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Clarify Goal of the Analysis – Process Mining Rule 1 of 4

This is article no. 1 of the four-part article series Privacy, Security and Ethics in Process Mining.

Read this article in German:
Datenschutz, Sicherheit und Ethik beim Process Mining – Regel 1 von 4

Clarify Goal of the Analysis

The good news is that in most situations Process Mining does not need to evaluate personal information, because it usually focuses on the internal organizational processes rather than, for example, on customer profiles. Furthermore, you are investigating the overall process patterns. For example, a process miner is typically looking for ways to organize the process in a smarter way to avoid unnecessary idle times rather than trying to make people work faster.

However, as soon as you would like to better understand the performance of a particular process, you often need to know more about other case attributes that could explain variations in process behaviours or performance. And people might become worried about where this will lead them.

Therefore, already at the very beginning of the process mining project, you should think about the goal of the analysis. Be clear about how the results will be used. Think about what problem are you trying to solve and what data you need to solve this problem.

Do:

  • Check whether there are legal restrictions regarding the data. For example, in Germany employee-related data cannot be used and typically simply would not be extracted in the first place. If your project relates to analyzing customer data, make sure you understand the restrictions and consider anonymization options (see guideline No. 3).
  • Consider establishing an ethical charter that states the goal of the project, including what will and what will not be done based on the analysis. For example, you can clearly state that the goal is not to evaluate the performance of the employees. Communicate to the people who are responsible for extracting the data what these goals are and ask for their assistance to prepare the data accordingly.

Don’t:

  • Start out with a fuzzy idea and simply extract all the data you can get. Instead, think about what problem are you trying to solve? And what data do you actually need to solve this problem? Your project should focus on business goals that can get the support of the process managers you work with (see guideline No. 4).
  • Make your first project too big. Instead, focus on one process with a clear goal. If you make the scope of your project too big, people might block it or work against you while they do not yet even understand what process mining can do.

Privacy, Security and Ethics in Process Mining – Article Series

When I moved to the Netherlands 12 years ago and started grocery shopping at one of the local supermarket chains, Albert Heijn, I initially resisted getting their Bonus card (a loyalty card for discounts), because I did not want the company to track my purchases. I felt that using this information would help them to manipulate me by arranging or advertising products in a way that would make me buy more than I wanted to. It simply felt wrong.

Read this article in German:
Datenschutz, Sicherheit und Ethik beim Process Mining – Artikelserie

The truth is that no data analysis technique is intrinsically good or bad. It is always in the hands of the people using the technology to make it productive and constructive. For example, while supermarkets could use the information tracked through the loyalty cards of their customers to make sure that we have to take the longest route through the store to get our typical items (passing by as many other products as possible), they can also use this information to make the shopping experience more pleasant, and to offer more products that we like.

Most companies have started to use data analysis techniques to analyze their data in one way or the other. These data analyses can bring enormous opportunities for the companies and for their customers, but with the increased use of data science the question of ethics and responsible use also grows more dominant. Initiatives like the Responsible Data Science seminar series [1] take on this topic by raising awareness and encouraging researchers to develop algorithms that have concepts like fairness, accuracy, confidentiality, and transparency built in (see Wil van der Aalst’s presentation on Responsible Data Science at Process Mining Camp 2016).

Process Mining can provide you with amazing insights about your processes, and fuel your improvement initiatives with inspiration and enthusiasm, if you approach it in the right way. But how can you ensure that you use process mining responsibly? What should you pay attention to when you introduce process mining in your own organization?

In this article series, we provide you four guidelines that you can follow to prepare your process mining analysis in a responsible way:

Part 1 of 4: Clarify the Goal of the Analysis

Part 2 of 4: Responsible Handling of Data

Part 3 of 4: Consider Anonymization

Part 4 of 4: Establish a collaborative Culture

Acknowledgements

We would like to thank Frank van Geffen and Léonard Studer, who initiated the first discussions in the workgroup around responsible process mining in 2015. Furthermore, we would like to thank Moe Wynn, Felix Mannhardt and Wil van der Aalst for their feedback on earlier versions of this article.

Success Criteria Process Mining

Process Mining is much more than the automatic drawing of process models.

Process mining is on the rise. By using Process mining, organizations can see how their processes really operate [1]. The results are amazing new insights about these processes that cannot be obtained in any other way. However, there are a few things that can go wrong. In this article, Frank van Geffen and Anne Rozinat give you tips about the pitfalls and advice that will help you to make your first process mining project as successful as it can be. Read more