HeyJaredDatAInnovators & Builders
DatAInnovators & Builders features Chief Data Officers and data leaders sharing real strategies for conquering data complexity and building AI solutions that work.
- Indexed episodes, last 90 days
- 6
- Latest publication
- Sep 22, 2026
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- Earliest in this view
- Jul 14, 2026
Latest episodes
Why Your AI Strategy Is Really Just Your Data Strategy (opens the original)
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Your AI strategy is really just your data strategy. Christian J. Ward , CDO and EVP at Yext , has built his career on that principle, from NLP-driven signal analysis on Wall Street to synchronizing brand data across hundreds of endpoints. Training your own models is not where he would be spending his time. Christian walks Saket through how opening Yext's data via MCP servers transformed client interactions overnight, why he tracks token usage by department and model, and how correlating weather
Why the real AI moat lives in your data when models are commodities (opens the original)
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Terry Miller , Vice President AI & Machine Learning at Omada Health , calls frontier models "very expensive commodities." With 14 years of longitudinal data for over a million members, the real competitive advantage lives in proprietary data and the workflows built on top of it. Terry walks Saket through how reusable templates and tightly bounded applications make agent deployments succeed, why combining traditional machine learning with LLMs unlocks new capabilities, and what "edge healthcare"
Why grand unified data architecture kills AI projects before they start (opens the original)
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For Anusha Dandapani, Chief Data & AI at UNICC, what keeps her up isn't model accuracy. It's the asymmetry of consequences. A wrong commercial AI call costs money, but in a humanitarian context, it can mean aid never reaches the people who need it. She tells Saket why she'd rather run a less sophisticated model with rigorous decision architecture than drop in a state-of-the-art system, and why nobody wants to talk about data lineage until it's too late. Topics discussed: Weighing asymmetry of co
Why AI programs stall after the pilot: the accountability gap no one has solved (opens the original)
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Most enterprises know AI has value. Getting it into the financials is a different problem entirely. Asif Mujahid has spent over a decade in healthcare data and AI, and his read is direct: the technology problem is largely solved. What remains is trust, governance, and accountability, and most organizations are still working out how to get there. Asif breaks down why centralizing data and AI ownership is non-negotiable, how he frames the CDO role across three distinct archetypes, and what it actu
Why most enterprise data isn't actually big data (opens the original)
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Most data leaders chase distributed infrastructure they don't need, paying what Mahesh Mishra calls a complexity tax. As VP of Artificial Intelligence at Cloudera, he argues most enterprise data isn't big data at all, and platform readiness, more than model quality, is the real barrier to production AI. Mahesh breaks down knowledge engineering as the missing layer beyond prompt and context engineering, explains why traditional data catalogs are becoming obsolete, and maps out how Iceberg and Lan
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