Task mining utilizes user interaction data, also known as desktop data, to assess the efficiency of a task within a larger process. This type of data is inclusive of keystrokes, mouse clicks and data entries that occur as part of completing a given operation. 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:
While task mining can yields many benefits, the most common ones that can be realized are the following:
However, task mining is not without its challenges. Some of them include the following:
IBM Process Mining provides an integrated platform combining process mining and task mining. This gives its clients a transparent view into their business processes in near real-time, making it easy to find and prioritize the best tasks for automation. IBM Process Mining exists to provide companies with technology that can help them save time, effort, and money.
IBM Cloud Pak for Business Automation is a flexible set of integrated software that helps you design, build and run intelligent automation services and applications on any cloud, using low-code tools.
Take your business from simple to intelligent automation, adding AI and machine learning to automate processes and reach your goals. IBM Process Mining provides data obfuscation, anonymization, and encryption features to protect sensitive data, and it allows clients to selectively choose what desktop applications and websites to record in order to capture only data considered relevant to a business’s project initiatives.