Processing data from business systems often comes with missing values, wrong timestamps, or inconsistent activity names. This raw data cannot be used directly for analysis because it gives false results and hides real process issues.
Many teams run into delays or wrong conclusions because they skip the step of cleaning their event logs. To get useful insights from process data mining, the data must first be cleaned and prepared. This article is about how to clean process data for accurate and reliable Process Data Mining.
Read our article "What is Process Data Mining? Applications and Benefits" to learn about process data mining.
What is Data Mining?
Data mining means finding useful patterns or facts in large sets of raw data. The goal is to understand what is happening in the data and use that to make better decisions.
It works by using tools and algorithms to find trends, connections, or groups in the data. For example, it can be found that people who buy one product also often buy another. It helps in areas like sales, fraud detection, and customer behavior.
Data mining does not care about how or when a process happens. It looks at the whole data and finds patterns, no matter where the data came from.
This is different from process data mining, which only works with event logs. Process data mining focuses on how a process actually happened step by step and builds a model to show that path. Data mining focuses on what is inside the data. Process data mining focuses on how data flows through a system.
What is Process Data Mining?
Process data mining is the method of discovering how a business process works by looking at event logs. An event log is a list of steps that happen in a system, like "create order," "approve order," and "ship product."
Process data mining reads these logs to find the real steps followed in a process. It builds a visual model that shows the actual flow of work, not the expected one, but what really happened. This helps find problems like delays, extra steps, or rule violations.
This method needs clean and correct data. If the logs are missing steps or have errors, the results will be wrong. That’s why data cleaning is a key part of the process of data mining; it helps make sure that the event log is complete, accurate, and ready for analysis.
What is Data Cleaning in Process Data Mining?
Data cleaning means fixing and preparing event logs before using them for process data mining. An event log is a list of actions that happen in a system. These logs are not always ready to use.
They may have errors, missing parts, or mixed formats. Data cleaning makes sure that the event log is correct, complete, and ready for analysis.
Role of Data Cleaning in Process Data Mining
Raw event logs often come from different systems. Each system might store data in a different way. This creates problems like missing steps, wrong time entries, or mixed-up activity names.
If these issues are not addressed, the process data mining tool will build a process model that is not true.
That model may show extra steps, skip real ones, or place actions in the wrong order. Cleaning makes sure the tool reads the real path of the process.
Key Objectives of Data Cleaning
The goal of data cleaning is to make the event log correct and useful. This includes three main tasks.
First, make sure every field in the log is accurate.
Second, remove events that do not belong to the actual process, such as test runs or system messages.
Third, use one common format for all values, like using the same date format or the same label for repeated activities.
This helps tools read and group the data without confusion.
Common Data Quality Issues in Event Logs
1. Missing Values
Sometimes, event logs have empty fields. For example, the timestamp or user ID might be missing. This makes it hard to place events in the right order or to group them by person or case. Missing values must be filled using rules, or the record should be removed if it cannot be fixed.
2. Incorrect Timestamps
Timestamps must show the real time when an activity happened. If the time is wrong, the event order will be wrong too. This can happen if the system clock is not correct or if logs come from different time zones. All times must be fixed to match one standard format and time zone.
3. Inconsistent Activity Names
The same activity can be written in different ways in different systems. One log may say "Submit Request," and another may say "Request Sent" for the same step. These names must be unified. Otherwise, the process model will treat them as two separate actions, which gives wrong results.
4. Duplicates and Redundant Events
Some events are recorded more than once. Others may show the same step that happened twice due to system errors. These duplicates must be removed. Keeping them gives a wrong view of how long or how often a step happened.
5. Out-of-Order Events
In some logs, the steps are not in the right order. For example, the system may record "Approve Request" before "Submit Request" because of time sync issues.
This makes the process map wrong. Cleaning must fix the order based on correct timestamps and logic rules.
Steps of Data Cleaning in Process Data Mining
Step 1: Extract Event Logs
Start by collecting raw event logs from all systems that record user actions, like ERP, CRM, ticketing, or workflow tools. Each log should include:
Case ID: a unique identifier for each process instance
Timestamp: the exact time of each activity
Activity Name: What action was performed
To do this, use export functions from databases or APIs. Tools like SQL queries or ETL (Extract, Transform, Load) pipelines can help pull data in bulk from different platforms. Always make sure the log is event-based, not just transaction-based, for accurate analysis.
Step 2: Filter and Remove Noise
Noise includes irrelevant system logs, technical errors, test runs, or entries from failed transactions. These don’t reflect real business steps and should be removed.
Techniques to filter noise:
Apply event type filters (e.g., only keep “complete” events)
Use activity frequency analysis to spot outliers
Remove logs with very short durations, as these may be system-generated
Filtering can be done using Python scripts or directly within process mining tools like Disco or Apromore.
Step 3: Handle Missing or Wrong Data
Data issues like empty timestamps, wrong user IDs, or invalid case IDs are common. These need careful fixing.
How to handle:
Use data validation rules to detect missing fields
Apply forward filling (using the last valid value) for timestamps
Replace incorrect entries using reference tables or domain rules
Drop cases only if they cannot be safely fixed
You can use pandas in Python for structured cleaning or rely on data quality plugins in ETL tools.
Step 4: Standardize Names and Formats
Standardization means making sure that similar actions are labeled the same way and that all fields follow one format.
