What is task mining?

Task mining vs. process mining

Task mining and process mining are AI-enhanced tools that help organizations make operational improvements, but the two approaches have different purposes.

Task mining surfaces granular user interaction data. By capturing an employee’s clicks and keystrokes, it reveals specific actions taken during daily work. The resulting insights help identify inefficiencies, leading to an improved employee experience and productivity.

Process mining, on the other hand, extracts broader business data from IT systems. It captures real-time event logs to create an end-to-end visualization of process performance. This helps organizations identify opportunities for optimization and value enhancement across the entire system.

While task mining delves into the specifics of employee tasks, process mining offers a broader perspective on overall process flow and performance. When used together, these technologies complement each other, providing a comprehensive understanding of how businesses operate and facilitating improvements in efficiency and effectiveness.

Some companies use process mining first to map processes and identify problem areas. They then use task mining to get a closer view of how employees work. Other companies start with task mining to see what drains employees’ time. Then, they apply process mining to see the bigger picture and the causes of those issues.
Task mining in action: Five example

Task mining in action: Five example use cases

Task mining improves applications across many industries, increasing efficiency and productivity.

Retail industry: Task mining helps retail companies analyze their order fulfillment processes to identify inefficiencies and automate tasks. This leads to faster fulfillment, increasing customer satisfaction and revenue.

• Business operations: By optimizing processes like invoice processing and approvals, task mining reduces future errors and improves supplier relationships. Plus, it identifies repetitive tasks suitable for automation, leading to improved use of resources and better efficiency.
• Customer service: Task mining analyzes agent interactions and tool usage to detect customer service bottlenecks. With the resulting data, companies can reorganize workflows, improve time to resolution (TTR), and ensure customer satisfaction.
• Sales and marketing: Organizations can use task mining to analyze successful sales representatives’ activities and optimize their lead-generation programs. This helps companies fill their pipeline and boost revenue.
• IT support and management: Task mining improves incident resolution by identifying patterns in support ticket data. With better incident resolution, the IT department reduces downtime and learns ways to prevent future issues.

Task mining also has a variety of cross-functional use cases, like improving documentation for training processes, streamlining workflows, and identifying compliance errors.

How task mining works

Let’s take a look at how task mining works in six steps:

• Data collection
  Task mining software runs in the background of user desktops, collecting data on their computer interactions. It records activities such as mouse clicks, application usage, keyboard inputs, copying and pasting, scrolling, and time spent on tasks. Some tools may also capture screenshots or recordings to add visual context to the collected data.
• Context recognition
  Task mining tools often use technologies like optical character recognition (OCR) to extract text from screenshots or recordings. OCR helps the software associate specific employee actions with the tasks they contribute to, providing a better understanding of the workflow.
• Task identification
  Task mining focuses on identifying individual tasks rather than entire processes. In this context, a task is an activity performed by a user as part of a larger process. From the data the software collects, task-mining tools use artificial intelligence (AI) and machine learning (ML) to understand what tasks the user is performing.
• Pattern recognition
  Advanced algorithms sift through the data to identify recurring tasks, like a common data entry mechanism or repeated error resolution. The software also recognizes individual employees’ different paths to task completion, offering valuable insights for process improvement.
• Automation recommendations
  Based on the analysis of user interactions and identified patterns, task-mining tools generate recommendations for automation. These may include suggestions for automating repetitive tasks, optimizing workflows, and streamlining processes. For example, it may propose that a team needs training in a specific area or a process needs restructuring.
• Visualization and reporting
  The software presents the results of the task mining analysis in user-friendly visualizations and reports. Graphs, charts, heatmaps, and diagrams let stakeholders track performance metrics over time, monitor the impact of automation efforts, and make informed decisions for process optimization.

Once the process is complete, it restarts. Task mining is not a one-time activity but a continuous process of monitoring, analyzing, and optimizing tasks and workflows.

Benefits of implementing task mining

Task mining has become popular among businesses since it runs independently in the background and provides helpful insights in just days.

Challenges of implementing task mining

While task mining can bring numerous benefits, it also comes with challenges. Understanding these issues and developing strategies to overcome them helps ensure your success with task mining.

Common challenges in task mining include:

• Resistance to change: When you introduce task mining, you may face resistance from team members. They may be skeptical about its benefits or balk at the idea of their actions being monitored. To prevent this, you’ll need to leverage change management. Consider hosting information sessions and offering training opportunities to gain stakeholder buy-in and support.
• Data privacy and security concerns: Task mining captures and analyzes user interactions, which raises privacy concerns among employees. You’ll need to take active measures to protect sensitive information and comply with relevant regulations.
• Limited data availability: Task mining needs access to sufficient data to offer insights. If your business produces incomplete or sparse datasets, your software may struggle to recognize patterns and inefficiencies.

With foresight and planning, you can overcome these challenges to produce meaningful results.

Best practices for successful task mining

After you install the software, task mining runs on its own. Still, there are a few best practices you can implement to ensure you get the results you want.

• Engage stakeholders early on: Involve representatives from IT, HR, operations, and other relevant functions to define software requirements, prioritize initiatives, and set measurable objectives. By collaborating with employees, you gain valuable insights and help them see the value of task mining.
• Align task mining with organizational goals: Ensure that task mining initiatives align with your organization’s strategic goals. By focusing on processes with the greatest impact on business outcomes, you maximize your potential improvements and boost your competitive advantage.
• Start with a pilot project: Begin with a small-scale test project with a few users. This trial run produces data and visualizations you can use to demonstrate the approach’s benefits and gain further support from stakeholders.
• Continuously monitor and evaluate: Regularly track and assess the outcomes of task mining initiatives to identify areas for improvement and make necessary adjustments. This helps you optimize your processes and save more time and resources.

Learn how Nintex Process Discovery offers insights into processes to improve your operational efficiency.