What is task mining?

This technology then uses optical character recognition (OCR), natural language processing (NLP), and machine learning algorithms to interpret and analyze this data, which in turn enables analysts and other stakeholders to identify operational inefficiencies. Task mining solutions are considered part of process discovery, a subset of process mining, and according to Gartner’s “Market Guide for Process Mining” the market for this technology is rapidly growing. As the COVID-19 pandemic continues to fuel digital transformation efforts, adoption of task mining technology is anticipated only to increase as the benefits of it are fully realized.

Process mining focuses on end-to-end process optimization, such as an overall procurement process, whereas task mining focuses on the individual tasks that ladder up to that larger process, such as budget approval for accounts payable. They also primarily differ in the types of data that they utilize for each analysis. Process mining primarily relies on business metrics and event log data from information systems, such as Enterprise Resource Planning (ERP) or Customer Relationship Management (CRM) tools. Task mining, on the other hand, can leverage user interaction data, which includes keystrokes, mouse clicks, or data entries on a computer; it can also include user recordings and screenshots at different timestamp intervals. These data points help analysts and researchers understand how individuals are interacting with a process and sub-process to complete a task. They both also leverage data science techniques to arrive at these insights to optimize processes; task mining just enables this at a more granular level.

While task mining and RPA both focus on process automation, the two technologies are different but complement each other well. While task mining technology helps businesses identify bottlenecks in their process workflows, RPA tools implement and execute against the automation opportunities that are discovered through these analyses.

Task mining tools start by collecting data from users’ machines, which can include keystrokes, clicks, user inputs, recordings, screenshots and more. From there, optical character recognition capabilities can add additional context about what the user is doing. For example, it might look at the timestamp data to help assemble a general timeline of activities in a sub-process. Once that data is structured appropriately, machine learning algorithms can be leveraged to cluster data into specific tasks in the sub-process, such as “submitting a purchase order.” The data can then be combined with event log data to help contextualize performance. This view into the data then allows businesses to discover bottlenecks and fix them accordingly.

Task mining techniques have been used to improve process flows across a wide variety of industries. Process maps can help businesses focus more on the key performance indicators (KPIs) that matter, spurring them to reexamine their operational inefficiencies through process mining and task mining.

Some use cases of task mining include:

• Task Documentation: As new team members onboard, documentation is frequently reviewed to close any knowledge gaps. However, depending on the project and the available resources, documentation may not always be available or up to date. Task mining tools provide a way for teams to bring insight into a task in a larger process, creating alignment across the team. It also reduces the need for individual dependences, providing an easy way to build documentation and visualizations through process mapping and other automation tools.
• Governance and Compliance: As businesses face stricter government regulations, task mining can help to hold companies accountable by identifying areas where compliance errors occur. This visibility can provides a pathway to resolving these issues more quickly, potentially saving companies on costs, such as legal fees and negative brand publicity.
• Task Automation: Since task mining produces a clearer view into specific sub-processes, it can also enables program managers and people managers to understand which parts of the process can be automated through tools like RPA.

While task mining can yields many benefits, the most common ones that can be realized are the following:

• Increased efficiency: Task mining focuses on identifying operational bottlenecks to expedite process improvement. As those inefficiencies are found and rectified, companies experience increased velocity across tasks. If a task in a process has been over-resourced, it can also lead to staff reallocation to other priority work, potentially improving employee morale by finding them more meaningful work.
• Better compliance: Tasking mining tools collect data from users, enabling governance teams to determine breakdowns in compliance during specific tasks. This ability to pinpoint problems and resolve them quickly can help facilitates better governance and compliance across the business.
• Greater transparency: Task mining can provide workforce insights at the individual level, enabling managers to give valuable feedback during performance check-ins and reward employees fairly for their work. It can also help them to reallocate employees to different work if it doesn’t seem to be a great fit.

However, task mining is not without its challenges. Some of them include the following: 

• Data privacy: Since task mining has the capability to record and log user actions, it can also raise concerns around privacy. As a result, these tools should be approved by the user before being activated and they should also protect users’ personal data through appropriate anonymization.
• Missing context: Since task mining focuses on a sub-process within a larger one, the larger context of performance can sometimes get lost. It’s important to leverage task mining technology in conjunction with process mining to get a fuller picture of performance across teams; otherwise, companies risk prioritizing task optimizations that do not have the greatest impact to the business.
• Concept drift: As businesses move quickly to transform for the digital age, tasks and processes can change in real-time. Changes in tasks and processes can impact analyses, leading to concept drift.