Podcast: Don’t Panic It’s Just Data!

Guest: Adrian Estala, VP, Field Chief Data & AI Officer, Starburst

Host: Doug Laney, Research & Advisory Fellow at BARC and Author of Infonomics & Data Juice

After years of heavy investment in data lakes and warehouses, many enterprises still face a frustrating reality. Insights continue to remain slow, fragmented, and hard to trust. 

In the recent episode of the Don’t Panic It’s Just Data podcast, host Doug Laney, Research & Advisory Fellow at BARC and Author of Infonomics & Data Juice, is joined by Adrian Estala, VP, Field Chief Data & AI Officer at Starburst. They sat down to discuss why more enterprises are adopting a new architectural approach, the business semantic layer, to speed up AI adoption.

What’s the Core Issue in AI Data Enterprise?

The core issue, Estala argues, is not a lack of infrastructure but an inconsistency between how data is organised and how enterprises think. “No one’s really there yet,” he says, reflecting on a decade of backend optimisation. “We don’t know what ‘perfect’ architecture means, especially in the AI age.”

The semantic layer, sometimes called a “context layer,” represents a shift from technical complexity to business usability. Typically, the system requires non-technical users to interpret schemas and pipelines; however, Starburst provides an abstraction that shows data in familiar business terms, along with metadata and governance rules.

“If you build it right,” Estala explains, “when a CFO walks in the room and sees their semantic layer, it makes sense to them.”

For an enterprise, this is more than just a usability improvement. It reduces duplication, eliminates conflicting metrics, and reduces reliance on IT teams for routine analysis. As Laney notes during the discussion, the goal is not to replace existing systems but to make them “that much more accessible” by layering business meaning on top.

Also Watch: AI Is Replacing BI — Here’s What CIOs Need to Know

Sovereignty, Governance & the European Reality

The conversation is even more acute in regions like Europe, where data sovereignty has become a major concern. Regulatory pressure has led enterprises to rethink not only where data is stored but also how it is accessed and shared.

Estala describes a federated model where data stays within national boundaries while still being usable globally. Organisations set up local clusters in countries like Switzerland or the United Kingdom, build data products locally, and apply strict rules for what can be shared centrally.

“I can decide which data products are approved to be shared,” he says, alluding to compliance mechanisms that ensure sensitive information cannot be traced back to individuals.

This creates a system that satisfies both regulators and business leaders. Executives no longer need to worry about jurisdictional complexities; they work with a unified view of data that has already been filtered, governed, and approved. “For them, it just feels like it’s already been brought together,” Estala adds.

As AI agents and copilots continue to gain popularity, the discussion also spotlights limitations. One such limitation is trust. Without confidence in the underlying data, even the most advanced AI tools struggle to provide meaningful value.

“If they don’t trust the answers, it’s just a cool toy,” Estala says, describing a common pattern where initial excitement fades once users doubt the reliability of outputs.

The semantic layer also tackles this discrepancy by embedding governance, lineage, and business rules directly into data products. Starburst helps enterprises clearly define which data is exposed to AI systems and under what conditions, making it easier to explain and justify decisions.

Currently, Estala observes, AI mainly speeds up existing workflows instead of transforming them. Executives are asking the same questions they always have, but getting answers faster and from broader datasets. The real change, he suggests, will come when trust allows leaders to ask entirely new questions and rethink decision-making.

How to Drive Business Value in 90 Days?

For CIOs and CDOs eager to move past experimentation, the Chief Data and AI officer outlines a focused, business-led approach. Rather than launching large-scale transformations, he suggests starting with a single domain and building momentum from there.

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The first phase focuses on collaboration, bringing business stakeholders into the design of the semantic layer and defining the data products that are most important. “We design it with the business team in the room,” he explains, stressing ownership from the start.

The next stage shifts to enablement, as teams begin to use and expand these data products themselves. This is where self-service takes root, reducing dependence on IT and promoting more exploratory use of data.

By the final phase, enterprises are ready to introduce AI agents on top of a trusted foundation. At that stage, technology becomes almost secondary. “Once you get to a semantic layer that you trust, adding an agent is easy,” Estala says.

As enterprises continue to adopt AI at larger scales, their competitive edge will come from algorithms and from how effectively they organise, govern, and contextualise their data. In this sense, the semantic layer is quickly becoming the backbone of modern, AI-driven decision-making.

Key Takeaways

  • Semantic layers make governed data accessible for enterprise AI.
  • Data sovereignty drives federated, compliant data architectures.
  • Trusted AI needs governed, metadata-rich data products.
  • Semantic layers deliver business value within 90 days.
  • Virtual layers reduce duplication and speed up analytics.

Chapters

  • 00:00 The Shift to Business Semantic Layers
  • 08:02 Data Sovereignty and Governance in Modern Strategies
  • 13:08 Foundational Capabilities for AI Systems
  • 18:11 AI Agents and Decision Making
  • 23:04 Practical Steps for Implementing Semantic Layers

To learn more about how data products and AI agents are changing enterprise analytics, follow:

Starburst LinkedIn: @Starburst

Starburst X: @starburstdata

Starburst YouTube: @StarburstData

EM360Tech YouTube: @enterprisemanagement360

EM360Tech LinkedIn: @EM360Tech

EM360Tech X: @EM360Tech

Follow: @EM360Tech on YouTube, LinkedIn and X

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