Having too much data but no clear understanding of what’s going wrong in business operations is a common problem. Teams often face delays, broken processes, or poor decisions because they don’t know whether to examine the data itself or the process behind it.
Without choosing the right method, efforts to improve performance can fail. This article is about the difference between process mining and data mining, and how to choose the right one for your needs.
Read our article "What is Process Data Mining? Applications and Benefits" to get familiar with process data mining.
What is Data Mining?
Data mining is the process of finding patterns, relationships, or useful information from large sets of structured data. It helps turn raw data into knowledge that supports decision-making.
What is Process Mining?
Process mining is the technique of analyzing event logs from business systems to discover how processes actually work. It shows the real steps, order, and flow of a process based on data.
Struggling to connect your data with how your business actually runs? eSystems helps you bring both together to fix processes and make better decisions.
Use Case of Data Mining
Predicting customer churn based on past behavior
Detecting fraud in banking transactions
Recommending products based on purchase history
Classifying emails into spam or non-spam
Grouping customers by buying patterns (customer segmentation)
Use Case of Process Mining
Finding bottlenecks in order-to-cash processes
Checking if employees follow the correct workflow
Comparing real process vs. designed process for compliance
Reducing delays in customer support ticket handling
Improving claims processing time in insurance companies
Managing process flows across apps is hard. eSystems builds connected systems that make this easy for business teams.
Difference between Process Mining and Data Mining
1. Objective and Focus
Process mining focuses on understanding how business processes actually happen. It shows step-by-step actions in a process.
Data mining focuses on finding hidden patterns or trends in large sets of data, not tied to any specific process.
2. Type of Data Used
Process mining uses event logs. These logs come from systems like ERP or CRM and include data like case ID, activity name, and timestamp.
Data mining uses structured data like rows and columns in databases. It does not need timestamps or event sequences.
3. Output and Insights
Process mining gives visual models of processes. It shows the flow, bottlenecks, and deviations.
Data mining gives patterns, rules, or predictions. For example, it can predict customer churn or find buying patterns.
4. Sequence and Time Dependency
Process mining depends on the order of events and their timing. The sequence helps build the actual process flow.
Data mining does not focus on event order. Time or sequence is not necessary to find patterns.
5. Techniques and Tools
Process mining uses algorithms like process discovery, conformance checking, and enhancement.
Data mining uses techniques like clustering, classification, regression, and association rules.
6. Application Area
Process mining is used for process analysis, compliance checks, and workflow optimization.
Data mining is used in areas like marketing, fraud detection, finance, and healthcare analytics.
7. Level of Detail
Process mining provides detailed, case-level insights. It shows what happened in each process instance.
Data mining provides high-level trends and summaries across the whole dataset.
8. Role in Business Improvement
Process mining helps improve how processes are carried out by identifying delays or rework.
Data mining helps improve decisions by showing patterns that can guide strategy or operations.
Benefits of Data Mining
Helps find hidden patterns in large datasets
Supports better business decisions using data insights
Improves customer targeting through behavior analysis
Detects fraud or unusual activity in transactions
Predicts future trends using historical data
Increases efficiency by automating data analysis
Benefits of Process Mining
Shows how business processes actually work using event logs
Finds delays, rework, or unnecessary steps in workflows
Checks if processes follow rules or compliance standards
Improves process performance by fixing weak points
Gives clear process maps without manual modeling
Tracks change in process behavior over time
Want to fix slow processes and bad data across your systems? eSystems can help with automation and process visibility.
Which One to Choose: Data Mining or Process Mining?
If you are looking for patterns in customer behavior, data mining will help you. It finds trends across large datasets without needing process steps.
If you want to understand how a business process really works, process mining is a better choice. It uses event logs to show the actual flow and steps.
If your goal is to make predictions like sales forecasts or risk scores, go with data mining. It uses past data to build models that predict future outcomes.
If your goal is to improve how fast or smoothly a process runs, use process mining. It helps you see delays, bottlenecks, or steps that don’t follow the rules.
If you don’t have timestamped event logs, then data mining is more useful. Process mining needs time-based logs to build the process view.
If you are doing compliance checks or audit trails, process mining gives a clear picture. It shows if the actual process matches the expected process.
If you want to group or classify large sets of data, data mining is designed for that. It helps in segmenting and labeling based on shared features.
If your workflows need both process control and clean data, eSystems offers tools to handle both using low-code platforms.
Conclusion
Process mining and data mining are two different techniques with different goals. Data mining helps find patterns in large datasets, while process mining helps understand how processes actually work. Both are useful, but solve different problems.
If you want insights from raw data, use data mining. If you want to fix or monitor processes, use process mining. Knowing when to use each can help improve your decisions, workflows, and overall business performance with more clarity and control.
About eSystems
eSystems helps businesses manage both their data and their processes — not one or the other. We clean, standardize, and synchronize master data across systems, while also building automated, end-to-end business processes using low-code platforms like Mendix, OutSystems, and Workato.
This is why the term “Process Data Mining” fits what we do. We combine insights from structured data (like in data mining) with real workflow understanding (like in process mining). That means we help you find what’s broken, fix it, and automate it — across both data and process levels.
Let’s talk about your process and data challenges with eSystems.
FAQ
1. What is the difference between process mining and data mining?
Process mining analyzes how processes actually work using event logs. Data mining finds patterns in large datasets without focusing on processes.
2. When should I use process mining instead of data mining?
Use process mining when you want to see the real workflow steps. Use data mining when you want trends or predictions from raw data.
3. Is process mining a type of data mining?
No, process mining and data mining are separate techniques. They use different data types and serve different goals.
4. What data is used in process mining?
Process mining uses event logs with timestamps from business systems like ERP or CRM.
5. What are common use cases of data mining?
Data mining is used for fraud detection, customer segmentation, product recommendations, and predictive analytics.
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