065 - Balancing Human Intuition and Machine Intelligence with Salesforce Director of Product Management Pavan Tumu
I once saw a discussion on LinkedIn about a fraud detection model that had been built but never used. The model worked — it was expensive — but it just simply didn’t get used because the humans in the loop were not incentivized to use it.
It was on this very thread that I first met Salesforce Director of Product Management Pavan Tuvu, who chimed in on the thread about a similar experience he went through. When I heard about his experience, I asked him if he would share it with you and he agreed. So, today on the Experiencing Data podcast, I’m excited to have Pavan on to talk about some lessons he learned while designing ad-spend software that utilized advanced analytics — and the role of the humans in the loop. We discussed:
Pavan's role as Director of Product Management at Salesforce and how he works to make data easier to use for teams. (0:40)
Pavan's work protecting large-dollar advertising accounts from bad actors by designing a ML system that predicts and caps ad spending. (6:10)
'Human override of the machine': How Pavan addressed concerns that its advertising security system would incorrectly police legitimate large-dollar ad spends. (12:22)
How the advertising security model Pavan worked on learned from human feedback. (24:49)
How leading with "why" when designing data products will lead to a better understanding of what customers need to solve. (29:05)
Om Podcasten
Are you an enterprise data or product leader seeking to increase the user adoption and business value of your ML/AI and analytical data products?
While it is easier than ever to create ML and analytics from a technology perspective, do you find that getting users to use, buyers to buy, and stakeholders to make informed decisions with data remains challenging?
If you lead an enterprise data team, have you heard that a ”data product” approach can help—but you’re not sure what that means, or whether software product management and UX design principles can really change consumption of ML and analytics?
My name is Brian T. O’Neill, and on Experiencing Data—one of the top 2% of podcasts in the world—I offer you a consulting product designer’s perspective on why simply creating ML models and analytics dashboards aren’t sufficient to routinely produce outcomes for your users, customers, and stakeholders. My goal is to help you design more useful, usable, and delightful data products by better understanding your users, customers, and business sponsor’s needs. After all, you can’t produce business value with data if the humans in the loop can’t or won’t use your solutions.
Every 2 weeks, I release solo episodes and interviews with chief data officers, data product management leaders, and top UX design and research professionals working at the intersection of ML/AI, analytics, design and product—and now, I’m inviting you to join the #ExperiencingData listenership.
Transcripts, 1-page summaries and quotes available at: https://designingforanalytics.com/ed
ABOUT THE HOST
Brian T. O’Neill is the Founder and Principal of Designing for Analytics, an independent consultancy helping technology leaders turn their data into valuable data products. He is also the founder of The Data Product Leadership Community. For over 25 years, he has worked with companies including DellEMC, Tripadvisor, Fidelity, NetApp, Roche, Abbvie, and several SAAS startups. He has spoken internationally, giving talks at O’Reilly Strata, Enterprise Data World, the International Institute for Analytics Symposium, Predictive Analytics World, and Boston College. Brian also hosts the highly-rated podcast Experiencing Data, advises students in MIT’s Sandbox Innovation Fund and has been published by O’Reilly Media. He is also a professional percussionist who has backed up artists like The Who and Donna Summer, and he’s graced the stages of Carnegie Hall and The Kennedy Center.
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