Richard Tomlinson, who leads product marketing for Databricks’ business intelligence and analytics products, has spent his career championing technology that tames data to help people make better decisions and fuel innovation. In this conversation, he discusses how natural-language querying for visualizations can finally offer more than SQL, how to make sure AI-powered analytics are trustworthy and how business context is key.
Why natural-language querying for visualizations has underdelivered
Natural-language querying for visualizations has been around for a decade. Why hasn’t it lived up to its promise?
Richard Tomlinson: The promise has been around since the early days of search-driven analytics, but it consistently underdelivered because the systems could parse the question but couldn’t understand the business.
Earlier generations of NL-to-SQL treated the problem as purely a language-to-schema translation — map words to columns, generate a query and return a chart. That works for simple lookups, but it falls apart in the face of enterprise data complexity. In real life, you may have competing definitions of the same metric across teams, ambiguous joins between tables, business rules that live in people’s heads rather than in metadata and no way to decide which source is authoritative when two numbers conflict.
The LLMs weren’t the bottleneck; context was. Even as models improved dramatically, they were still guessing at meaning from raw table names and column headers, which is why the same question asked twice could produce two different answers.
What changed isn’t just better models; it’s the recognition that you need a governed semantic layer underneath the language model that tells the agent what “revenue” means for this team, which table is the certifiedys apply. Without that, natural-language querying was a demo that impressed in a conference room but not with real enterprise data
Generating trustworthy answers for every user
Natural language has opened AI-powered analytics up to everyone. But how do you make sure non-specialists get answers they can trust?
Richard Tomlinson: Enabling more people to ask questions is the easy part. What’s hard is making the answers trustworthy enough to act on. Three things have to be true underneath.
First, the data itself has to be governed. You need clean, well-organized data built around how the business actually works, where categories are consistent across the business, each record is clearly defined and there are no duplicate customers or products. It all needs to be secured through fine-grained access controls so every user sees only what they’re authorized to see.
Second, the meaning of that data has to be explicit and machine-readable, not buried in tribal knowledge or someone’s spreadsheet. You need governed metric definitions, documented business terms, clearly defined primary and foreign key relationships and certified sources, all registered in a system like Unity Catalog where an AI agent can consume them at query time.
Third, there has to be a trust and verification layer, with the ability to trace every AI-generated answer back to the definitions, sources and permissions that produced it. This enables a business user to inspect why the system said what it said, not just what it said. That’s the foundation of Genie One: governed data, explicit semantics and inspectable provenance. Without it, you’re just giving everyone a faster way to get the wrong answer.
How semantic grounding changes the game
What is semantic grounding and how does it contribute to effective natural language-based data visualizations?
Richard Tomlinson:Semantic grounding is the process of anchoring an AI agent’s interpretation of a question to the organization’s actual business definitions, relationships and rules, rather than letting the model infer meaning from column names and hope for the best.
In practice, this means that before a single line of SQL is generated, the agent already knows that “net revenue” uses a specific approved calculation, that it should be pulled from a certified finance table rather than a marketing summary, that fiscal quarters start in February and that free-trial users are excluded from retention metrics, for example.
At Databricks, Genie Ontology provides a unified context layer that combines explicitly modeled knowledge — such as metric views, governed pages, domains and certified sources — with context automatically learned from how the organization actually works. That context is extracted directly from dashboards, queries, notebooks and other enterprise assets.
Each piece of learned context, called an Ontology Snippet, carries an authority score based on itsery time, a ranking system evaluates competing snippets on both relevance and authority, resolves conflicts and delivers only the context the user is permitted to see
A visualization created using this process isn’t just a good-looking chart. It’s grounded in the same definitions and logic that the organization’s domain experts would use, which makes the difference between a plausible answer and a trustworthy one.
