11 Benefits of Process Mining in 2019 according to practitioners

Process mining enables businesses to understand their processes which is critical to business success. Savvy business leaders focus on processes. Results depend on external factors so they have significant variation. However, over the long run, better processes yield better results.

Despite the importance of processes, companies have limited data about how their processes are run. Most process data is stored in detailed log files which are difficult to analyze without process mining tools.

General Business Benefits

Data-driven decision making

Prerequisites for data driven decision making are data availability, high quality of data and strong data visualization/analytics capabilities. Process mining tools increase data availability to enable data driven decision making. Read more


Data-driven decision making in 2019: Step-by-step guide

Most successful organizations treat data-driven decision as a primary objective and pursue it with religious zeal. However, data-driven decision making, the steps leading to it and how AI is changing it are not well-defined.

In an AI-first company, data-driven decision making means

  • Strategic decisions are made by a diverse group including executives that rely on a sufficiently comprehensive set of information. By definition, a bad strategic decision should be important enough to lead to failure of the company.
  • Most operational decisions are handled by continuously learning machine learning models which produce explainable decisions. Operational decisions are frequent (once a week or more frequent) and not critical (a single mistake is unlikely to lead to failure of the company).
  • Operational decisions which can not be automated with good accuracy are delegated to humans.
    • If data is lacking, opinion based decisions are made.
    • If data exists and has been analyzed, decision maker relies on analysis.
    • If data exists but is not analyzed yet, cost of analysis determines whether an opinion or data based decision will be made.

    Before settling on this framework for decision making for modern corporations, we need to identify how we can evaluate different decision making models. However, if you like you can directly skip to the sections that interest you: Read more


33 Process Mining Case Studies Including Project Results

We can learn better about new technology by observing its real-life applications. That’s why we have compiled process mining case studies from different resources. As we review them, we understand how the insights from process mining can improve different businesses. This article aims to reveal typical results and usage areas of process mining, including a summary of these case studies.

What are the typical results of process mining projects?

After mining their processes, many companies gained vital insights into their businesses. These insights helped them to execute better process analysis and prepare action plans. In conclusion, companies have achieved various results for process improvement. Top 3 typical results are as the following: Read more


Affective Computing: In-Depth Guide to Emotion AI [2019]

A digital heart

Affective computing systems automatically identify emotions. Affective computing is also called emotion detection, emotion AI, artificial emotional intelligence or affective AI. Understand affective computing in detail:

What is affective computing?

Affective computing systems auto-recognize emotions. See a longer definition below:

Affective computing  is the development of systems that can recognize, interpret, process, and simulate human feelings and emotions.


It may seem strange that machines can do something that is so inherently human. However, there is growing research supporting the point that human emotions are recognizable using facial and verbal clues. Read more


Machine Learning Accuracy: Learn the Metric to Assess ML Models [’19]

Demonstrates the 4 possible categories of results of a model: True positive, false positive, true negative, false negative

There are various theoretical approaches to measuring accuracy* of competing machine learning models however, in most commercial applications, you simply need to assign a business value to 4 types of results: true positives, true negatives, false positives and false negatives. By multiplying number of results in each bucket with the associated business values, you will ensure that you use the best model available.

Further complicating this situation is the confidence vales provided by the model. Almost all machine learning models can be built to provide a level of confidence for their answer. A high level approach to using this value in accuracy* measurement is to multiply it with the results, essentially rewarding the model for providing high confidence values for its correct assessments. Read more