Discover, model, monitor, optimize and automate the underlying processes. 


Process mining represents a set of techniques that are used for analyzing and tracking processes that helps businesses make faster, more informed decisions for process improvement through data-driven insights.

It is based on combining data mining and process analytics in order to mine log data from their information systems to understand the performance of their processes, revealing bottlenecks and other areas of improvement. The logs make visible how computer-mediated work is really happening, including who did it, how long it takes, and how it departs from the average. Process analytics create key performance indicators for the process, which enables a company to analyse how business actually functions and to focus on the priority steps to improve.

The main goal of process mining is to turn event data into insights and actions. 

So, when people (or even robots) work with different IT systems, system captures their activities and creates complete view on how the things are getting done. It vizualizes the end-to-end process and reveals eventual improvements.

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Quantify the impact of in your process gaps so organisations can demonstrate value before and after implementing an improvement  

Helps organisations to measure the impact of process gaps on bussiness outcomes and demonstrate value before and after implementing an improvement 

Reducing costs and increasing ROI by quantifying inefficiencies in the operational models  

Identifies the most valuable, impactful places to insert automation

Zero in on bottlenecks, deviations and inefficient processes that should be rethought or automated 

Can be used in any industry: telecommunications, healthcare, logistics, bank industry etc. 


There are three categories of process mining techniques. 

  1. PROCESS DISCOVERY: The first step in process mining. It is crucial for understanding how any process in companies is actually executed. The main goal of process discovery is to transform the event log into a process model. This model is the most widely adopted. 
  2. CONFORMANCE CHECKING: Compares an event log that have been recorded during the execution of the process with an existing process model to analyse the differencies between them. May be used to detect deviations (compliance checking), evaluate the discovery algorithms, or enrich an existing process model.  
  3. ENHANCEMENT: The idea of this model is to extend it with other relevant informations (cycle and waiting times, costs, roles and other) and to improve the performance of this model with respect to certain process performance measures. 

Process Intelligence is a concept that goes beyond traditional concept of process mining. It works with all processes and monitor every process instance in real time, revealing bottlenecks and distruptions. 

Process Intelligence can be used as a key to enabling RPA efforts. It can reveal all processes that are good candidates for the automation, helps to improve all processes before automating them and helps build an automation program based on facts and data that system has captured. 


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