Building a data team

AI Expectations

Navigating the expectations of executives versus actual business needs for AI is a common challenge. Chris Benson highlights that executives often expect advanced AI solutions, but the immediate need might be basic data aggregation and metrics. Daniel Whitenack adds that executives are often removed from technical details, necessitating a gentle education process to align expectations with reality 1.

The harder you try to do it, the faster you're running into that brick wall.

--- Daniel Whitenack

Both emphasize the importance of rethinking data infrastructure to ensure successful AI modeling, as fragmented data can hinder progress 1.

   

Prototyping

Prototyping AI solutions is crucial for demonstrating value and feasibility. Chris Benson suggests using tools like Streamlit to create prototypes that showcase potential benefits without full-scale development. He notes that while data teams may not always build robust products, they should aim to create prototypes that effectively communicate value 2.

Do we expect data teams or data scientists to actually build robust products?

--- Chris Benson

Daniel Whitenack agrees, emphasizing the importance of lightweight prototyping to refine ideas and prove their viability before full implementation 2.