075 - How CDW is Integrating Design Into Its Data Science and Analytics Teams with Prasad Vadlamani

How do we get the most breadth out of design and designers when building data products? One way is to have designers be at the front leading the charge when it comes to creating data products that must be useful, usable, and valuable.

 

For this episode Prasad Vadlamani, CDW’s Director of Data Science and Advanced Analytics, joins us for a chat about how they are making design a larger focus of how they create useful, usable data products. Prasad talks about the importance of making technology—including AI-driven solutions—human centered, and how CDW tries to keep the end user in mind. 

Prasad and I also discuss his perspectives on how to build designers into a data product team and how to successfully navigate the grey areas between various areas of expertise. When this is done well, then the entire team can work with each other's strengths and advantages to create a more robust product. We also discuss the role a UI-free user experience plays in some data products, some differences between external and internally-facing solutions, and some of Prasad’s valuable takeaways that have helped to shape the way he thinks design, data science, and analytics can collaborate.

 

In our chat, we covered: 

 

• Prasad’s first introduction to designers and how he leverages the disciplines of design and product in his data science and analytics work (1:09)
• The terminology behind product manager and designer and how these functions play a role in an enterprise AI team (5:18)
• How teams can use their wide range of competencies to their advantage (8:52)
• A look at one UI-less experience and the value of the “invisible interface” (14:58)
• Understanding the model development process and why the model takes up only a small percentage of the effort required to successfully bring a data product to end users (20:52)
• The differences between building an internal vs external product, what to consider, and Prasad’s “customer zero” approach. (29.17)
• Expectations Prasad sets with customers (stakeholders) about the life expectancy of data products when they are in their early stage of development (35:02)