Techniques:
Create a mapping dictionary for different versions of activity names (e.g., “PO Approve” → “Approve Purchase Order”)
Convert timestamps to ISO 8601 format for uniformity
Normalize case IDs using regex (e.g., remove special characters or unify length)
This step helps process models group activities correctly, avoiding duplicates or broken traces.
Step 5: Check Event Order and Logic
Each process has a natural order. If events appear in the wrong sequence, it may be due to clock mismatches, logging errors, or manual mistakes.
Fixing this involves:
Sorting events by timestamp within each case
Using process constraints (e.g., "Activity A must always come before B") to validate order
Comparing with a reference model to detect and flag logic violations
Some tools can auto-detect anomalies, while others need manual rules. Ensure that parallel activities are correctly marked if they can occur in any order.
Step 6: Automate Data Consistency Checks
Instead of doing manual reviews each time, use automation to enforce consistency across logs.
With eSystems, you can set up low-code workflows using platforms like Mendix or OutSystems. These tools help:
Automatically apply cleaning rules across systems
Detect duplicates and fix formatting errors
Enforce naming conventions and field requirements
This not only reduces manual work but also keeps the event logs in sync with ongoing process changes.
Want to make data cleaning faster and more accurate? eSystems helps automate this step for you with custom workflows built around your system setup.
Step 7: Synchronize Data Across Systems
Logs often come from different software tools, and if the same event is updated in one but not others, your process mining model breaks.
eSystems solves this by:
Enabling 2-way data synchronization across platforms
Using automation to update fields in real-time across connected systems
Managing data ownership rules so there is no confusion over who controls what
This step prevents conflicts, ensures consistency, and removes the need for constant manual updates.
If keeping your data consistent across tools is a challenge, eSystems can help you build a unified master data setup that works smoothly behind the scenes.
Tools Used for Data Cleaning in Process Data Mining
Spreadsheet Tools
Spreadsheets like Excel or Google Sheets are used to clean event logs when the data is small or simple. You can use filters to remove rows, find duplicates, or check for missing values. Formulas help detect incorrect entries. Conditional formatting is used to highlight errors or patterns.
Scripting Languages (Python, R)
Python and R are used when the data is big or more complex. These languages offer libraries to handle missing values, fix data formats, remove outliers, and check event sequences.
Python’s pandas and R’s dplyr are popular tools. Scripting also helps repeat the cleaning steps automatically each time new data comes in.
Process Data Mining Platforms with Built-in Cleaning Options
Some process mining platforms have built-in tools to clean data before analysis. They help detect duplicate events, correct order issues, and remove logs that do not fit the process model. You can apply filters, rename activities, and match data from different sources. This makes cleaning easier for users who do not code.
Master Data Automation with eSystems
eSystems helps clean and manage data using automation and low-code tools. We use platforms like Mendix, OutSystems, and Workato to remove manual work.
Our solutions fix messy data, keep formats consistent, and update records across systems without errors. Data ownership and control are also improved so fewer mistakes happen.
eSystems also enables two-way data sync, which means if a change is made in one system, it is updated everywhere else.
This protects master data from becoming inconsistent. Our team builds custom workflows that match your exact process and system setup.
Want to make your data cleaning simple and repeatable? Talk to eSystems about how automation can support your process data mining needs.
Importance of Data Cleaning in Process Data Mining
Better Process Discovery and Visualization
Clean data helps show the real process path. It makes the process model clear, with no fake steps or gaps. This helps teams understand what is really happening in their operations.
Reliable Performance Metrics
If the event log is clean, the metrics like duration, frequency, and rework rates are correct. Wrong data gives wrong numbers. Clean logs ensure the data tells the truth about how the process performs.
Effective Bottleneck Detection
Bottlenecks are spots where the process gets stuck. If the log has missing times or extra steps, the bottlenecks may be hidden. Clean data makes it easier to find delays or parts where work piles up.
Meaningful Insights for Business Improvement
When the data is correct, you can trust the process insights. It helps decide where to improve, what to remove, and how to make the process faster. Clean logs are the starting point for any change that needs facts.
Conclusion
Data cleaning is the most important step before using data mining. Without clean event logs, the results will be wrong or hard to trust. Cleaning helps fix missing values, correct wrong entries, and make the process steps clear. It also makes tools work better and gives true insights.
By using the right tools and methods, teams can prepare data in a way that makes the whole process easier to study, improve, and manage.
About eSystems
We at eSystems help organizations clean, manage, and automate their data using low-code technologies. Our core work is focused on simplifying complex processes and making data easier to handle.
We use platforms like Mendix, OutSystems, and Workato to fix issues such as duplicates, missing values, and out-of-sync records across systems. This is how we support accurate, fast, and repeatable Data Cleaning in Process Data Mining.
If your team is struggling with messy event logs or scattered process data, we can help you build automation that keeps everything clean and in sync. Let us simplify your data cleaning process. Get started with us today.
FAQ
1. What is data cleaning in process mining?
Data cleaning in process mining means fixing errors, filling missing values, and formatting event logs to make them ready for accurate process analysis.
2. Why is data cleaning important in process mining?
It ensures the event log is complete and correct, so the process model shows real steps without errors or false insights.
3. What are common data quality issues in process mining?
Common issues include missing timestamps, duplicate events, wrong activity names, and steps recorded in the wrong order.
4. What tools are used for data cleaning in process mining?
Tools include spreadsheets, Python or R scripts, process mining platforms, and low-code automation platforms.
5. What are the steps of data cleaning in process mining?
Steps include extracting logs, removing noise, fixing missing data, standardizing formats, checking order, and syncing across systems.
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