AI VS PROCESS MINING: THE DIFFERENCES AND APPLICATIONS

The main difference between AI and Process Mining: it’s about data structure and models

Process Mining is gaining more and more spotlight in the new era of data science - Augmented Analytics. At the same time, companies in transformation are accelerating the adoption of Artificial Intelligence (AI) in search of the best experience for their customers and, of course, efficiency gains.

A frequent question is: what is the difference between AI and Process Mining. AI seeks to emulate (human) intelligence in machines. It searches for ways to empower computers with cognitive capabilities (e.g. think, learn), so they can perform more complex activities. One of the key technologies empowering AI today is machine learning, and, more specifically, deep learning. 

In Machine Learning, instead of coding an algorithm to perform a task you need, you try to teach an algorithm to perform an activity by providing input data (sometimes, lots of data). This training requires algorithms that allow the machine to "learn" from historical data and then model and act on the new data it will receive for processing. Algorithms for classification, clustering, and regression became the very building blocks of modern AI. In almost every AI project, there is an important data preparation journey until the most appropriate model is found.

AI and Machine Learning, therefore, share a commonality with Process Mining. The existence of a model derived from the data. On the other hand, this is exactly where a key difference lies. Because of its specialization, there is structure in the data ingested by a process mining tool, as well as there is some predefined guidance to the modelling of that same data. In most AI/Machine Learning projects, there is a quest to shave and mold the data as well as to find the right model for it. 

Main applications: AI and Process Mining

Where has AI been more successful recently? In scenarios where there is a more fixed, stable structure to the input data. Images, for examples, are always matrices of Red, Green or Blue (RGB), or something equivalent to this. AI now excels in recognizing images.

In Process Mining, the input data takes the form of events, that usually contain at least 3 pieces of data: a timestamp, a case id and an activity name. This minimum structure provides for a series of abstractions that propel this area: cases, traces, process models. These abstractions support discovery algorithms, conformance analysis algorithms, simulations, and much more. 

Process Mining is a sort of Machine Learning technique where structure is more present and more can be learned with much less effort. Actually, one application of Process Mining is to explore datasets that may subsequently be automated with AI-powered technologies, because the exploration is so much easier using Process Mining paradigms.

To keep it short: if your dataset comprises events, and you need to analyze a process, or something that looks like a process, you will find much more support and a shorter road to results by using a Process Mining solution such as EverFlow.

 

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