024 - How Empathy Can Reveal a 60%-Accurate Data Science Solution is a Solid Customer Win with David Stephenson, Ph.D.

David Stephenson, Ph.D., is the author of Big Data Demystified, a guide for executives that explores the transformative nature of big data and data analytics. He’s also a data strategy consultant and professor at the University of Amsterdam. In a previous life, David worked in various data science roles at companies like Adidas, Coolblue, and eBay.

Join David and I as we discuss what makes data science projects succeed and explore:

• The non-technical issues that lead to ineffective data science and analytics projects
• The specific type of communication that is critical to the success of data science and analytics initiatives (and how working in isolation from your stakeholder or business sponsor creates risk))
• The power of showing value early,  starting small/lean, and one way David applies agile to data science projects
• The problems that emerge when data scientists only want to do “interesting data science”
• How design thinking can help data scientists and analytics practitioners make their work resonate with stakeholders who are not “data people”
• How David now relies on design thinking heavily, and what it taught him about making “cool” prototypes nobody cared about
• What it’s like to work on a project without understanding who’s sponsoring it

Resources and Links

DSI Analytics Website

Connect with David on LinkedIn

David’s book: Big Data Demystified 

On Twitter: @Stephenson_Data

Quotes from Today’s Episode

“You see a lot of solutions being developed very well, which were not designed to meet the actual challenge that the industry is facing.” — David

“You just have that whole wasted effort because there wasn’t enough communication at inception.” — David

“I think that companies are really embracing agile, especially in the last few years. They’re really recognizing the value of it from a software perspective. But it’s really challenging from the analytics perspective—partly because the data science and analytics. They don’t fit into the scrum model very well for a variety of reasons.” — David

“That for me was a real learning point—to understand the hardest thing is not necessarily the most important thing.” — David

“If you’re working with marketing people, an 80% solution is fine. If you’re working with finance, they really need exact numbers. You have to understand what your target audience needs in terms of precision.” — David

“I feel sometimes that when we talk about “the business” people don’t understand that the business is a collection of people—just like a government is a collection of real humans doing jobs and they have goals and needs and selfish interests. So there’s really a collection of end customers and the person that’s  paying for the solution.” — Brian

“I think it’s always important—whether you’re a consultant or you’re internal—to really understand who’s going to be evaluating the value creation.”— Brian

“You’ve got to keep those lines of communication open and make sure they’re seeing the work you’re doing and evaluating and giving feedback on it. Throw this over the wall is a very high risk model.” — Brian