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Jeff Camm, Professor and Inmar Presidential Chair in Analytics at Wake Forest University School of Business joins Vijay and Mike to explain why so many data science projects fail to deliver ROI, and what his analysis of over 1.4 million job postings reveals about the skills that actually drive decisions.
He shares how he built Wake Forest's MSBA around "the bookends" (problem framing and influence), his blueprint for a new undergrad core(Artificial Intelligence, Business Intelligence, Decision Intelligence) and why managers want choices, not answers. check against transcript for factual accuracy
If your models aren't changing decisions, this one's for you.
What You'll Learn
- The measurable skill-set differences between data science and decision science roles, based on a decade of job posting data
- Why data science projects fail and why the reasons echo OR project failures from decades past
- How to design an analytics program around "the bookends": problem framing and influence
- How to actually teach problem framing (experiential learning, real client messes, no data up front)
- A three-course blueprint for the undergrad business core: Artificial Intelligence, Business Intelligence, Decision Intelligence
- Why prescriptive analytics should deliver families of solutions, not single answers
- Why you should ask an LLM for choices, not answers and what that means for teaching analytics in the AI era
Timestamps
0:00 - Preview
1:01 - Meet Jeff Camm
1:40 - Why "decision scientist," not "data scientist"
4:20 - The research outcome: academia versus 1.4M job ads
9:10 - Why data projects fail
12:50 - Building Wake Forest's Masters in Business Analytics from scratch
14:42 - The bookends: problem framing and influencing
18:22 - Teaching problem framing: the practicum and "Mess to Model"
20:35 - Team charters and learning to work in teams
22:50 - A new undergrad core: AI → BI → Decision Intelligence
29:10 - Problem-centric teaching and just-in-time technique
33:30 - "Nobody likes to be told what to do": choices, not answers
34:50 - LLMs, families of solutions, and the risk/return trade-off
38:40 - Jeff's papers and where to learn more
Resources:
- "Data Science and Decision Science Skills: Are They Different and Does It Matter?" (Camm, Fry & Shafer, Harvard Data Science Review, Summer 2025): https://hdsr.mitpress.mit.edu/pub/9ir6e1j6/release/1
- The INFORMS Journal on Applied Analytics: https://pubsonline.informs.org/authored-by/Camm/Jeffrey+D
- "Stop Modeling Just the Data, Start Modeling Decisions" (forthcoming in MIT Sloan Management Review digital at time of publishing)
Follow the show
Apple: https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064
Spotify: https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b
Connect with guest
- Jeff Camm: https://www.linkedin.com/in/jeff-camm-395b366/
Connect with hosts
- Prof. Vijay Mehrotra (University of San Francisco): https://www.linkedin.com/in/vijay-mehrotra-ba9498/
- Prof. Michael Watson (Northwestern University): https://www.linkedin.com/in/michael-watson-07600a1
About the podcast
The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.
For business inquiries, email at decisionintelligencepodcast@gmail.com
