Manual tasks are often hidden inside daily operations, making it hard to see where time is wasted or where errors happen. Without clear visibility into user-level actions, process improvement and automation become guesswork. Task mining in process data mining solves this by capturing detailed desktop activity.
This article covers what task mining is, how it works, its role in process data mining, and the use cases where it adds the most value.
Read our article “What is Process Data Mining? Applications and Benefits” to explore its fundamentals and practical advantages for businesses.
What is Task Mining?
Task mining is the process of capturing and analyzing how users perform tasks on their computers. It collects data from mouse clicks, keyboard strokes, and screen activity to understand user-level actions.
The main goal is to find patterns in these actions that can help improve productivity, reduce manual effort, or prepare for automation.
Task Mining versus Robotic Process Automation (RPA)
Purpose: Task mining discovers how users perform tasks; RPA automates those tasks using predefined rules.
Sequence: Task mining is used first to collect data; RPA comes later to execute automation based on that data.
Function: Task mining captures real user behavior; RPA mimics that behavior without user involvement.
Use case: Task mining helps identify what should be automated; RPA focuses on doing the automation.
Flexibility: Task mining adapts to variations in how users work; RPA requires clearly defined, structured tasks.
Task Mining versus Process Mining
Data Source: Task mining collects data from user desktops; process mining uses event logs from enterprise systems.
Focus Level: Task mining looks at individual user actions; process mining focuses on entire business workflows.
Visibility: Task mining reveals manual steps and shortcuts users take; process mining shows system-level transactions.
Detail Depth: Task mining provides granular task-level insight; process mining gives high-level process mapping.
Integration: Task mining fills gaps in process mining by capturing what systems don’t log, especially in manual work.
How Task Mining Works
1. Capture data from user devices
Task mining tools collect data by monitoring how users interact with their desktops. This includes tracking mouse clicks, keystrokes, window switches, and application usage. The data is collected either through sensors or lightweight software agents installed on the device.
2. Classify events and group actions
Once raw data is captured, the system labels each action as a specific event, like “open file” or “copy text.” These events are then grouped into sequences that represent complete tasks. This step helps convert scattered user actions into meaningful units of work.
3. Recognize patterns and analyze behavior
The grouped actions are analyzed to find repeated sequences and common paths users follow. The system detects patterns such as frequent copy-paste cycles or manual data transfers. These insights help spot inefficiencies, rule-breaking, or inconsistent workflows.
4. Generate task flows and visual output
Finally, the system creates a visual map that shows how tasks are performed step by step. These flowcharts or timelines show variations across users and sessions.
The output makes it easier to understand current work habits and prepare for automation or standardization.
If your workflows rely on consistent and clean data, eSystems can help you lay the right foundation for automation readiness.
Key Functionalities of Task Mining
1. Track desktop interactions
Task mining tools continuously monitor user actions on their screens, like mouse movements, typing, or switching between applications. This helps collect a full picture of how tasks are done in real working environments. It captures details that system-level tools often miss.
2. Record screens and map events
Some tools record screen activity to visually link user behavior with events. The system detects and timestamps each event based on screen changes. This mapping makes it easier to analyze both what happened and how it looked during execution.
3. Visualize task-level flows
After collecting and mapping the data, the tool builds visual workflows of individual tasks. These task flows show each action in sequence, along with duration and frequency. This helps understand task complexity, effort, and variations across employees.
4. Identify manual and repetitive work
One of the main goals of task mining is to spot tasks that are repeated often and done manually. These tasks are usually time-consuming and prone to error. Identifying them helps decide where automation or process improvement will have the most impact.
For organizations aiming to connect user-level activity with central systems, eSystems’ Master Data Management ensures your data is structured and synchronized across tools.
Role of Task Mining in Process Data Mining
1. Add user-level details to system logs
Process mining relies on system event logs, but those logs often miss what happens on a user’s desktop.
Task mining fills this gap by adding user-level actions like copy-paste, form entry, or data lookup. This combined data gives a complete view of both system and manual steps in a process.
2. Provide context from human actions
Task mining explains why certain process paths are taken by showing user behavior. For example, if a user opens an external file before submitting a form, that step is visible only through task mining. This added context helps analysts understand process variations and decision points.
3. Detect automation opportunities
By analyzing repeated and rule-based user actions, task mining identifies tasks that can be automated.
It helps spot activities like manual data entry or switching between two systems to copy information. These findings guide where to apply automation for the best return.
4. Improve accuracy of process discovery
Without task-level data, process discovery can miss important variations or steps handled manually.
Task mining improves discovery by showing real user paths, exceptions, and workarounds. This leads to more accurate process maps and better decision-making for redesign or automation.
Pairing task mining insights with eSystems’ MDM solutions helps you avoid data silos and build more accurate, actionable process maps.
Task Mining Use Cases
1. Reduce repetitive desktop tasks
Task mining helps identify tasks users perform again and again, such as renaming files or entering the same type of data.
These tasks consume time and are prone to error. Once identified, they can be redesigned, automated, or removed.
2. Build automation with real user data
Instead of guessing what to automate, task mining shows actual user workflows. It provides data on how different users complete the same task, including edge cases and exceptions.
This makes automation design more accurate and aligned with real behavior.
3. Optimize customer service processes
Customer service teams often handle tickets, switch tools, and follow manual scripts. Task mining captures these activities in detail.
The insights help streamline workflows, reduce time per case, and standardize best practices across agents.
4. Support process data mining with detailed tasks
Process mining tools can show where tasks begin and end, but they don’t explain every step taken within them.
Task mining adds this layer by breaking down what happens between system events. This detail helps process analysts make better decisions for improvement.
If you’re exploring task automation or improving your data flow, eSystems offers proven solutions to centralize and enrich your master data across systems.
Conclusion
Task mining helps organizations understand how users perform tasks and where improvements can be made. It captures actions that system logs often miss and adds depth to process analysis.
When used together with solutions like eSystems’ Master Data Management, companies can improve data quality, create better workflows, and identify clear opportunities for automation. Task mining is not just a tool for observation. It is a practical step that supports cleaner operations and smarter decisions across systems.
About eSystems
At eSystems, we simplify digital transformation through low-code, automation, and data-driven solutions. We focus on creating scalable systems, automating manual work, and cleaning and managing data across your applications. Our expertise in Master Data Management helps organizations connect user-level task insights with centralized process data.
If you're using task mining in process data mining and want to improve data accuracy, remove manual bottlenecks, and build automation-ready workflows, we at eSystems are ready to help. Start your journey with us and turn insights into action.
FAQ
1. What is task mining in process data mining?
Task mining captures user interactions, like clicks, keystrokes, and screen activity, at the desktop level to reveal how tasks are actually performed. It adds detailed context to system logs, making process analysis more complete.
2. How does task mining differ from process mining?
Task mining focuses on user-level actions captured from desktops, while process mining analyzes event logs from enterprise systems. The two combined provide both micro- and macro-level insights into workflows.
3. When should I use task mining alongside process mining?
Use task mining when you need to understand manual desktop activities that system logs don’t capture. It helps explain variations and edge cases in broader process flows revealed by process mining.
4. Can task mining help identify automation opportunities?
Yes, task mining spots repeated, manual desktop steps like copy-paste or data entry, clearly showing where automation or RPA bots could replace manual work.
5. Does task mining improve the accuracy of process discovery?
Task mining fills the gaps left by event logs by showing real user actions, exceptions, and workarounds, leading to richer, more accurate process maps.
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