The final speaker at Process Mining Camp 2018 was Wil van der Aalst, the founding father of process mining. In his closing keynote, Wil talked about the updated skill set that process and data scientists need today.
The final speaker at Process Mining Camp 2018 was Wil van der Aalst, the founding father of process mining. In his closing keynote, Wil talked about the updated skill set that process and data scientists need today. Since process mining research was starting up in Eindhoven in the late 90s, the availability of suitable data has increased tremendously, which makes it even more important that this data can and will be used in an appropriate and responsible manner.
This requires dedicated capabilities from the process miner in each stage of the analysis pipeline: Processing and analyzing data, being responsible about the effects on people, and on business models. When you look for people who are skilled in all of these technical areas, as well as in soft skills like communication and ethics, you start looking for (as they would say in the Netherlands) a “sheep with 5 legs”, or something that is very rare. Becoming a data scientist requires a lot of effort to learn all the skills that are needed to live up to these high expectations.
As ambassadors of process mining, we also have the responsibility to use the right terms. Wil sees a clear a distinction between Artificial Intelligence (AI), machine learning, and data mining. At the same time, one could argue that process mining is data mining, but the underlying techniques are very different. So, saying that process mining is part of data mining, or AI, doesn’t make any sense.
There are incredible expectations around AI and Big Data, which is very dangerous as we have seen in past “AI winters”. We should be careful not to overpromise and try to be realistic. The incredible successes of machine learning techniques like deep learning are, for example, still limited to very specific fields.
Some in the media and big technology companies use terms like Artificial Intelligence, Machine Learning, and Deep Learning interchangeably. They might argue that you don’t need process mining as you can just put an event log into a deep neural network and a process model will come out. There is, however, not one deep learning algorithm that can discover a process model. Instead, when we look at process mining it combines the fields and methods of ‘process science’ and ‘data science’. This makes it even more challenging for us to cover all required skills.
But do you, as a professional, need to know how a car works internally in order to drive it? It depends on what you want to accomplish. For example, if you need to drive fast around the Nurburg Ring, it can be very useful. Also, if you need to select a car then it would certainly be useful to know about its internals. Process mining is a relatively young technology. Therefore, it is useful to know how it works in order to select the right tool, and to use it to maximum effect.
So what kind of skills do you need as a process miner? You need to be able to extract and clean the data, spend time on the analysis, and interpret the results. This is not easy. All the involved parties need to invest the time to determine what the process maps actually mean, so that they can really trust their interpretation. The sheep with five legs would be the ideal process miner, but in most cases this is not realistic.
Traditionally, you will often rely on collaboration between a data-driven expert and a business/domain-driven expert. However, you can also think about more hybrid process mining profiles. Some experts can integrate technological skills into their domain knowledge, while other data scientists can be process mining experts which are especially skilled to perform specific types of analysis in a particular domain.
Fran Batchelor is a Nursing Informatics Specialist who supports the surgical services at three of UW Health’s hospitals. She used process mining to analyze the flow of urgent and emergent surgical cases added to the schedule.
Niyi started with process mining on a cold winter morning in January 2017, when he received an email from a colleague telling him about process mining. In his talk, he shared his process mining journey and the five lessons they have learned so far.
Dinesh Das is the Data Science manager in Microsoft’s Core Services Engineering and Operations organization. He sees process mining as a silver bullet to achieve this and he shared his learnings and experiences based on the proof of concept on the global trade process.
Wim is a program manager responsible for improving and controlling the financial function at the City of Amsterdam. He shared the five-step approach they used for introducing process mining.
As a data analyst at the internal audit department of Euroclear, Olga helped Daniel, an IT Manager, to make his life at the end of the year a bit easier by using process mining to identify key risks.
As a Sunday afternoon project, Marc collected data from Jira, a project management software for (agile) software development, to see how process mining could be applied to help the teams learn from each other.
After tackling the inevitable complexity of any healthcare process through a combination of simplification strategies, the team at HULA were able to reveal bottlenecks that, once removed, can lead to a faster cancer diagnosis.
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