Why AI Builders Need a Metadata Goldmine with Chris Aberger, VP at Alation

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Jun 18, 2025

Chris Aberger, VP at Alation and co-founder of Numbers Station AI, dives into the transformative role of metadata in AI applications. He discusses the challenges of making structured data AI-ready and why quick iterations are vital for success. Chris emphasizes the importance of feedback loops in enhancing AI effectiveness and empowers end-users to become builders through innovative tools. The conversation also sheds light on navigating the complexities of AI deployment and the importance of collaboration in the evolving tech landscape.

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ANECDOTE

Startup Born from Stanford Research

Numbers Station began from a Stanford research paper on applying LLMs to structured data.
The founders quickly converted this insight into a startup, launching the company within eight months.

ADVICE

Iterate Fast to Solve Real Problems

Iterate quickly to find product-market fit and learn what buyers truly need.
Focus on solving executive purchaser problems rather than only enhancing data engineer productivity.

INSIGHT

Beyond Chat: Agentic Workflows

Chat with your data is a useful entry point but not the end goal.
The evolution is toward agentic workflows that get things done on data, not just deliver insights.

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The future of business intelligence is being rewritten. Have you ever wondered how AI will unlock the power of unstructured data?

In this episode of Data Radicals, host Satyen Sangani is joined by Chris Aberger, newly-minted VP at Alation to discuss building AI-powered data workflows.

A startup pioneer, Chris explores the importance of metadata in enhancing AI applications within organizations, the significance of quick iterations, and the evolving role of AI engineers.

“ That two-step realization is what's causing a lot of this activity that we're seeing in the market, which is, I know I need to plug into databases. I'm now coming to terms with the fact that this is actually a really tough problem to get right.”

Listen to this episode to learn:

Why metadata curation and feedback loops are crucial for making AI effective
The necessity of a fast-paced, iterative approach in developing AI solutions
How to enable end-users to become builders through AI and metadata tools

Listen now: alation.com/podcast/episodes/ai-builders-metadata-chris-aberger

*Satyen’s narration was created using AI

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“People have realized that, okay, like structured data is actually like the hard problem to get right. And all these organizations' really valuable data is inside their databases in the structured formats. We have to figure out how to make this ready for the AI era. And then the kind of second level problem that people are discovering is how do I make this structured data actually work? Oh, it's metadata. And I think that realization that that kind of two-step realization is what's causing a lot of this activity that we're seeing in the market, which is, I know I need to plug into databases. I'm now coming to terms with the fact that this is actually a really tough problem to get right. In order to get it right, I need to effectively go build a data catalog or metadata provider, and therefore we're seeing a lot of activity in this space.” – Chris Aberger

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Time Stamps

*(02:04): From the Stanford AI Lab to founding Numbers Station

*(12:10): From chat with your data to act with your data: From data users to business builders

*(19:23): The value of metadata to production-ready AI

*(28:46): What are precision agentic workflows?

*(35:35): Empowering enterprise data users to build with AI

*(45:50): Satyen’s takeaways

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Sponsor

This podcast is presented by Alation.

Learn more:

* Subscribe to the newsletter: https://www.alation.com/podcast/

* Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/

* Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/

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Links

Connect with Chris on LinkedIn