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Bosch, ZF & Fraunhofer on Building Data Science Teams

March 7, 2022
Data literacy
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Below is a write-up based on our recent Webinar: How to Build a Data Science Team for Resilient Manufacturing. Watch the full webinar.

One of the most important software inventions of the 20th century was the graphical user interface (GUI). But what has that got to do with resilient manufacturing?

Over the last few decades, the manufacturing industry has seen a massive increase in the amounts of data being collected and the level of analysis and assessment being carried out on that data. As Frank Heitkaemper from Bosch said, “Manufacturers have been doing “big data” before the term was even invented”.

What has changed is how efficiently manufacturers are now able to access and actually do something with that data. “In the not-so-distant past, we’d be carrying USB sticks between machines,” said Andreas Riess, ZF Group: Even in “the last decade, most data workers spent time dragging-and-dropping and copy-and-pasting.”

GUI-driven visual programming tools have been instrumental in enabling manufacturers transition from the manual, code-heavy, resource-intensive environments of the past to today’s automated, scalable insights-driven organizations.

In the recent webinar, we interviewed three manufacturing experts, Frank Heitkaemper, Bosch, Daniel Wehner, Fraunhofer Institute, and Andreas Riess, ZF Group, to discuss what it takes to upskill employees with modern data-driven tools to enable scalable, sustainable, and resilient processes.

3 takeaways they shared with us on building successful data science teams are:

Incorporate Domain Expertise: Successful data science groups are built around diverse expertise. The machinist on the shop floor, the engineer who’s been solving quality issues, the people in IT and network security, they all bring different domain knowledge. This combined knowledge enables the team to build competence. (Frank Heitkaemper, Bosch)

Collaborate with Line of Business: Engineers or people with scientific backgrounds might find it easier to pick up data science skills, but the decisions about implementing data-driven insight are made by managers of business units, clients, or even investors. Being able to easily share information and package expertise in an easily consumable form is an important success factor. (Daniel Wehner, Fraunhofer)

Get Rid of the Tool Discussion! Stakeholders on teams all have their favorite tool or programming language, data format, or storage location and more. Giving each person on the team the flexibility to use what they prefer while enabling collaboration means everyone gets to focus faster on solving the problem. (Andreas Riess, ZF)

Coming back to what GUIs have to do with all this…

GUIs were developed “in reaction to the perceived steep learning curve of command-line interfaces”. They are the foundation of the low-code/no-code concept that makes sophisticated data analytics accessible to even non-technical users.

View the webinar and learn more about KNIME’s no-code/low-code approach to data analytics.

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