Process mining: building the future by looking at the past
Predicting the future requires an understanding of the forces that shaped the present. So how do you make strategic decisions about where your organization should be going in an uncertain future?
We met up with Michal Filip Kowalik from process mining firm Minit, to find out what the attraction of Process Mining is, and how it can be used in strategic decision making.
PEX: Why have Minit decided to go into the Process Mining space?
Michal: Our team has been observing struggles of the large organizations to monitor their enterprise processes for a very long time. Progress in computing power and capabilities of enterprise applications allowed us to build a process mining solution which through a usage of algorithms allows companies to improve revenue, decrease costs and assure compliance through data-driven process monitoring. We are already seeing significant improvements in Robotic Process Automation, Digital Twin Organization and overall process efficiency through usage of Minit Process Mining.
PEX: What are the benefits of Process Mining to clients?
Michal: Customers have an opportunity to improve their enterprise processes through an immediate view into their systems. Historically this would mean long weeks of investigations through expensive consultancy services. Minit process mining solution connects directly into enterprise applications and within seconds paints ‘process as it is’ and allows users to immediately apply improvements as well as monitor on a regular basis. All of that with nearly 100% data accuracy, fraction of the time spent and lower price.
PEX: How can Process Mining help with predictive analytics?
Michal: Predictive analytics is a key part of process mining technology – Minit is introducing ‘what if’ scenarios within existing solution which enables simulation of key performance indicators (duration, costs, automation) based on improved process flows. We are also adding first A.I. based features which will allow automatic improvements of the process based on historical data and patterns observed.