This year, Martin Szugat and with him Datentreiber were once again invited multiple times to podcast discussions, expert panels, and expert interviews, as guest authors or guest lecturers. Naturally, most of these talks revolved around data strategies, data thinking, and data strategy design. In case you missed any of them, we have compiled a short selection of the most interesting contributions for you to listen to and watch in this blog post.
🎧 Digital Upgrade Podcast: Generating More Value from Facebook and Co. Data
Every company consciously and often unconsciously collects a variety of data and data signals. But how can a business utilize the vast amount of data effectively and profitably? This question was answered in the Digital Upgrade Podcast with Thomas Besmer from Hutter Consult AG.
🎧 The Erium Podcast: For Which Companies Does Data Science Even Make Sense?
What is the difference between data science and computer science? How do you develop a corporate strategy regarding future data science use cases? These and other questions were discussed by Martin Szugat and Dr. Maksim Greiner on The Erium Podcast.
🎧 Digital Heroez: Boys Need Toys and 4 Other Typical Mistakes
If you want to learn how to avoid the five typical mistakes when developing a data strategy, then listen to the Digital Heroez Talk Podcast for digital superpowers with Lena Saldner and Datentreiber’s Martin Szugat.
🎧 Datenbusiness Podcast: Datenchefs #22
The Datenbusiness Podcast focuses on the key topics surrounding data value creation and data science applications in the business world. In it, Dr. Bernard Sonnenschein regularly speaks with leaders who, in one way or another, work with data refinement. Among them is Datentreiber’s Martin Szugat, who discusses what makes a good data strategy, how to start developing such a strategy, and shares real project examples.
🎧 Erde 5.0 – Perspektivwechsel: Artificial Intelligence
Karl-Heinz Land and Roland Fiege spoke with Datentreiber’s Martin Szugat about the opportunities, risks, and current status of artificial intelligence. Beyond the topic of data economics, they also discussed how digitalization can be achieved in Germany.
🎧 Podcast KI in der Industrie – The Success Factor of Data Strategy
In the KI Podcast, Datentreiber’s Martin Szugat talks to Peter Seeberg about the value of a data strategy and reveals the questions companies should ask themselves to successfully leverage data as a production factor.
🎧 Experiencing Data: Using Design Thinking to Maximize Effectiveness
Want to find out how our data strategy design was created and discover some interesting use cases from our Datentreiber clients? Then tune in to the Experiencing Data Podcast. Brian O’Neill and Martin Szugat also discuss how design thinking can drive data innovation.
🎬 Data Brain Meetup: Open Source Toolbox for Data Thinking
The recording of the Data Brain Meetup with Datentreiber’s Martin Szugat and design thinking expert Martijn Bakker shows you the most important tools and interesting use cases of our data strategy design. These tools are available for free so you can apply them to your AI projects.
🎬 BOECKBX: Is data the new oil?
Is data the new oil? That was the key question discussed by Datentreiber’s Martin Szugat and Fabian Böck in his tech podcast. They also explored how companies can evolve to become data-driven, along with other emerging trends.
🎬 B2B Digital Masters Convention: Data-Driven Business
Why is “boring the new sexy”? Find out in the B2B Digital Masters Convention recording. The “most boring” optimizations often produce the best results, as Datentreiber’s Martin Szugat observed during a panel discussion with four other experts on the exciting topic of data-driven business.
👓 Professional Articles with Valuable Tips
We were also invited to write several articles and interviews to address central questions, for example how to eliminate marketing cost drivers in 3 steps and why business intelligence leads to better decisions in marketing. In the three-part series on design thinking for data products, we explore how design thinking helps develop data products that users truly want, are economically viable, and are technically feasible, and how to combine the organizational, user, and data perspectives.
