How to Use Data Mining for Process Mining?

Analytics Data

The missing link between data and process science

We live in a hyper-linked world, a civilization where access to data is easier than ever before in history. Terms like “Data Science” and “Big Data” have entered the common linguistic so rapidly that few folks appreciate the profound vicissitudes they imply. Changes that interrupt the everyday lives of all of us; they obviously drive corporations to grow.

“Folks that do not change fast enough are inexorably destined to vanish.”

We are regularly bombarded with terms like business intelligence, big data, process mining, and data mining, but what exactly do they mean? The answer is simple: they all are into the processing of huge data provided by several information systems. But their roles differ accordingly.

Though the term data mining is known to individuals, process mining still appears to be a new subject for many. Several questions are going around the individual’s mind, “what is process mining? How does it work?”

Nowadays, data is really getting heavy! This is because of the continuous expansion of companies that generate an influx of data coming from mixed and disseminated sources. Thus, the extraction of data is a tricky and toilsome endeavor. And this is the moment where process mining comes to the rescue.

About process mining: everything one needs to know

First things first, process mining is a technique used by enterprises to monitor and analyze processes. It is a platform that combines technologies from business intelligence (BI), data analytics, process analytics, and data mining, thus, following holistic and deep insights into processes. Further, insights can be used by data analysts to optimize processes.

Process mining helps enterprises to collect insightful information to assess the productivity, reliability, and efficiency of business processes across industries.

For instance, earlier, process mining was exclusively used in the industrial sector to reduce labor and errors. Nowadays, industries are preferring enhanced technologies such as artificial intelligence (AI) and others. Thus, process mining has become a main concern for enterprises across every market. It is a vital tool for enterprises that are continuously enhancing IT and business processes.

In the simpler form, we can say that process mining is a primary platform that allows enterprises to gather information from current systems to impartially envision how business processes work and how they can be enhanced.

The rapid expansion of process mining over the course of two decades has resulted in immense opportunities for enterprises to modify their business processes. It is based on certain types of data that can map processes in your enterprise by using specific algorithms such as machine learning (ML).

Simple! It’s like a movie story in which an actor cannot live without the actress. In this case, the story is the same, “where data lives, process mining lives.”

Being a novel technology for business process management (BPM), process mining complements Business Intelligence (BI) tools. This BPM technique helps enterprises to collect present data and transform it into a visually structured, thorough process graph, showcasing the real process contrasted to the assumed one.

Professor Wil van der Aalst was the one who got this brilliant idea of combining data analytics and visualization. Wil thought, “what if instead of doing this all by hand, we automatically do it using the existing data?” Along with his colleagues, he focused on process analysis using event data in research – and what, it worked!

Data from the call centers, which covers voice-related data, can be mined with process mining software.

Data mining and process mining

At the core, both methods have a lot in common, as they use mathematical techniques and algorithms. The only difference is that process mining focuses on the analysis of procedures while using event data, whereas data mining operates with data in general.

Being part of business intelligence, both techniques analyze huge volumes of data to achieve greater insights. Thus, allowing users to make better decisions. Algorithms and AI are playing an important role in both fields in order to uncover hidden patterns and relationships.

Process mining is a relatively new discipline that has emerged from the need to connect the worlds of business process management and data mining. Data mining concentrates on the analysis of large data sets, while business process management is focused on improving, modeling, and controlling business processes.

Data mining is a computer technique exclusively used to detect incomprehensible patterns and complex relations in (big) data. In short, it is all about discovering previously unknown/unexpected relationships amongst the data. The insights derived through data mining might be used for scientific discovery, marketing, and fraud detection.

For instance, which product is performing best in the supermarket or which advertising campaign leads to the highest conversion rate? Another key difference is data mining consists of tables of data. In contrast, process mining consists of events from the IT systems, audit trails, event logs, and data that are provided with a timestamp.

Data science is the future because enterprises that are unaware or not able to use big data smartly won’t survive. It is not sufficient to concentrate on data analysis or data storage; one needs to relate data to process analysis.

How does process mining work?

Process mining starts by assessing an established business or IT processes to discover monotonous tasks that can be automated using enhanced technologies such as machine learning (ML), robotic process automation (RPA), and artificial intelligence (AI). Automation helps to minimize the errors in process outcomes.

Process mining techniques

There are three main process mining techniques exclusively used by enterprises to recognize gaps in organizational leadership, allow automated decision making, focus on improvements of implemented processes, and stimulate processes to predict future outcomes.

The three major classes of process mining techniques are as follows:

  • Conformance checking – is exclusively used in organizational and procedural models, rules and regulations, declaration processes, and company rules and policy.
  • Performance mining – this form of process mining offers insights into frequencies, service levels, and lead times.
  • Discovery – this form of process mining uses an event log as the basis for a model.

Process mining tools

These software tools help enterprises to collect relevant information and deliver insightful analytics based on the data they pull. The major software and tools include:

  • Icaro Tech EverFlow
  • UiPath RPA
  • Kofax Insight
  • Celonis
  • ARIS Process Mining
  • ProDiscover
  • myInvenio

Advantages of process mining

  • Makes intelligent use of ‘Big Data.’
  • Enhances operational processes.
  • It will help you to better understand aberrations, bottlenecks, risk of failure, and inefficiencies during crucial processes.
  • It fills the gap between Workflow Management (WfM) systems and traditional Business Process Management (BPM).

Let’s wrap up….

Process mining is increasingly crossing the chasm to businesses, being used in a much more practical and continuous manner, making it virtually effortless for business users to continuously stay on top of their process. Thus, process insights are made available to business professionals at an ever-increasing rate.

With modest beginnings in the academe, process mining has grown into a strong cornerstone of business transformation programs.

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