025 - Treating Data Science at IDEO as a Discipline of Design with Dean Malmgren

Dean Malmgren cut his teeth as a chemical and biological engineer. In grad school, he studied complex systems and began telling stories about them through the lens of data algorithms. That led him to co-found Datascope Analytics, a data science consulting company which was purchased by IDEO, a global design firm. Today, Dean is an executive director at IDEO and helps teams use data to build delightful products and experiences. Join Dean and I as we explore the intersection of data science and design and discuss: Human-centered design and why it’s important to data science What it was like for a data science company to get ingested into a design firm and why it’s a perfect match Why data science isn’t always good at creating things that have never existed before Why teams need to prototype rapidly and why data scientists should hesitate to always use the latest tools What data scientists can learn from design team and vice-versa Why data scientists need to talk to end users early and often, and the importance of developing empathy The difference between data scientists and algorithm designers Dean’s opinions on why many data analytics projects fail Resources and Links Twitter: @DeanMalmgren IDEO Datascope Quotes from Today’s Episode “One of the things that we learned very, very quickly, and very early on, was that designing algorithms that are useful for people involves a lot more than just data and code.” — Dean “In the projects that we do at IDEO, we are designing new things that don’t yet exist in the world. Designing things that are new to the world is pretty different than optimizing existing processes or business units or operations, which tends to be the focus of a lot of data science teams.” — Dean “The reality is that designing new-to-the-world things often involves a different mindset than optimizing the existing things.” — Dean “You know if somebody rates a movie incorrectly, it’s not like you’d throw out Siskel and Ebert’s recommendations for the rest of your life. You just might not pay as much attention to them. But that’s very different when it comes to algorithmic recommendations. We have a lot less tolerance for machines making mistakes.” — Dean “The key benefit here is the culture that design brings in terms of creating early and getting feedback early in the process, as opposed to waiting you know three, five, six, seven months working on some model, getting it 97% accurate but 10% utilized.” — Brian “You can do all the best work in the world. But at the end of the day, if there’s a human in the loop, it’s that last mile or last hundred feet, whatever you want to call it, where you make it or break it.” — Brian “Study after study shows that 10 to 20% of big data analytics projects and AI projects succeed. I’ve actually been collecting them as a hobby in a single article, because they keep coming out.” — Brian  

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Are you an enterprise data or product leader seeking to increase the user adoption and business value of your ML/AI and analytical data products? While it is easier than ever to create ML and analytics from a technology perspective, do you find that getting users to use, buyers to buy, and stakeholders to make informed decisions with data remains challenging? If you lead an enterprise data team, have you heard that a ”data product” approach can help—but you’re not sure what that means, or whether software product management and UX design principles can really change consumption of ML and analytics? My name is Brian T. O’Neill, and on Experiencing Data—one of the top 2% of podcasts in the world—I offer you a consulting product designer’s perspective on why simply creating ML models and analytics dashboards aren’t sufficient to routinely produce outcomes for your users, customers, and stakeholders. My goal is to help you design more useful, usable, and delightful data products by better understanding your users, customers, and business sponsor’s needs. After all, you can’t produce business value with data if the humans in the loop can’t or won’t use your solutions. Every 2 weeks, I release solo episodes and interviews with chief data officers, data product management leaders, and top UX design and research professionals working at the intersection of ML/AI, analytics, design and product—and now, I’m inviting you to join the #ExperiencingData listenership. Transcripts, 1-page summaries and quotes available at: https://designingforanalytics.com/ed ABOUT THE HOST Brian T. O’Neill is the Founder and Principal of Designing for Analytics, an independent consultancy helping technology leaders turn their data into valuable data products. He is also the founder of The Data Product Leadership Community. For over 25 years, he has worked with companies including DellEMC, Tripadvisor, Fidelity, NetApp, Roche, Abbvie, and several SAAS startups. He has spoken internationally, giving talks at O’Reilly Strata, Enterprise Data World, the International Institute for Analytics Symposium, Predictive Analytics World, and Boston College. Brian also hosts the highly-rated podcast Experiencing Data, advises students in MIT’s Sandbox Innovation Fund and has been published by O’Reilly Media. He is also a professional percussionist who has backed up artists like The Who and Donna Summer, and he’s graced the stages of Carnegie Hall and The Kennedy Center. Subscribe to Brian’s Insights mailing list at https://designingforanalytics.com/list.