If you’re investing in AI to power faster decisions, smarter insights, or automated workflows, you're betting on tools that rely on context. And context lives in metadata — not just schemas, but human-added information like descriptions, tags, and ownership.
For years, creating that kind of metadata felt like clean-up work. Important, but never urgent. It came after the dashboard launched or once the fires were out. Often it was treated as a compliance checkbox, useful for audits but disconnected from daily work. And even when teams cared, keeping metadata current was tedious, manual, and thankless.
Then AI happened.
Now teams are asking questions in Looker Chat. LLMs are writing SQL. Agents are resolving incidents. And all those incomplete descriptions, unclear owners, and placeholder tags? They’re no longer harmless. Now they can break trust, mislead AI tools, and derail decisions.
The Real Problem Isn't the Model. It's the Missing Context
Before AI, getting answers from data usually meant asking someone. Analysts, engineers, and domain experts didn’t always have perfect context — but they asked questions, made smart assumptions, chased down the right people, or raised a flag when something looked off. Even with gaps, they filled them with judgment.
Now, AI tools are being asked the same questions.
And while AI can process far more data than a person ever could, it doesn’t know what’s deprecated, which version is trusted, or whether that users table is actually test data. It doesn’t see Slack threads or tribal knowledge. It sees column names, schema, and metadata — if it exists. And when that metadata is missing or incomplete, it doesn’t pause or ask. It fills in the blanks.
That’s how a staging table powers a board report, or a retired metric ends up in a forecast. Not because the model made a wild leap, but because it wasn’t given enough to go on.
That’s the risk: not that AI makes mistakes, but that it hides them behind confidence.
AI Monitoring Isn’t the Whole Picture
AI results monitoring is a valuable tool — it helps teams track how AI models behave, review generated SQL, and catch problems that slip through.
But most AI errors don’t come from unpredictable leaps. They happen because the input was missing something. A table that looked fine wasn’t. A metric had quietly gone stale. A key definition changed, and no one flagged it.
Monitoring catches the outcome. Metadata prevents the mistake.
We Need a Smarter Way to Keep Metadata in Shape
The challenge is that metadata was never designed for speed. Creating it takes time. Maintaining it takes discipline. And historically, it’s been hard to justify that investment, since the cost of bad metadata didn’t show up right away.
This urgency isn’t just about performance. It’s also about protection. As more teams adopt AI, compliance and privacy increasingly depend on metadata — not just for audits, but to define ownership, tag sensitive fields, and enforce access controls in real time.
AI is raising the stakes. But it’s also giving us new ways to solve the problem.
How Elementary’s AI Agents Help
This is where Elementary’s agents come in.
- The Governance Agent identifies and fills metadata gaps based on your usage and policies. It creates missing descriptions, suggests tags, and assigns owners automatically.
- The Catalog Agent makes that metadata usable. It helps humans and AI tools search and select the right assets with full context — not just names, but health, ownership, and trustworthiness.
They don’t just patch things up. They help you build a system that scales and keeps up.
The Next Era of AI Needs Metadata That Works
You don’t have to take our word for it. In just the past six months, metadata and governance platforms have been at the center of major acquisitions: Salesforce acquiring Informatica, ServiceNow picking up Data.World, and Cloudera buying Octopai. These aren’t just product expansions. they're strategic bets on metadata as the foundation for enterprise AI.
We talk a lot about scaling AI, building copilots, embedding agents. But none of that works reliably without solid foundations, and the culture to maintain them.
If you want AI to power how your business thinks and acts, invest in metadata that's accurate, operational, and alive.
Start with what's been ignored:
- Treat ownership as non-negotiable.
- Treat metadata gaps as reliability risks.
- Treat tags and descriptions as part of the interface AI uses to make decisions.
Because it already is.
If you’re investing in AI to power faster decisions, smarter insights, or automated workflows, you're betting on tools that rely on context. And context lives in metadata — not just schemas, but human-added information like descriptions, tags, and ownership.
For years, creating that kind of metadata felt like clean-up work. Important, but never urgent. It came after the dashboard launched or once the fires were out. Often it was treated as a compliance checkbox, useful for audits but disconnected from daily work. And even when teams cared, keeping metadata current was tedious, manual, and thankless.
Then AI happened.
Now teams are asking questions in Looker Chat. LLMs are writing SQL. Agents are resolving incidents. And all those incomplete descriptions, unclear owners, and placeholder tags? They’re no longer harmless. Now they can break trust, mislead AI tools, and derail decisions.
The Real Problem Isn't the Model. It's the Missing Context
Before AI, getting answers from data usually meant asking someone. Analysts, engineers, and domain experts didn’t always have perfect context — but they asked questions, made smart assumptions, chased down the right people, or raised a flag when something looked off. Even with gaps, they filled them with judgment.
Now, AI tools are being asked the same questions.
And while AI can process far more data than a person ever could, it doesn’t know what’s deprecated, which version is trusted, or whether that users table is actually test data. It doesn’t see Slack threads or tribal knowledge. It sees column names, schema, and metadata — if it exists. And when that metadata is missing or incomplete, it doesn’t pause or ask. It fills in the blanks.
That’s how a staging table powers a board report, or a retired metric ends up in a forecast. Not because the model made a wild leap, but because it wasn’t given enough to go on.
That’s the risk: not that AI makes mistakes, but that it hides them behind confidence.
AI Monitoring Isn’t the Whole Picture
AI results monitoring is a valuable tool — it helps teams track how AI models behave, review generated SQL, and catch problems that slip through.
But most AI errors don’t come from unpredictable leaps. They happen because the input was missing something. A table that looked fine wasn’t. A metric had quietly gone stale. A key definition changed, and no one flagged it.
Monitoring catches the outcome. Metadata prevents the mistake.
We Need a Smarter Way to Keep Metadata in Shape
The challenge is that metadata was never designed for speed. Creating it takes time. Maintaining it takes discipline. And historically, it’s been hard to justify that investment, since the cost of bad metadata didn’t show up right away.
This urgency isn’t just about performance. It’s also about protection. As more teams adopt AI, compliance and privacy increasingly depend on metadata — not just for audits, but to define ownership, tag sensitive fields, and enforce access controls in real time.
AI is raising the stakes. But it’s also giving us new ways to solve the problem.
How Elementary’s AI Agents Help
This is where Elementary’s agents come in.
- The Governance Agent identifies and fills metadata gaps based on your usage and policies. It creates missing descriptions, suggests tags, and assigns owners automatically.
- The Catalog Agent makes that metadata usable. It helps humans and AI tools search and select the right assets with full context — not just names, but health, ownership, and trustworthiness.
They don’t just patch things up. They help you build a system that scales and keeps up.
The Next Era of AI Needs Metadata That Works
You don’t have to take our word for it. In just the past six months, metadata and governance platforms have been at the center of major acquisitions: Salesforce acquiring Informatica, ServiceNow picking up Data.World, and Cloudera buying Octopai. These aren’t just product expansions. they're strategic bets on metadata as the foundation for enterprise AI.
We talk a lot about scaling AI, building copilots, embedding agents. But none of that works reliably without solid foundations, and the culture to maintain them.
If you want AI to power how your business thinks and acts, invest in metadata that's accurate, operational, and alive.
Start with what's been ignored:
- Treat ownership as non-negotiable.
- Treat metadata gaps as reliability risks.
- Treat tags and descriptions as part of the interface AI uses to make decisions.
Because it already is.
