Process mining and task mining are two of the hottest trends in process optimization today. With leading vendors like Celonis and UiPath building out both capabilities, it can be confusing when each technology is most appropriate.
In this comprehensive guide as a data analytics expert with over 10 years of experience, I‘ll analyze the key differences between task mining and process mining across three dimensions:
- Data collection and analysis
- Scope and granularity
- Complementary strengths
Understanding these distinctions is essential to determine when to use each solution. First, let‘s provide some background.
An Introduction to Process and Task Mining
Process mining utilizes algorithms to analyze structured event log data from enterprise systems like ERPs and CRMs. It provides end-to-end visibility into processes by mapping out sequences of events, identifying bottlenecks, and highlighting deviations.
Task mining captures detailed user interaction data like clicks, keystrokes, applications used, text selections, and more. It focuses on steps and actions within specific tasks to optimize user performance.
While process mining takes a top-down approach, task mining analyzes bottom-up data to drive process improvements. But how exactly do they differ?
Key Differences in Data Collection and Analysis
Process mining and task mining rely on fundamentally different data collection and analysis techniques:
Data Collection
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Process mining extracts event log data as a byproduct of transactions. Events may include order created, payment processed, etc.
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Task mining uses desktop analytics to explicitly record clicks, typing, applications used, etc. to capture user actions.
Analysis Techniques
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Process mining utilizes algorithms and statistical analysis to model processes and find deviations.
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Task mining leverages computer vision, OCR, NLP and rules engines to analyze input patterns and build predictive models.
Data Sources
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Process mining connects to IT systems like ERPs, CRMs, supply chain systems.
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Task mining tracks data within productivity tools like Office 365 and desktop applications.
Use Cases
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Process mining improves cross-functional processes like order-to-cash.
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Task mining optimizes repetitive individual tasks like data entry.
This table summarizes some of the key differences:
| Dimension | Process Mining | Task Mining |
|---|---|---|
| Data collection | Logs from IT systems | Desktop analytics |
| Analysis techniques | Algorithms, statistics | Computer vision, NLP, rules |
| Data sources | Enterprise systems | Productivity tools |
| Use cases | Cross-functional processes | Repetitive individual tasks |
With these differences in data and analysis, process mining and task mining are suited for different use cases. Process mining optimizes end-to-end workflows, while task mining improves specific repetitive tasks.
Different Scope and Granularity
Process mining and task mining also differ significantly in terms of scope and granularity:
Scope
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Process mining maps out complete end-to-end processes with multiple interconnected tasks.
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Task mining analyzes the detailed steps within a singular task in the process.
Granularity
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Process mining visualizes tasks, sequences, variants at a high level.
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Task mining captures each click, keystroke, and action within a task in minute detail.
For example, consider the procure-to-pay process:
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Process mining would analyze the full cycle from purchase requisition to invoice payment.
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Task mining would focus specifically on the invoice receipt task by examining each step taken by an AP clerk.
The scope and level of detail differs significantly:
| Dimension | Process Mining | Task Mining |
|---|---|---|
| Scope | End-to-end processes | Single tasks |
| Granularity | Tasks, sequences, variants | Clicks, keystrokes, actions |
Think of process mining as the big picture view while task mining zooms into the details within processes.
Complementary Strengths
While process mining and task mining are distinct technologies, they are incredibly powerful together.
Process mining provides the high-level view but lacks granular visibility into manual tasks. Task mining fills these blindspots by capturing desktop actions.
On the other hand, task mining alone loses sight of the broader process context. Combining it with process mining connects the dots.
Together, process mining and task mining enable drilling up and down between macro process flows and micro user actions. This drives truly data-driven decision making.
Here are some examples of how they complement each other:
Cross-Departmental Processes
- Process mining maps the end-to-end workflow between departments
- Task mining analyzes steps within departments to identify bottlenecks
Deviation Analysis
- Process mining flags deviations from the happy path
- Task mining uncovers why deviations occur through user actions
Impact Analysis
- Process mining models the current workflow
- Task mining analyzes implications of changes on user tasks
By leveraging both process and task mining, organizations gain comprehensive visibility into operations for targeted improvements.
Key Takeaways
While process mining and task mining are often conflated, they are distinct technologies with different strengths:
- Process mining optimizes end-to-end workflows using system event log data
- Task mining enhances repetitive tasks through detailed user action analysis
To choose the right solution, focus on your use case – cross-functional processes or specialized tasks.
Even better, combine process and task mining to get the best of both worlds. Process mining provides the 30,000 ft view while task mining zooms into the details. Together, they offer comprehensive visibility to make data-driven decisions.
Both technologies are critical elements of a successful process excellence program. If considering process and task mining solutions, download my Vendor Selection Guide or contact me to discuss your needs. With over 10 years of experience in process optimization, I‘m happy to provide guidance to maximize your success.