What Is Process Mining?
Process mining is a family of techniques in the field of process management that support the analysis of business processes based on event logs. During process mining, specialized data mining algorithms are applied to event log data in order to identify trends, patterns, and details contained in event logs recorded by an information system. Process mining aims to improve process efficiency and understanding of processes. Process mining is also known as Automated Business Process Discovery (ABPD). [1]

Process Mining Solves Fundamental Challenges Associated With Business Process Management [2]
Process mining, a relatively new and innovative technology, has the capability to solve challenges associated with process management and improvement – which are both, for the most part, back-burner issues presently among most companies. Process mining also creates the ability to revitalize process management in organizations where it has been inactive or unused for years.

One of the largest challenges associated with business process management involves the creation of “current state” processes, which is a description of how a business process is being performed currently. In business process reengineering, firms are largely interested in an improved “to be” process – and they often have little to no interest in exploring an “as is” process. Understanding the current process is imperative in deciding whether it’s worth investing in improvements, in addition to recognizing where performance issues exist and how much variation there is in the process across the company.

Another one of the other largest challenges with process management is the lack of connections between business processes and a firm’s enterprise information systems. Some enterprise systems, like SAP for example, are process-oriented in regards to supporting processes like procure-to-pay or order-to-cash. Although these enterprise systems can support these processes, there is rarely an easy way to understand how the process is being performed from the information system. To uncover information about how a process is performing day to day, typically a difficult set of manual steps are required to gather and synthesize data.

Process mining can address both of these current issues. Until recently, process mining has been an academic topic – enthusiastically pursued by researchers like Wil van der Alst, a Dutch computer scientist. This approach had little practical relevance until about eight years ago, when Celonis was founded. Celonis, a Munich-based company, has developed four major versions of its software that collects data from a vendor’s systems, but also from others like Salesforce, ServiceNow, and Oracle, in addition to any other type of system through APIs. Wil van der Alst is presently the Chief Scientific Advisor at Celonis.

Process mining software can help organizations easily capture data from enterprise transaction systems and it provides comprehensive information about how key processes are performing. It also creates event logs as work is done, such as when an order is received, when a product is delivered, and when a payment is made. These logs make it clear what is happening – including who did what, how long it took, and how it departs from the average. Process analytics create the ability to view key performance indicators for a process, which will inherently enable a company to focus on the priority steps for improvement. The AI algorithms can detect the main sources of the variation – for example, the algorithms might point out every time a new customer needs a credit check, the process is slowed down significantly.

Process mining is effectively being used to analyze the current state of business process performance, identify areas of improvement, and assess the results of process improvements. This makes it an effective partner for tools like robotic process automation (RPA), since it can first identify the best places to implement “bots” and subsequently provide the means to calculate the beneficial impact of the RPA implementation. When choosing where to apply process mining, organizations must ask themselves how they can get the most value from applying it to processes that have been digitized (i.e. supported by an IT system), and take note of where any unstructured work occurs outside of the IT system (i.e. reviews and approvals).

In closing, process mining may not be for everyone. Large, complex organizations with a commitment to quality and a profound interest in internal benchmarking can benefit the most from the transparency mining creates. On the flip side, if an organization isn’t oriented to process management, it probably won’t benefit from process mining.