Data Science of Process Mining – Understanding Complex Processes

Why Data Scientists Should Become Familiar with Process Mining

Data science teams around the world begin to start looking into Process Mining because:

1. Process Mining fills a gap which is not covered by existing data-mining, statistics and visualization tools. For example, data mining techniques can extract decision trees, predictions, or Frequent Patterns, but cannot display complete processes.
2. Data scientists with their skills to extract, link, and prepare data are ideally equipped to exploit the full potential of Process Mining. For example, the data of different IT systems such as the CRM data calls in the call center of a bank and the interactions with the customer advisor in the branch must be linked with each other in a 'Customer Journey' analysis.
3. Analytical results must be communicated with the business. Data Science Teams do not analyse data for themselves, but to solve problems and issues for the business. If these questions revolve around processes, then charts and statistics are only meaningful in a limited way and are often too abstract. Process Mining allows you to provide a visual representation to the process owner, and also to directly profit from their domain knowledge in interactive analysis workshops. This allows you to find and implement solutions quickly.

Next Steps

Are you curious and want to know more about Process Mining? We recommend the following links:

• 15 minute recording, Presentation on Process Mining at bpmNEXT (with introduction and live demo): youtu.be/ql1S1wAxJ0E?t=10s
• Introductory article on Process Mining: fluxicon.com/s/pmarticle

2 free online courses (so-called MOOCs) offer an introduction to the topic of Process Mining in English:

• The 'Process mining: Data science in Action' MOOC at Coursera is a course given by Prof. Wil van der Aalst himself and provides a comprehensive picture of the foundations and the background of Process Mining algorithms, starting on October 7: www.coursera.org/course/procmin
• The 'Fundamentals of BPM' MOOC of the Queensland University of Technology has generally a business process management focus but also includes a practical segment about Process Mining, starting on October 15th: moocs.qut.edu.au/learn/fundamentals-of-bpm-october-2015

To really get a good picture of what Process Mining can do (and what it can‘t do), it is best to try it out yourself. Here are two easily accessible ways to get started:

• The academic Process Mining platform 'ProM' is Open Source and contains hundreds of plug-ins the with the latest Process Mining algorithms: promtools.org
• For an easy introduction and for the professional Power User you can download the demo version of our Process Mining software 'Disco' from the following webpage: fluxicon.com/disco/

Authors: Anne Rozinat, PhD & Christian W. Günther, PhD



Anne Rozinat has more than 10 years of experience with the application of Process Mining. Christian W. Günther received his doctorate under Prof. Wil van of Aalst and his research ensured that nowadays even the most complex and heterogeneous processes can be analysed using Process Mining. Both are the founders of Fluxicon and the makers of the popular Process Mining software Disco. They organize the annual Process Mining Conference Process Mining Camp and blog regularly about Process Mining on fluxicon.com/blog/.

Related:

• Coursera: Process Mining: Data science in Action, April 2015
• MOOC: Process Mining: Data science in Action