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Why Your ‘Data Exhaust’ Is Your Most Valuable Asset

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Manage episode 501602466 series 75006
Content provided by The New Stack Podcast and The New Stack. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by The New Stack Podcast and The New Stack or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

Rahul Auradkar, executive VP and GM at Salesforce, grew up in India with a deep passion for cricket, where his love for the game sparked an early interest in data. This fascination with statistics laid the foundation for his current work leading Salesforce’s Data Cloud and Einstein (Unified Data Services) team. Auradkar reflects on how structured data has evolved—from relational databases in enterprise applications to data warehouses, data lakes, and lakehouses. He explains how initial efforts focused on analyzing structured data, which later fed back into business processes.

Eventually, businesses realized that the byproducts of data—what he calls "data exhaust"—were themselves valuable. The rise of "old AI," or predictive AI, shifted perceptions, showing that data exhaust could define the application itself. As varied systems emerged with distinct protocols and SQL variants, data silos formed, trapping valuable insights. Auradkar emphasizes that the ongoing challenge is unifying these silos to enable seamless, meaningful business interactions—something Salesforce aims to solve with its Data Cloud and agentic AI platform.

Learn more from The New Stack about the evolution of structured data and agent AI:

How Enterprises and Startups Can Master AI With Smarter Data Practices

Enterprise AI Success Demands Real-Time Data Platforms

Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

  continue reading

902 episodes

Artwork
iconShare
 
Manage episode 501602466 series 75006
Content provided by The New Stack Podcast and The New Stack. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by The New Stack Podcast and The New Stack or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

Rahul Auradkar, executive VP and GM at Salesforce, grew up in India with a deep passion for cricket, where his love for the game sparked an early interest in data. This fascination with statistics laid the foundation for his current work leading Salesforce’s Data Cloud and Einstein (Unified Data Services) team. Auradkar reflects on how structured data has evolved—from relational databases in enterprise applications to data warehouses, data lakes, and lakehouses. He explains how initial efforts focused on analyzing structured data, which later fed back into business processes.

Eventually, businesses realized that the byproducts of data—what he calls "data exhaust"—were themselves valuable. The rise of "old AI," or predictive AI, shifted perceptions, showing that data exhaust could define the application itself. As varied systems emerged with distinct protocols and SQL variants, data silos formed, trapping valuable insights. Auradkar emphasizes that the ongoing challenge is unifying these silos to enable seamless, meaningful business interactions—something Salesforce aims to solve with its Data Cloud and agentic AI platform.

Learn more from The New Stack about the evolution of structured data and agent AI:

How Enterprises and Startups Can Master AI With Smarter Data Practices

Enterprise AI Success Demands Real-Time Data Platforms

Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

  continue reading

902 episodes

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