Processes often look simple in plans but behave differently in real systems. Teams face delays, rework, and unclear responsibilities because they don’t see the actual flow of work. These issues stay hidden when decisions rely only on designed process maps.
Process discovery offers a way to expose what really happens inside business operations by using system data. This article covers how process discovery works in process data mining, which techniques are used, and what challenges to prepare for.
Read our article "What is Process Data Mining? Applications and Benefits" to explore how process data mining works and why it matters.
Process data mining means using event logs from business systems to understand how work is actually done. An event log is a record of activities, such as when an order is created, approved, shipped, or closed. These logs help you see the real steps people follow in a process.
Instead of relying on how the process is supposed to work, process data mining shows how it really works based on real data.
There are three main types of process data mining.
This method builds a process model directly from event data. It does not need any existing documentation. It reads what actually happened in the system and shows the complete flow of activities.
This method compares the real process with the one that was originally planned or designed. It shows where people skipped steps, repeated actions, or did things in the wrong order.
This method improves the current process by adding more information from actual data. It helps businesses update their process based on how it is really being used.
Process discovery is a technique that creates a process model by reading event logs. It works without any input from older process maps or manuals. The software simply reads the records and figures out the order of steps in the process.
Many companies think their processes follow a certain path. But the data often tells a different story. Process discovery helps reveal the exact flow that employees and systems actually follow in day-to-day work.
Process discovery is important because it helps you find the truth about how work is getting done. It shows the difference between what managers believe and what employees actually do.
It can point out where work is done more than once, which wastes time. It also shows where work is delayed or stuck, which helps in solving process slowdowns.
When you understand the real flow, it becomes easier to automate parts of the process. It also helps in checking if people are following the rules or not. This makes the business more efficient and more reliable.
Want to uncover the real workflow behind your business processes and fix what's slowing you down? Start exploring process discovery with expert guidance.
The Alpha algorithm is one of the earliest process discovery methods. It works by checking the order in which events happen in an event log. It uses that order to create a process model.
This method is useful when the logs are clean and follow a clear pattern. But it struggles when the data has noise, missing entries, or complex loops. Because of that, it is not often used alone in real business settings.
Heuristic mining looks at how often certain paths or actions happen. It helps filter out rare or incorrect data and focuses only on common behavior.
This makes it better for real-world business processes where not everything follows a perfect path. It can still show the main structure while ignoring small or random errors. Heuristic mining is often used in companies that deal with messy or incomplete data.
Inductive mining creates process models that are both readable and reliable. It finds patterns like loops, decisions, and parallel actions in the data.
This method makes sure the process model can be followed without getting stuck or lost. It is a better choice when you need a clear and correct process map for deeper analysis or automation.
Low-code platforms can speed up process discovery by building applications that automatically capture and connect event data. eSystems uses platforms like Mendix and OutSystems to create digital workflows. These tools help make process steps traceable and visible across all systems.
With Workato as the integration tool, eSystems connects data from multiple systems. This makes it easy to see the full process, even when data is stored in different places. It also helps clean and standardize the data during the process.
If your current systems hide how your processes really work, eSystems can make your data flow clear and usable. Want to uncover your real workflows without building everything from scratch?
Get in touch with eSystems to explore low-code process discovery solutions that fit your business.
When master data is messy or stored in different systems, it becomes hard to track how a process actually works. That’s where eSystems’ master data management strategy plays a role.
eSystems helps set up 2-way data synchronization. This means when data is updated in one system, it also updates in all connected systems. As a result, event logs become complete and reliable. This is critical for accurate process discovery.
With eSystems, you don’t just clean up your data. You make it work across departments, tools, and business units. Want to stop chasing down missing or duplicated data and start seeing the full picture of your operations?
Talk to eSystems and see how MDM can unlock hidden value in your process mining.
When event logs are missing steps or contain wrong data, the discovered process model becomes misleading. This can result in bad decisions, failed automation, or delays in fixing process issues.
To solve this, companies should improve how data is recorded and ensure all systems capture complete event details. Checking data quality before running process discovery is a must.
Some business processes do not follow a clear path. They may involve many decisions, loops, or exceptions. This makes it hard to create a clean and readable process map.
As a result, the final model becomes confusing and hard to use for process improvement. Using inductive mining or splitting large processes into smaller parts can help manage this complexity.
When systems don’t talk to each other, parts of the process remain hidden. Data silos break the chain of events, making it impossible to see the full workflow from start to end.
This is where eSystems can help. By using low-code platforms and integration tools like Workato, eSystems connects data across systems. This makes your processes visible, connected, and ready for accurate discovery. If disconnected data is holding your process mining back, reach out to eSystems and fix the flow from the source.
Process discovery helps uncover how business processes truly work by using real data from event logs. It supports better decisions, smoother automation, and stronger compliance. With the right techniques and complete data, businesses can find gaps, fix delays, and improve performance.
But challenges like missing logs, complex workflows, and disconnected systems must be handled properly. Solving these problems ensures that process discovery gives a clear and useful picture of real operations.
We are eSystems, a low-code consulting and delivery company that helps organizations simplify and automate business processes at scale. Our work focuses on building flexible, high-speed digital solutions using tools like Mendix, OutSystems, and Workato.
In the context of process discovery in process data mining, we support businesses by removing data silos, enabling two-way synchronization, and creating process transparency across all systems. This helps you discover how your processes really run and gives you the tools to fix, improve, or automate them.
If your business is ready to replace assumptions with real insight, we’re here to help.
Get started with eSystems and make process discovery part of your digital strategy today.
Process discovery is the method of creating a process model from event logs. It shows how a business process actually runs based on real data.
It helps identify the real flow of work, not just what’s on paper. This reveals gaps, delays, and rework in business processes.
Techniques include Alpha Algorithm, Heuristic Mining, Inductive Mining, low-code automation, and master data synchronization for full process visibility.
Key challenges include incomplete logs, complex workflows, and disconnected systems. These can lead to incorrect or unclear process models.
It gives a clear view of actual processes, helping to fix problems, reduce waste, and support automation and compliance.