When a process breaks, slows down, or does not follow the expected steps, teams often do not know why. The problem is not always the process design. It is the lack of visibility into how the process actually runs. Event log analysis in process data mining offers a way to trace every step using real system data.
This article explains how event log analysis works, what data it uses, and why it is key to understanding and improving business processes.
Read our article “What is Process Data Mining? Applications and Benefits” to learn the basics and how it can help your organization.
What is Event Log Analysis?
Event log analysis is the process of studying digital records called event logs. These logs capture every step or action taken in a software system.
Each time a user does something, like submitting a form or finishing a task, the system creates an event log.
Analyzing these logs means checking the order, timing, and details of these actions to understand how the process really works. It helps uncover how tasks are done, who performs them, and when.
Why Event Log Analysis Matters in Process Mining
Event log analysis is a key part of process mining because it shows what is actually happening inside a business process. Instead of relying on opinions or guesses, it uses system data to reveal the exact steps followed in real operations. This helps companies see the true workflow, from start to end.
By studying event logs, teams can detect delays, repeated steps, skipped tasks, or wrong sequences. It also shows how long each step takes and who is involved.
This level of detail makes it easier to find problems in a process, like bottlenecks or unwanted variations.
Most importantly, event log analysis allows continuous monitoring. Once the real process is known, businesses can compare it with the ideal process. This helps improve the workflow and keep it aligned with goals. It also supports auditing, compliance checking, and automation.
Struggling to see how your processes actually run? Learn how eSystems helps you uncover real workflows using event log data.
Key Components of Event Log Analysis
Events: Individual records that show when a specific action occurs in a system
Activities: Specific tasks or steps performed in a process (e.g., “approve invoice”)
Cases: A complete instance of a process (e.g., one customer order or one claim)
Timestamps: Time data showing when an event happened
Resources: People or systems that performed the activity
Types of Data Found in Event Logs (timestamp, activity, resource)
Event logs hold structured data that describes what happened in a process. Each log is made up of fields that provide useful details. The most common fields are timestamp, activity, and resource.
A timestamp shows the exact date and time when an event happened. It helps you track the duration between steps, find delays, and measure performance. Without timestamps, you can’t see how fast or slow a process runs.
Activity tells what was done during the event. For example, it could say “submit form” or “approve request.” This helps map the actual steps of a process and check if they follow the correct order.
The resource identifies who or what performed the activity. It could be a person, a team, or a system. This field helps understand workload, check if tasks are done by the right people, and detect gaps in responsibility.
All three fields are required to make the process analysis accurate and complete.
How Event Log Analysis Works in Process Data Mining
1. Collection of Event Logs from Systems
Event logs are collected from systems that support business operations. These include enterprise tools like ERP systems, customer platforms, ticketing systems, or workflow apps. Every time someone performs an action such as creating a file, submitting a request, or updating a record, the system records it as an event.
This event includes the action, the time it happened, who did it, and other related details. These logs are stored in system databases, servers, or cloud storage. For process mining, all this event data must be exported and gathered into a centralized dataset.
Collecting logs from different systems is the first step to seeing how a process behaves across the entire organization.
2. Structure and Clean the Data
After collection, the event log data is often messy or unstructured. It may contain missing timestamps, incomplete activities, or wrongly labeled resources. To prepare the data for analysis, it must be cleaned and structured.
Cleaning means removing errors such as duplicate entries or incomplete rows. Structuring means organizing the data into clear columns like case ID, activity name, timestamp, and resource.
This makes sure every process instance is traceable from start to end. If this step is skipped, the analysis will be inaccurate and can lead to wrong decisions.
Dealing with messy or incomplete logs? Learn how eSystems helps you automate data cleaning and structuring for reliable analysis.
3. Discover Process Models from Logs
Once the data is clean and structured, tools can use it to generate a process model. A process model is a visual map that shows the real flow of activities over time.
It connects each event in the correct sequence based on timestamps and case IDs. This model shows which activities are common, which paths are followed most, and how tasks move from one stage to another.
Unlike process diagrams made by people, this one comes from actual data. It helps teams see what is really happening in the workflow, not just what they think should happen.
4. Use Log Analysis to Find Deviations and Bottlenecks
After the process model is created, it is checked for problems. A deviation happens when the process takes a different path than expected. For example, a step may be skipped, or an approval may happen before a review.
A bottleneck is when tasks pile up at one point and slow the whole process. These issues are found by measuring how long each step takes, how often it repeats, and how often it breaks the usual flow.
This helps identify which parts of the process need fixing to improve speed, quality, and consistency.
Missing hidden process issues? Learn how eSystems helps you detect bottlenecks and deviations through automated log analysis.
5. Enable Master Data Visibility with eSystems
eSystems helps make sure the master data used in event logs is complete and connected. Many logs come from systems that manage data like customer details, product codes, or employee records.
If this data is spread across multiple platforms, it can cause errors or gaps in the logs.
eSystems checks all data sources and creates a unified view. This helps find missing fields, mismatches, or outdated records that can affect the analysis. With better visibility into master data, the process model becomes more accurate and useful.
