FAQ Process Mining

What is Process Mining?

Process Mining is a combination of data mining and algorithmic reverse process modelling. Process patterns are traced on the basis of the collected log data that transactions cause in ERP or other workflow business systems. These transactions are reverse engineered by process mining algorithms. They depict the real processes which took place at the time of the respective data logs. It is normal that most company processes have many variants that are not registered in the firm's business process models (BPM). Process mining illustrates process data in the business workflow context and thus allows to find root causes that only become visibel when looking at the sequence of events.

What is a Digital Process Twin?

This process minng analyses results in a digital process twin that mimicks the as-is process. The digital twin serves then as a model to deliver insights, as a basis to identifiy deviations againt target processes and as a playground to run scenarios. Often, data attached to the digital twin deliver new insights as they are illustrated in the timely sequence of the occurence in a business process.

How does Process Mining generate business value?

The world's most valuable resource is no longer oil, but data; they say. But how do firms access this resource to generate business value measured by P&L impact. Process mining focus on data that provides insight on how business processes are really taking place in business operations. For many years generating value from process insights has been discarded such as activity-based costing. With process mining technology, value that sits unrecognized in inefficient or not complaint processes can now be accessed and changed.

How does Process Mining fit into digital transformation?

Process mining belongs to big data analytics and data mining. It resembles workflow analytics and helps firms to understand better what really takes place in their operations. It also defines a starting point for digital upgrading such as closing seamless data chains and automation due to manually steps in between. For many firms process mining might well serve as a starting point for workflow automation and tackling legacy processes with un-interrupted digital solutions.

How is Process Mining connected to business targets and KPIs?

Linking company KPIs to process mining objectives is key for successful improvement initiatives. It is important to acknowledge that there is a direct relationship between existing data in the current system landscape and business targets and requirements. For instance, the quote for maverick buying may be defined as the number of invoices without purchase order to the total amount of invoices. Process mining can extract and illustrate the data such that this KPIs can be continuously monitored.

Is a Process Mining project an IT or a business project?

Process mining serves business objectives. It uses the data in the current IT systems and data extraction is necessary. However, the data extraction is only a small part of the project scope and often done by standardized tools. Most important is tacit knowledge about the processes and the targets of the initiative. In some cases the business department can extract the data themselves and no IT department involvement is necessary.

Is Process Mining a part of Business Intelligence?

As we gain a better understanding of the performance of processes by using Process Mining, it is also possible to monitor and control the actual outcome. In that case, you can see Process Mining is a part of Business Intelligence. However, the Business Intelligence perspective is often data-centric and lacks the sequence of event information as well as the associated time. The Process Mining perspective is process-centric and as such augments Business Intelligence. The same argument holds true for data mining.

Why should you use Process Mining?

Process Mining is used for multiple purposes. Most often it is used in the field of performance, digitalization, compliance and automation. Core is the insight that comes from the process-centric data analysis bridging often end-to-end processes difficult to recognize for siloed organizations. Also, process mining reflects the organizational behavior by reverse engineering the real process sequence and thus replicates operational reality.