Why process mining is the top skill to learn for business analysts in 2022

The increasing importance of business process analysis

We are living in a world where business and business interactions are becoming increasingly digital – starting with digital customer interactions and ending with digital business models. That means business processes, too, must become digital and more dynamic and data-driven. With this rate of change increasing, so increases the need for business process analysis and understanding of how business processes relate to larger processes, customer journeys, and process dynamics.  

Processes can’t be just viewed as a sequence of steps in the business process model and notation diagram (BPMN) schema. We need to understand more about the process, lead times, exceptional process execution paths, and much more. So, the process should be analyzed not just with qualitative methods but also with quantitative methods. This type of quantitative business process analysis method is Process Mining. Introduced by Dutch computer scientist Wil van der Aalst in 2012, it has become a very popular approach for business process improvement analysis.

Process mining provides a realistic view of the process, showing process variants (the number of alternative execution paths) and many more process metrics. It is like an x-ray tool for a business analyst to see inside the business process. On the market, there are many process mining tools that provide these insights, like PafNow

Demand for a data-driven approach 

Another aspect of business process digitization is bringing in the use of data, artificial intelligence and machine learning (AI/ML), and automation. Obviously, it is not the goal per se to make all processes data-driven, but it is very important to understand the impact of a data-driven approach on the business process in any context.  

The same applies to identifying and analyzing process candidates for automation using RPA and AI/ML. We already see a demand for knowledge and skills in the business analysis field that includes data-driven techniques. And again, for that, we need quantitative information to understand how well existing processes are working.  

Process mining could help us not just understand the current state of the business process in detail, but also find out causalities and correlations and even do some modeling of the process.  

Finally, we need to understand that business process improvement and changes are never-ending processes, and we need tools and techniques to monitor processes after changes are implemented. This is another use case for process mining because you can monitor business processes in real time and measure the impact of changes you made. 

Here at Emergn, we use process mining for the identification and validation of automation use cases. As a result of analysis with process mining and ML, we could predict what process is best for automation, digital transformation, or rationalization. For example, process mining helps to find high-volume repetitive manual tasks. In the picture below you can see dashboards, we are building using process mining and ML from business process event data. These dashboards become another tool business analysts can use to understand how business processes are working in action. Data analysis is another very demanding skill for BAs. 


If you are a business analyst looking for something new to learn, look no further than process mining, the best skill to learn in 2022. There are many sources for learning, but you can start with the classic Wil van der Aalst course. This course will provide a common understanding of process mining, and sometimes you can do process mining without specific tools. There are also many pieces of training provided by top process mining tools like Celonis, PafNow, and others. 

Process mining knowledge will help you not just be better at business process analysis but will also help you break into related areas such as automation, data analytics, customer experience, and much more.