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
“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