By now, several companies recognize that they have opportunities to use process mining to enhance process efficiency, improve decision making, and gain competitive advantage.  But implementing a process mining initiative is complex. You need to set a right goal, ensure that the infrastructure requirements are met, such as tools, systems and data sets, and tackle the intrinsic challenges, such as securing commitment, reinventing processes and changing organizational behavior.

One of the fundamental issues in the mining initiatives is that business processes do not produce data of appropriate quality to mine. This can be caused by missing data as well as poor data quality in event logs, problems with timestamps, event granularity, ambiguity of activity names, and some parts of a process that need to be modeled may not produce data at all. Every company today produces a massive amount of data, every day and in every business segment. Voluminous data needs to be handled in business areas such as sales, procurement, finance, etc. Furthermore, from our experiences, we have identified three core challenges that companies face when launching process mining initiatives.

1. Inaccurate mapping of business processes
2. Handling data sources and how to connect them efficiently
3. Using insights to drive operations forward

Before organizations can tackle these challenges, they need to clarify fundamental questions: How to differentiate the necessary data from irrelevant data and how to provide the technical infrastructure for mining? For some companies, the starting point is to realize the accelerators such as data extraction and robotic process automation to speed up the preparatory phase of the process mining initiative.

• Data extraction: As data might be stored in various formats, programs etc. and need to be merged, data extraction can accelerate this process. A well-synchronized system landscape is the base of process mining. Even though implementing a functional process mining strategy can be challenging, it is also very rewarding. With the upcoming trends in this area, the technology will become even more relevant.
• Robotic Process Automation (RPA): RPA faces rising relevance in the future. Based on a survey by Deloitte [1], 58% of the 523 asked companies worldwide started their automation journey. Most of these RPAprojects focus on monitoring hybrid processes. Process bots currently do not take over the entire process on their own. Most of the processes are still too business-critical, so the human work cannot be fully taken over by a bot. The best practice for such critical processes is a hybrid process, where the bot takes over preparation tasks, for example, and the human workforce monitors the progress of the bot.

The smartest of technology advancements can only deliver value if combined with transformational initiatives:

• Don’t forget the people: While trying to achieve process excellence, it is important not to lose the focus on the people driving those processes. By using RPA, the tasks within the processes can be automated, but the data needs context around it. A complete view enables a visualization on how work is done inside the organization.
• Think outside the box: The Gartner Market Guide for Process Mining forecasts growth of at least three times within the next 2 years. Order-to-cash (O2C) or procure-to-pay (P2P) processes have been the most popular processes when companies think about process mining, but there are endless opportunities to use this technology. Departments such as sales, marketing and logistics also hold potential for processes optimization. Therefore, it becomes more important to think outside the box and enable more existing processes that can leverage transformational technologies.
• Process compliance and simulation: According to McKinsey, one of the key practices of continuous improvement in action is performance transparency [2]. This can be achieved by simulating your processes and checking them for compliance. Process mining is seen not only as a pure process recognition tool, but also as a conformance testing tool. It helps organizations compare the current state of the process with the model situation and, more importantly, respond accordingly. A simulation of your process helps visualize the changes to the process before it goes into production.