Process Mining & Task Mining: Business understanding on user and system level

Process Mining and Task Mining – both terms appear again and again in the discussion about Automation, Artificial Intelligence and modern process management. Both methods are intended to create a better understanding of running processes in the company. But what are the differences between the methods for process visualization and which should companies implement?

Challenges of modern processes in the company

Process management in the enterprise is undergoing a transformation with digitization. With the increasing shift of essential business processes into the digital world, decision makers are confronted with a number of challenges:

  • Complexity

An end-to-end process involves many different steps and often affects different areas in the company. This makes the process complex and can hardly be overlooked by a single person within the process.

  • Definition and reality

Process definitions are created as part of the introduction of quality management or ERP systems. But the definition does not always correspond to actual practice within the company. Time and again, shadow processes arise in companies. Employees circumvent the standardized procedures – for example, because the definition is inefficient or not practical.

  • Introduce Robotic Process Automation (RPA)

Process automation has the potential to automate many tasks, improve internal efficiency, customer and employee satisfaction, and reduce errors. However, the prerequisite for process automation is a precise knowledge of the process to be automated.

Understanding the critical business processes is the central challenge of process management. Process Mining and Task Mining are two tools to overcome this challenge.

Process and Task Mining: Two Approaches to Business Processes in the Enterprise

Dynamic teams, complex structures: Achieving consistent processes vis-à-vis customers and partners is a major challenge in the digitized enterprise. A recent study shows that more than half of the decision-makers surveyed see factual process information as the greatest added value.

Process managers are detectives in this respect. Process and Task Mining are two ways to solve the case. As in a criminal case, they examine the crime scene and answer the question: what happened here? The difference lies in the investigative approach. Every process leaves traces in the form of data:

  • Business data stored as event logs by the IT systems involved.
  • User data recorded at the individual workstation

Process Mining: Understanding processes through IT systems

For a long time, log files were only of interest to the system administrator if he had to correct a faulty configuration. But log files contain essential information from which conclusions can be drawn about the underlying process. Process Mining uses event logs to reconstruct the underlying process.

Process Mining is basically a software-supported process audit. The data from the event logs are collected, read, analyzed and visualized. This results in 3 advantages of Process Mining:

  • Display processes completely

Digital processes can be captured and analyzed from the first point of contact to the end through Process Mining. This is an essential prerequisite for end-to-end automation with RPA.

  • Use historical data

By evaluating event logs, historical data on the analyzed processes is also available, which contributes to a better overall picture and forms the basis for developing viable predictive models.

  • Gain valuable insights

The evaluation of event logs and the visualization of processes provide insight into the inner structure of essential business processes and thus allow a data-based prioritization of optimization measures.

Process Mining naturally has a clear limitation. Since Process Mining analyzes event logs of IT systems, all processes and process steps that leave no traces in the systems are excluded from the analysis.

Task mining: understanding processes through the user

Instead of analyzing processes at the server level, Task Mining starts at the individual user’s desktop. Software is installed on the user’s computer that records all of the user’s interactions. This includes all keystrokes, mouse clicks and other inputs. Compared to Process Mining, Task Mining puts the cart before the horse. These are 4 advantages of Task Mining:

  • Capture process steps outside of IT systems:

Event logs only store process steps that run within the IT system. However, a completely digital process chain is not real in most use cases and companies. Even with a high degree of digitization, essential process steps take place outside of CRM, ERP or marketing suite. These steps are not considered in Process Mining. Activities such as invoice receipt are left out. Task Mining is able to map exactly these steps and thus provide a more complete picture of the process

  • Optimization of all processes

This results in a robust database for optimizing even those processes that do not run within the IT systems. Pointless process optimization measures are avoided, while meaningful cases can be identified better and more precisely.

  • Identifying shadow IT

Not every process works in reality as the IT systems intend. Users are good at finding ways to circumvent procedures that are perceived as inefficient – for example, by using alternative software solutions (shadow IT). Processes can then no longer be correctly recorded and optimized at the organizational level.

  • Localizing systemic problems

Task Mining reveals individual deviations from the existing process definitions. If these deviations occur frequently, this often points to a systemic problem in the process definition. The process may not be feasible at all, or it may be extremely cumbersome. Resourceful employees quickly find a workaround, which is identified in this way.

The user-centric approach in software-based process analysis is not a panacea. Task Mining is not sufficient for automating complex processes from end to end. RPA tools must later perform the exact steps required. Real people, however, function differently than computers. They proceed individually and are flexible in their actions. The data generated by Task Mining is therefore not sufficient for the software robot. Although it is also possible to automate manual clicks and entries, automation at the software level is often more efficient and more resilient.

Task Mining or Process Mining: False Alternative

For understandable reasons, Task Mining and Process Mining are often discussed against each other. Process Mining in particular is resource-intensive in the first step. Companies want to prioritize the measures that promise a short-term benefit. But: Process Mining and Task Mining deal with the same issue – only from two different perspectives.

Whereas Task Mining starts from the individual user and his actions and develops the individual process steps from there, Process Mining captures the process from the result. For RPA and comprehensive End-to-end Automation, Process Mining is necessary to understand the steps within the IT system. Task Mining can help identify user-level deviations and incorporate them into future process design.

Both methods pursue the overall goal of developing a comprehensive understanding of business processes. Only the use of both methods leads to a comprehensive picture of the processes running in the company. For companies, it is therefore worthwhile in the medium term to anchor both Task Mining and Process Mining in the company.

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