The DAIS 2026 Speaker Spotlight is a series where we hand the mic to the speakers heading to Data + AI Summit and let them answer five short questions — in their own voice, no press-release polish.
Below, Jasmeet Jaggi on why business semantics is the foundational trust layer for self-service analytics — and how Unity Catalog Metric Views are changing what's possible for both human users and AI agents. Lightly edited for length — otherwise, the words are his.
“
An AI is only as good as the data definitions it's fed.
— Jasmeet Jaggi
The topic
What is your talk about, and who is it for?
This session is the ultimate 'A to Z' guide on business semantics, specifically designed for data practitioners managing Databricks gold and platinum layers who want to unlock true, dependable self-service analytics for their business users.
Why this, why now
What's changed in the last 6–12 months that makes this topic urgent right now?
AI agents and conversational chat systems are rapidly pushing the boundaries of self-service data analytics, but they only work if they can find a trusted source. Business semantics is the foundational piece for generating trusted, accurate insights. Over the last year, the Databricks platform has introduced massive advancements in core analytics features via Metric Views. Teams can achieve lightning-fast performance without the typical asset bloat in their gold and platinum layers, native measure support in SQL makes interop and advanced analytics easy — all secured and governed seamlessly by Unity Catalog.
The personal stake
Why are you the person giving this talk?
I've been building self-service data and analytics products for over a decade. Throughout my career, I've watched tools introduce incredibly sophisticated data modeling features that ultimately still barely scratched the surface of true self-service. Business Semantics changes everything. It gives us a unique opportunity to empower customers to deliver dashboards, AI agents, and apps that let business users explore data fully without being constantly bottlenecked by engineering teams. What makes the Databricks approach so compelling is its openness; customers can consume these insights in whatever tools they prefer without ever having to compromise on correctness or performance.
What you'll leave with
What will someone be able to do on Monday morning that they couldn't do before?
By Monday morning, attendees will walk away knowing how to:
- Leverage the advanced analytics capabilities packed into Unity Catalog Metric Views and Semantic Graphs.
- Achieve fast, predictable query performance without the headache of managing data extracts, manual pre-aggregates, or complex rollup tables.
- Query metrics natively in SQL and execute seamless performance joins across Metric Views and standard Tables.
- Implement cross-fact analysis and deploy robust semantic models directly inside Databricks Dashboards.
The bigger picture
How does this fit into where Databricks — and data and AI more broadly — is heading?
AI has completely transformed how people expect to interface with their data, and Databricks Genie is leading the charge by offering a single, interactive conversational experience for analytics. However, an AI is only as good as the data definitions it's fed. Business semantics serves as that critical, foundational trust layer — the connective tissue that ensures both human users and AI agents can consume data and make high-stakes business decisions with absolute confidence.
Part of the DAIS 2026 Speaker Spotlight series — more voices dropping in the weeks ahead. Got a DAIS speaker you'd love to hear from next? Mention them in the comments — we're always listening.
