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Key Takeaways: ✅ Data-to-Action, Data-to-Value, and Data-to-Insights are Costly and Slow with Centralized Data Storage and Schema Arch.✅ Nextdata Has Revolutionized Data Architecture with "Autonomous Data Products." ✅ Making Data Products Self-Contained Applications, Running in Containers with Nextdata OS with Their Own API ✅ Highly Scalable, Constantly Managed by Agents, Easily Accessible by Other Agents. Podcast Guest: Zhamak's LinkedIn Page: ✅ / zhamak-dehghani Nextdata LinkedIn Page: ✅ / nextdata-technologies Website: ✅ https://www.nextdata.com/ #nextdata #dataarchitecture #datatools #datastartups #containers #scalable #aistartups #zhamakdehghani #aistartups #aistartupceo Podcast Host: Walter Bacon, Founder, Walter Bacon, Inc. Recruiting ✅ Recruiting Firm Specializing in Early Stage AI and Developer Tools Startups. ✅ Helping CEOs and Founders of AI Startups Achieve Their Growth Goals by Recruiting High-Impact Engineering and GTM Individual Contributors and Leaders with Domain Expertise. Walter's LinkedIn: ✅ / walterbacon Walter's Website: ✅ https://walterbacon.com/client-endors... Walter's YouTube Channel: (My CEO Interviews. Game-Changing Practical B2B AI Startups) ✅ / @walterbaconinc #walterbacon #friendsofwalterbacon #recruitingforaistartups #aistartupjobs #recruiters #recruiting #airecruiting #engineeringrecruitment #engineeeringrecruiter #siliconvalleyrecruiter #startuprecruiter 0:00 - Introduction 0:19 - What Problems Does Nextdata Address? Data to Action, Data to Value, and Data to Insights are Costly and Slow 1:57 - Can You Explain Data 3.0 vs. the Old Model? Disconnected, Lacks Context, Storage-Centric, Schema-Centric, Very Difficult for AI Agents 4:10 - Can You Explain Data 3.0 and "Autonomous Data Products?" Constantly Running, Updating, Self-Managed, Fully Available by API 7:00 - Can You Explain the Nextdata OS, the Container, and How it Scales? 9:34 - Can You Describe the Magnitude of Speed Gained with Your Platform? 10x to 50x Depending on the Client 3 Strategies: Standardization, Automation, Agentic AI to Produce the Data Product 11:50 - Who is Your Ideal Prospect? Environment Complexity and Domain Complexity 12:09 - Customer Success Story 13:10 - How Can People Learn More?