Why agentic analytics starts with a well-governed data layer

2026-04-02 · Source: Databricks · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Cloud Computing & IT Infrastructure · Depth: Intermediate, medium

Summary

Databricks VP Nick Eayrs discusses how AI is transforming analytics from static dashboards to dynamic, agentic workflows, highlighting the critical need for robust data semantics and governance. He argues that while AI offers predictive capabilities and real-time insights, it intensifies existing organizational challenges like fragmented definitions and inconsistent metrics. Eayrs emphasizes that trusted AI outcomes depend on a solid data layer with clear business definitions, data lineage, access controls, and open standards. He explains that legacy BI models, with their proprietary semantic layers and tool-locked data, are ill-suited for AI's scale and dynamic querying needs, leading to slow decision-making and high engineering burdens. The solution involves treating business metrics as foundational, making them accessible via standard languages like SQL, ensuring openness and interoperability, and leveraging AI for metadata management.

Key takeaway

For CTOs and VPs of Engineering evaluating AI integration into analytics, your focus must shift to foundational data governance and semantic layers. Fragmented metrics and proprietary BI tools will hinder AI scalability and trust, leading to costly, inconsistent insights. Prioritize establishing a machine-readable, open semantic layer with certified business definitions to ensure your AI initiatives deliver reliable, auditable results.

Key insights

AI amplifies the need for robust data semantics and governance to ensure trusted, scalable analytical outcomes.

Principles

• Trusted AI requires trusted data foundations.
• Open standards enable scalable, interoperable analytics.
• Business metrics are foundational assets.

Method

Establish explicit, certified, and reusable business metrics accessible via standard languages (SQL). Build on open data formats and interfaces, then apply AI-enabled governance for metadata management and conversational intelligence.

In practice

• Define core business metrics explicitly.
• Standardize data access via SQL.
• Prioritize open data formats.

Topics

• Agentic Analytics
• Data Governance
• Semantic Layer
• Legacy BI
• Open Data Standards

Best for: CTO, VP of Engineering/Data, Executive, AI Architect, Director of AI/ML, Data Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Databricks.