033 - How Vidant Health’s Data Team Creates Empathetic Data Products and Ethical Machine Learning Models with Greg Nelson

Greg Nelson is VP of data analytics at Vidant Health, as well as an adjunct faculty member at Duke University. He is also the author of the  “Analytics Lifecycle Toolkit,” which is a manual for integrating data management technologies. A data evangelist with over 20 years of experience in analytics and advisory, Nelson is widely known for his human-centered approach to analytics. In this episode, Greg and I explore what makes a data product or decision support application indispensable, specifically in the complex world of healthcare. In our chat, we covered:

• Seeing through the noise and identifying what really matters when designing data products
• The type of empathy training Greg and his COO are rolling out to help technical data teams produce more useful data products
• The role of data analytics product management and why this is a strategic skillset at Vidant
• The AI Playbook Greg uses at Vidant Health and their risk-based approach to assessing how they will validate the quality of a data product
• The process Greg uses to test and handle algorithmic bias and how this is linked to credibility in the data products they produce
• How exactly design thinking helps Greg’s team achieve better results, trust and credibility
• How Greg aligns  workflows, processes, and best practice protocols when developing predictive models

Resources and Links:

Vidant Health Analytics Lifecycle Toolkit Greg Nelson’s article “Bias in Artificial Intelligence”  Greg Nelson on LinkedIn Twitter: @GregorySNelson Video: Tuning a card deck for human-centered co-design of Learning Analytics

Quotes from Today's Episode

“We'd rather do fewer things and do them well than do lots of things and fail.”— Greg

 

“In a world of limited resources, our job is to make sure we're actually building the things that matter and that will get used. Product management focuses the light on use case-centered approaches and design thinking to actually come up with and craft the right data products that start with empathy.”— Greg

 

“I talk a lot about whole-brain thinking and whole-problem thinking. And when we understand the whole problem, the whole ‘why’ about someone's job, we recognize pretty quickly why Apple was so successful with their initial iPod.”— Greg

 

“The technical people have to get better [...] at extracting needs in a way that is understandable, interpretable, and really actionable, from a technology perspective. It's like teaching someone a language they never knew they needed. There's a lot of resistance to it.” — Greg

 

“I think deep down inside, the smart executive knows that you don’t bat .900 when you're doing innovation.” —  Brian

 

“We can use design thinking to help us fail a little bit earlier, and to know what we learned from it, and then push it forward so that people understand why this is not working. And then you can factor what you learned into the next pass.” — Brian

 

“If there's one thing that I've heard from most of the leaders in the data and analytics space, with regards particularly to data scientists, it’s [the importance of] finding this “other” missing skill set, which is not the technical skillset. It's understanding the human behavioral piece and really being able to connect the fact that your technical work does have this soft skill stuff.” — Brian

 

“At the end of the day, I tell people our mission is to deliver data that people can trust in a way that's usable and actionable, built on a foundation of data literacy and dexterity. That trust in the first part of our core mission is essential.”— Greg