Process Mining Camp 2014 — Fireside Chat with Nicholas Hartman
As a warm-up for Process Mining Camp, we have asked some of the speakers for an up-front interview. Previously, we have already spoken with Frank van Geffen from the Rabobank, with Johan Lammers from the Centraal Bureau voor de Statistiek, with Shaun Moran from Customer Dimension Analytics, and Antonio Valle from G2.
Today, you can read the interview with Nicholas Hartman.
Nick is director of CKM Advisors and will give a practice talk and a workshop about process mining in the context of other data science tools at camp.
Interview with Nick
Anne: Hi Nick, thank you for coming over from New York for Process Mining Camp! I can see from your twitter feed that CKM are putting lots of efforts into recruiting data scientists. What do you think is it that makes the job of a data scientist attractive for students?
Nick: Hi Anne. I’m really looking forward to the trip and meeting others within the process mining community.
It’s increasingly common for graduates to move into, and be very successful in, fields that were not directly related to their college or graduate studies. The students we encounter are keen to ensure that that their first steps into the ‘real world’ open even more doors for career options down the line.
A decade ago, management consultancy was often viewed as the main path for getting rapid exposure across sectors before settling down in a particular area. Today for many top candidates, and particularly for those from a science or math background, data science offers a better opportunity to both get that breadth of exposure to business challenges but also utilize and expand upon the technical skill-sets that interest these individuals. As a rapidly expanding field there are certainly a lot of opportunities to continue longer term advancing through data science. However, even those that end up moving horizontally after a few years will still possess the base of skills required to succeed in the data-driven economy of the future.
We also see a lot of top candidates that want to make sure they don’t want to end up as the ‘smartest person in the room.’ Rather, they want to feel like they’re a part of a team of people where everyone can contribute to the problem being solved while also constantly learning new skills from each other. That sort of communal collaborative atmosphere is really at the core of the data science community, and it’s certainly something that today’s graduates find attractive.
Anne: Yes, I can also recognize the mix of business and technical challenges as something that attracts people to process mining. As a field, it lies somewhere between information systems and computer science. So, interesting algorithms can be applied to very relevant problems in today’s companies. This is really exciting.
And you are absolutely right, community is very important. The process mining community is still quite small but an enthusiastic one. Do you think it has a place in the wider data science community now? Should it have a place there? How do you see the relationship?
Nick: Absolutely, process mining is a core component of data science. In fact, for most of the business applications of data science that we’re seeing some element of process mining is a major contributor.
One of the great things about the processing mining movement is that it’s focused directly on applying data to solve relevant issues that matter to stakeholders–the process owners. The broader data science, or dare I say “big data,” movement is often guilty of focusing too much on tools and too little on developing actionable output with those tools. A focus on process mining as part of an organization’s data science initiative helps ensure that the data science team and its technical assets are focused on delivering output that will have a measurable impact for stakeholders.
In return, the broader data science community can help process miners in conducting analytics on increasingly large and unwieldy datasets, and connecting process data to other information that can help tell a more complete story. Basic process mining can be performed on a single system audit-log file, but increasingly we’re seeing stakeholders asking for things like text analytics to be layered on top of the process mining. These sorts of challenges require close collaboration between a diverse set of data scientists that can bring together these complementary skill sets.
Anne: Right! Next to your practice talk, where you will present two case studies, you will also give a workshop about data science tools that are commonly used together with process mining. What can participants expect from this workshop at camp?
Nick: I’ll start by presenting an overview of the the main steps we typically follow–from data ingestion and storage through to presentation–when completing a project and will highlight popular tools that are used by data scientists to facilitate those processes. In each of these areas I’ll pull from examples of our project work to describe things to consider with different tools, languages and services. There are currently no end-to-end data science solutions available, which means that skilled data scientists will need to integrate an appropriate collection of tools to deliver a successful analytics implementation.
The later half of the workshop will focus on going deeper into a few use cases of such tools, including text mining and automated ingestion of data into an analytics environment for process monitoring.
I’ll conclude with some suggestions on places to get started both in terms of experimenting with tools and getting access to useful test data. It’s certainly a lot to cover, but I hope there will be something new for everyone in the session. I’m also looking forward to learning through the discussions we have amongst the group.
Anne: Thanks, Nick! We look forward to having you at camp next week!
Process Mining Camp takes place on 18 June in Eindhoven! Tickets are sold out right but you can still sign up and be notified if more ticket should become available…
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