Panel discussion - Building Data Products
summary
Panelists argue companies must move from ad hoc analysis to measurable, production data products: examples include recommendation engines that lift conversion, churn models paired with AI segmentation, chatbots for internal efficiency, and marketing mix models tied to profitability dashboards. They report cloud storage costs make keeping years of data cheap (roughly 1 GB ≪ $1/year), stress self‑service BI, multidisciplinary teams, rapid MVP iteration, and KPIs-driven funding as the route that turns prototypes into reliable, adopted services.
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chapters (13)
- 0:00Panel introduction
- 3:05Why data products matter
- 9:27Data in decision-making
- 14:03Industry data challenges
- 17:15Data’s evolution over 25 years
- 19:46Technology is the easy part
- 24:43Startup mistakes with data products
- 28:33Prerequisites for data products
- 33:12Measuring adoption and usability
- 34:37Solving problems vs. products
- 37:06Funding and culture
- 41:50Final messages
- 48:00Closing
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