations, where the process appears to have gone off-track when in reality, it hasn’t. Such errors waste time, mislead teams, and lead to poor decision-making.
Effective MDM ensures that all systems refer to the same version of the truth. This is especially important in process data mining, where data from many sources is combined and analyzed. Without proper MDM, even advanced conformance checking techniques will produce incorrect results because the data itself is not trustworthy.
eSystems helps organizations build MDM systems that reduce manual cleanup, prevent duplication, and maintain consistency across platforms. By automating the management and synchronization of master data, eSystems enables accurate conformance checking and more dependable process insights.
Want to ensure your conformance checking gives reliable results? Let eSystems strengthen your process data through smart master data management.
Conclusion
Conformance checking is a key part of process data mining that helps compare how processes actually run versus how they should run. It relies on clean event logs, accurate process models, and the right analysis techniques to detect deviations.
These insights help improve efficiency, support compliance, and guide process improvements. For reliable results, the quality of your underlying data matters. That is why managing your master data correctly is essential for making conformance checking truly effective.
About eSystems
eSystems is a Nordic technology partner that helps organizations simplify and automate their business processes using low-code platforms, smart integration, and modern data management. We focus on solving real problems like scattered data, system backlogs, and process inefficiencies through tools that are fast, flexible, and built to last.
In conformance checking, accuracy depends on the quality of your process data. That is where we come in. We help you clean and synchronize your master data, automate event logging, and make sure your systems produce the consistent data needed for reliable conformance analysis.
If you want your conformance checking to reflect the truth and not just assumptions, let eSystems help you get your process data right from the start.
FAQ
1. What is conformance checking in process mining?
Conformance checking compares actual process behavior with a predefined model to find deviations. It helps verify if processes follow expected rules.
2. Why is conformance checking important?
It helps detect errors, policy violations, and inefficiencies in business processes. This improves compliance, transparency, and process control.
3. What are examples of conformance checking?
Examples include skipped approvals, extra steps, or tasks done in the wrong order. These are found by analyzing event logs against the model.
4. What techniques are used in conformance checking?
Common techniques are token-based replay, alignment-based checking, and automata-based analysis. Each has different strengths based on accuracy and speed.
5. How does master data affect conformance checking?
Poor master data causes false deviations and incorrect analysis. Clean, consistent data improves the accuracy of conformance checking results.
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