Shared Service Organizations (SSOs) are focused on cost-savings, efficiency, value creation, and digital transformation across their business. As a result of these goals, end-to-end (E2E) process automation has become a top objective for the majority of SSOs. In fact, according to SSON Research & Analytics, 81% of shared service practitioners voted E2E process integration as a top priority for their organization in 2022, citing intelligent automation as the way to get there.
As many shared service practitioners are aware, the most difficult stage of the automation journey can typically be the beginning. It is all too common for SSOs to dive into their investment of IA solutions, such as robotic process automation (RPA), without having a proper understanding of their process's readiness.
Process exploration tools like process discovery, process mining, and task mining are how SSOs can assess the processes they wish to automate. However, the Intelligent Automation (IA) 2022 Benchmarking Survey conducted by SSON R&A earlier this year tells us less than half of SSOs have invested in one of these tools; and only 16% of SSOs have processes ready for automation before investing in a tool.
With so many organizations having a clear need for these tools, SSON has set out to explain the differences between process exploration tools, and how businesses can determine the best fit for them.
Process Mining
Process mining is a technique in which businesses discover, map, and improve their processes through insights provided by real-time data found in company logs and event forms. This technique compiles data under one umbrella, thus making it easier for SSOs to locate and utilize. Process mining gives businesses the unique opportunity to understand how their processes are performing, see where bottlenecks lie, and a resource that automatically points out where to begin automation projects.
While process mining can be very helpful for businesses looking to organize and understand the data around potential automation opportunities, one commonly referred to shortcoming is its failure to gather all relevant information on a process. While company logs and event forms are useful, process mining fails to capture feedback from the users who are interacting with the process on a daily basis, meaning the tool fails to understand the process on a human level.
Process Discovery
Process discovery is an AI-powered tool that, when implemented correctly, can cover the blind spots left behind by process mining. The use of computer vision and machine learning enables process discovery to observe users and uncover process variations from digital traces of human work.
Process discovery is comprised of three steps. The first is observing the tasks in a process through a computer implement that collects data. This data is then sent through a machine learning program which determines automation opportunities. This information is then used to complete the final step, a detailed process assessment that shows SSOs where to best begin their automation journey.
According to the IA benchmarking survey, only 19% of SSOs have chosen to invest in a process discovery tool, likely because they lack either the technical capabilities or process knowledge, to utilize it. For process discovery to work correctly businesses need to have access to comprehensive real-time data, and many are struggling with low-quality data capture and bots that are not capturing the complexity of processes.
If you are interested in learning more about the benefits of process discovery and other process exploration tools, be sure to download “IA Global Market Outlook Report: End-to-End Transformation for Business Processes”.
Task Mining
Task mining is frequently paired with process mining because it’s focused on how users interact with processes, rather than the data provided by forms and logs. This tool collects granular user data from the front end, meaning it looks at clicks, keystrokes, and date entries related to the process users oversee.
The IA benchmarking survey tells us that 31% of companies have invested in task mining tools, similar to the 35% of companies that have used process mining tools. Businesses that realized they were only getting a partial scope of their processes through process mining are investing in task mining to build greater insights into the processes.
Recently, task mining has become a useful tool for businesses looking to understand how employees are interacting with their processes during a hybrid work setting. The insights task mining provides tell businesses what tasks employees are struggling with while working from home, and what tasks are performed just as efficiently as in the office.
If you are interested in learning more about task mining's possible applications in a hybrid work environment, be sure to download the “How Task Mining Can Drive Process Excellence” report.