Data Science Profitability

Keith emphasizes the importance of positioning data science as a profit center rather than a cost center within organizations. He discusses the common reporting structures for data scientists and highlights the confusion that arises when teams report through IT, which can hinder profitability. The conversation also touches on the balance between investing in software tools and human resources to drive value.

In this clip
From this podcast
Super Data Science: ML & AI Podcast with Jon Krohn
655: AI ROI: How to get a profitable return on an AI-project investment — with Keith McCormick
Related Questions
- How can I communicate tomorrow that we don't have to reinvent the wheel since a similar project has already been developed within the organization that has run successfully for the last two and a half years? The next steps are to leverage the Agile analytics schema and adapt the source to Workday, and all the DAX formulas and modeling are already done. This also aligns with my Q4 objectives and Khalil's requirements for the project.

Data Science Profitability

In this clip

From this podcast

Super Data Science: ML & AI Podcast with Jon Krohn

655: AI ROI: How to get a profitable return on an AI-project investment — with Keith McCormick

Related Questions