6. Automate Log Collection and Synchronization with eSystems
eSystems uses low-code tools and integration platforms like Workato to automate how data moves between systems. Instead of manually copying event logs from one system to another, automation keeps them updated in real time.
If a user changes a record in one place, that change is automatically reflected in all other connected systems. This two-way synchronization reduces human error and keeps the event data consistent.
When logs are accurate and always up to date, the process mining results are more reliable.
Key Benefits of Event Log Analysis
Better Visibility into Business Processes
Event log analysis gives a full view of how a business process actually works in real time. It shows the exact steps taken, who performs them, and when each step happens.
This visibility helps teams move away from assumptions or outdated process maps. By using event data, they can see if tasks are completed in the right order, how often they repeat, and where delays occur.
This level of detail is useful for both managers and analysts because it reveals how work really flows across departments and systems.
Data-Driven Process Improvement
With event log analysis, decisions are based on facts, not opinions. The data shows where the process is slow, where tasks get stuck, and how long each step takes.
Teams can use this information to change specific parts of the process, test new steps, or remove waste. This makes improvement efforts more focused and measurable.
Instead of changing the whole process blindly, teams can fix the exact parts that are broken. Over time, this leads to faster workflows, fewer errors, and better results.
Identify Compliance Issues and Process Gaps
Event log analysis also helps in checking whether a process is followed as required. Some tasks must be done in a fixed order to meet legal or internal rules.
The event logs show if steps are skipped, done too early, or handled by the wrong person. This makes it easier to catch compliance problems before they cause damage. It also helps spot missing steps, repeated actions, or weak handoffs between teams.
By finding these gaps, companies can fix broken workflows and avoid risks tied to non-compliance or poor quality control.
Challenges in Event Log Analysis
Incomplete or Noisy Logs
Incomplete or noisy logs are a common issue in event log analysis. Some systems may not record all events, while others may capture too many irrelevant ones.
Missing timestamps, duplicate entries, or logs with wrong case IDs make it hard to trace the real process. This lowers the accuracy of the process model and makes insights unreliable.
Solution:
To fix this, logs should be cleaned and validated before analysis. Use filters to remove errors and fill in missing data where possible.
eSystems helps solve this challenge by reviewing master data across different systems and improving data health. This increases the accuracy of logs by reducing missing fields and removing duplicates. When the master data is more complete and aligned, the logs generated from it also become more reliable.
Complex and Unstructured Processes
Some business processes have many paths, optional steps, or unplanned workarounds. This makes the event logs hard to analyze.
The process model becomes too large or too messy, which hides useful insights. It is also difficult to know if variations are valid or if they show errors in the workflow.
Solution:
To manage this, analysts should group similar cases and compare them to a reference model. Process mining tools can also simplify the model using filters or abstraction levels. Adding domain knowledge helps decide which variations are allowed.
eSystems supports this by creating visibility into the full process through unified data and automated process discovery.
When processes are mapped using accurate master data, it becomes easier to filter out noise and focus on valid patterns.
Scalability and System Integration
When event logs come from many systems or across departments, collecting and syncing data at scale becomes difficult.
Systems may store data in different formats, update at different times, or use different naming rules. Without smooth integration, the logs remain isolated and hard to analyze together.
Solution:
To solve this, systems must be connected and synchronized in a consistent way. eSystems offers automation using low-code platforms and tools like Workato. These tools allow event logs to be collected from different sources and synchronized in real time.
When one system is updated, others reflect the change. This avoids data mismatches and keeps logs accurate across platforms, even at a large scale.
Conclusion
Event log analysis helps you see how business processes really work by using system data instead of guesses. It shows the exact steps, timing, and people involved. This helps find delays, mistakes, or skipped steps.
When the data is collected, cleaned, and analyzed correctly, teams can improve their workflows, fix problems, and stay on track with rules. While there are challenges like missing data or system issues, good tools and clear methods can make the analysis strong and reliable.
About eSystems
We are eSystems, a low-code transformation partner focused on simplifying, automating, and scaling business processes.
Our core strength is building fast, flexible, and reusable solutions using platforms like Mendix, OutSystems, and Workato. These tools help businesses gain real-time visibility and better control over complex systems and data.
In the context of event log analysis in process data mining, we support organizations by improving data quality, automating log collection, and connecting disconnected systems. We help uncover how processes really work by turning raw data into reliable insights across systems.
If you are ready to turn your system logs into process insights and drive real operational change, get in touch with us today.
FAQ
1. What is event log analysis in process mining?
Event log analysis is the study of system-generated records to see how processes actually run. It helps discover real steps, timing, and actors in workflows.
2. Why is event log analysis important for process improvement?
It shows where delays, skipped steps, or repeated actions happen. This helps teams fix broken parts and improve overall process flow.
3. What kind of data is used in event log analysis?
Event logs use data like timestamps, activities, and resources. These fields help track what happened, when, and who did it.
4. How does event log analysis detect process bottlenecks?
It measures the time and sequence of each step to spot slow points. This helps identify where work piles up or moves too slowly.
5. Can event log analysis help with compliance monitoring?
Yes, it checks if tasks follow the required rules and order. It flags skipped or wrongly executed steps for audit and correction.
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