Slow approvals, repeated mistakes, and unclear responsibilities are common issues faced by companies around the world. Many teams do not know where their processes break down or why certain steps take longer than expected.
Tasks are delayed, rules are missed, and resources are wasted — but no one can clearly explain where the problem starts. This happens because most companies do not have full visibility into how work moves through their systems on a daily basis.
To fix this, it is not enough to rely on reports or manual checks. What companies need is a way to see the full picture of their process using real system data. This is where process data mining becomes useful. It helps teams understand what is really happening inside their operations.
This article is about the key benefits, practical applications, and core techniques of process data mining.
Process data mining is a method used to study how things are actually done in a system. It works by using event data, which are records of each step in a process. These records usually come from software systems that people or machines use during their work.
The main goal is to find out how a process works in real life, not how people think it works. Process data mining shows the real paths, delays, and mistakes that happen. It helps find patterns by looking at the order of tasks, how long they take, and who performs them.
There are three main types of process data mining:
Process discovery – finding the actual process flow from event data.
Conformance checking – comparing the real process with the expected process.
Enhancement – improving the process using what the data shows.
This method is useful because it uses real data, not guesses or opinions.
Process data mining gives many useful benefits to teams and companies. These include:
Finding real process flows: It shows how work actually moves through a system.
Spotting delays and bottlenecks: It helps find where tasks slow down or get stuck.
Checking compliance: It shows if the process follows company rules or industry standards.
Improving decisions: It provides facts based on data, which helps managers make better choices.
Tracking performance over time: It lets teams measure how changes affect the process.
Because the method uses system data, it gives a clear and honest view of what’s happening.
To get the most out of these benefits, teams need accurate and connected data behind every process. That’s where solutions like eSystems can quietly power the performance you expect from process mining.
Here are some common areas where it's applied:
Healthcare: Hospitals use it to track patient flows, reduce wait times, and improve care steps.
Finance: Banks use it to check how loans are processed or to detect fraud in transactions.
Logistics: Delivery companies use it to study how packages move and fix delays in routes.
Manufacturing: Factories use it to improve production steps and avoid machine downtime.
Customer service: Support centers use it to study how service tickets are handled and how to respond faster.
Human Resources (HR): HR teams use it to check how long it takes to hire people, process employee requests, and onboard new staff.
Supply Chain: Businesses use it to follow product movement, check inventory handling, and fix delays in order processing.
In each case, the goal is the same, to understand what’s really happening and make the process better.
Process data mining depends on correct and complete data. If the base data is wrong, the process mining results will not show the real process. That is why master data management is important. It keeps important data like customer names, product codes, and employee records accurate across all systems.
eSystems helps fix common problems in master data. These problems include missing values, duplicate entries, and different formats in different systems. By cleaning and standardizing the data, eSystems makes sure that process mining works with reliable information. This leads to better models and more useful insights.
Another key point is data synchronization. Many companies use many systems. If one system updates and the others do not, the event logs become confusing.
eSystems solves this by creating two-way synchronization. This means if you change a record in one place, it updates in all other connected systems. For process mining, this gives a full and correct view of the process.
Master data also helps in understanding who owns what data and who is responsible for keeping it correct. eSystems automates many of these steps. This reduces manual work and helps teams stay focused on process improvement.
If your goal is to get real results from process mining, you must start with strong data. You can see how eSystems supports better master data to help you get more value from your process analysis.
Process mining and data mining are two different methods used to learn from data, but they focus on different things.
Process mining looks at event data to understand how processes actually happen. It uses time-stamped logs from systems to show the real flow of work, step by step.
Data mining finds patterns in large datasets. It looks for trends, groups, or rules, but it does not focus on the steps of a process.
In simple words, Process mining explains how things happen in order, whereas data mining explains what patterns are inside the data.
Data cleaning is the step where raw event data is fixed and made ready for analysis. In process data mining, this step is very important because messy data gives wrong results.
Common cleaning tasks include:
Removing missing values, like skipped steps or missing timestamps
Fixing wrong formats, such as incorrect dates or names
Filtering noise by removing actions that do not belong to the process
Merging logs from different systems to build a complete view
Clean data makes sure the analysis shows the real process correctly.
