40: Comparisons

When Linda took over the process improvement team, she realized that meeting with domain experts for two hours per week on a project did not lead anywhere. After she switched to full-day workshops, everyone noticed that it was significantly better. They have gained focus and obtained much better results. Linda shares step by step how she prepares and runs these workshops.

Healthcare processes are complex due to the individuality of each patient, so process mining is particularly useful for understanding them. Process maps Owen's research group discovered at a UK hospital illustrated how often patients were misdiagnosed and sent around. Owen talks about their studies as well as their methodological framework for process mining in healthcare.

Data protection teams, the works council, and process owners need to trust that you will act responsibly in your process mining project. How do you address their concerns? We talk with Daniel about the organizational side (who to involve when and how) as well as technical steps you can take, such as removing unnecessary information, anonymization, and pseudonymization.

How do you determine what you should compare in a process? Hanna discovered a process at her bank for three different regions. She analyzed the case durations but also rework, parallel vs. sequential flows, and long waiting times. After understanding the different process characteristics in each region, Hanna identified best practices for improving them.

Clickstreams are the digital traces that visitors leave when they navigate through a website. You can then analyze this data with process mining to make the website more effective or user-friendly. Irene shares her experience of a recent clickstream analysis project. We discuss why the funnels of traditional web analytics are not enough to understand behavior.

Vinicius, Rudi, and Anne talk about the common challenges and best practices for process mining. They discuss the importance and unimportance of data quality, data privacy, process mining methodologies, and domain expertise. What must you do to ensure your process mining initiative is successful? Watch the café to find out!

When you import your data set, you first have to look around and get an overview about your process. Where does it start and where does it end? Can you recognize the expected process? Is there a lot or little variation? Do you see rework or other unexpected patterns? Join us as we discover the loan application process from the BPI 2016 dataset.

Some preprocessing tasks can be easily done in Disco. Others require the use of data transformation tools. Xixi has classified the common preprocessing tasks for process mining into six categories: Enriching, Integration, Filtering, Transformation, Reduction, and Abstraction. For each category, we discuss concrete examples from practice.

Many companies use or plan to use automation techniques to reduce manual labor. But which parts of a process can be automated, and which parts should be better kept human? Erik and Lloyd share how they use Disco to analyze the performance of their RPA robots, identify new automation opportunities, and analyze their training processes.

Auditors have different goals than process improvement teams. For example, internal auditors verify the efficiency of the process and whether it is under control. They analyze the segregation of duties, variants, and cases. Mieke talks about the benefits of using process mining as an auditor and presents two recent research works by her and her team.