Cleaning event data can be complex across disconnected systems. With automation and integration tools from eSystems, teams can reduce this effort and focus on what matters, using clean data for real process improvement.
Process discovery means building a process model from event logs without knowing the model beforehand.
Common techniques include:
The Alpha algorithm, which looks at the order of steps to build a model
Heuristics miner, which works well with messy real-world data
Fuzzy miner, which focuses on the most common paths and hides the rare ones
An inductive miner that builds full models that work even for complex processes
These methods help turn raw event data into process models.
Event logs are the main input used in process data mining. Each event log is a list of records that shows what happened, who did it, and when it happened.
An event log usually includes:
The case ID, which shows which instance of the process the event belongs to
Activity that tells what action was taken
A timestamp that records the time of the action
A resource that shows who or what system performed the action
By analyzing event logs, you can understand the real process, see where delays happen, and find problems like repeated work or skipped steps.
Process enhancement means using the results from process data mining to make the process work better. It helps improve how tasks are done by using real data instead of guesses.
Once the event logs are studied and the process model is made, the team can:
Find steps that take too long
Remove tasks that are repeated without reason
Add new steps to fill gaps
Change the order of tasks to save time
Adjust resources to balance the workload
The goal is to make the process faster, simpler, and more useful by using facts from data.
Conformance checking is a way to compare the actual process with the expected process model. It shows if the real work follows the rules or breaks them.
This is how it works:
The system checks the event logs against the designed process model
It looks for any extra steps, missing steps, or steps done in the wrong order
It marks these changes as deviations
Conformance checking helps find compliance problems. It is useful in fields like banking, healthcare, and legal work where rules must be followed.
Root cause analysis is used to find out why problems happen in a process. Instead of only seeing where the process went wrong, it finds out why it went wrong.
Process data mining helps by:
Studying the event data to see patterns linked to problems
Checking which tasks often lead to delays or mistakes
Finding conditions that cause changes in the process
For example, if some tasks always slow down when done by a certain team or during a certain time, the data will show that. This helps teams fix the real issue, not just the surface problem.
Task mining focuses on what people do at their computers during a process. It captures actions like mouse clicks, typing, and opening files or tools.
It uses software to record how tasks are done on a screen. These records are then turned into a log, just like in process mining.
Task mining is helpful when:
Event logs are not enough
Small manual tasks are not recorded by systems
You want to see the user steps inside the software
Task mining gives a detailed view of user actions and helps improve work at the task level. It works best when combined with process mining for full process understanding.
Process data mining is a practical way to see how work actually happens inside systems. It helps teams find delays, check compliance, and improve processes based on real data. But the quality of insights depends on the quality of the data used.
To get useful and accurate results, organizations must focus on clear, complete, and well-managed data. With the right foundation in place, process data mining can support better decisions and stronger performance across many business areas.
eSystems is a digital transformation partner that helps businesses simplify and automate their processes. We focus on master data management, low-code development, and system integration.
Our goal is to make your data more accurate, your processes easier to manage, and your systems ready for change.
We work closely with teams to clean and unify their core data, automate routine tasks, and build flexible solutions that support real process improvement. If you are looking to get reliable insights from process data mining, it starts with the right data and automation in place.
Get started with process data mining by building the right foundation with us.
Process mining software works by reading event data from systems like ERP or CRM tools. It builds a visual model that shows how tasks are done in real life. This helps identify delays, rework, or steps that do not follow the expected process. With the right master data in place, platforms like eSystems can support more accurate and useful mining results.
Process mining can be used by any team that runs structured processes. It is common in finance, procurement, human resources, logistics, and healthcare. Whether the goal is speed, compliance, or cost savings, process mining gives real insight into what needs fixing.
Business intelligence tools report on data like totals or trends. Process mining goes further by showing the step-by-step flow of work. It helps answer how and why something happened, not just what happened. This makes it useful for improving actual processes, not just tracking them.
Process mining helps find slow steps, repeated actions, skipped approvals, and rule violations. It gives a clear picture of real-time performance and reveals where a process breaks down. Teams can then make changes based on facts instead of assumptions.
Yes. Process mining tools can bring in data from many different systems to show a full process. When supported by strong data synchronization and integration tools like those from eSystems, it becomes easier to keep the data clean, connected, and ready for analysis.