How To Build Governed Self-Service That Actually Scales

We’ve all heard the promise: “Self-service analytics for the whole business.” But let’s be honest—most BI tools overpromise and underdeliver. They talk about democratization, but then hand users clunky interfaces, half-baked dashboards, and a ticket queue longer than your last grocery run.

Business users can do analytics—they just haven’t had the right tools

It’s time to flip that. Business users can do analytics—they just haven’t had the right tools. That’s where data models come in. They’re not just backend infrastructure. They’re the blueprint for intuitive, governed, AI-powered exploration. And they’re changing how businesses interact with data. For good.

Stop expecting business users to think like data engineers

There’s this assumption that if someone’s not on the data team, they don’t really know how to do analytics. And that’s totally wrong. It’s not in their job description to understand all the intricacies of the table in a data warehouse and how it’s been architected. 

Self-service means giving non-technical teams the right interface to ask real questions and get real answers—without needing SQL or a data dictionary. That could be a drag-and-drop workbook with reusable metrics and dimensions, or a natural language chat with an LLM that understands your business logic.

Self-service means giving non-technical teams the right interface to ask real questions and get real answers—without needing SQL or a data dictionary.

The point is: If users have the right interface and the right context, they’ll get to insights faster. No tickets required.

Why is your data team still doing the work AI was made for?

There are two big truths in data right now: AI is changing how data teams operate, and the role of the data team is more strategic than ever. They’re expected to help lead the business—and lead the AI strategy. At Sigma, for example, our data team built a dashboard using Snowflake Cortex to analyze Gong transcripts and surface product insights from hundreds of customer calls to inform our roadmap. That’s the kind of work data teams should be doing.

But to get there, the manual work has to go. Ad hoc analysis, dashboard updates, list pulls—if it can be automated, it already should be. Otherwise, your team won’t have the bandwidth to deliver real value.

There are two big truths in data right now: AI is changing how data teams operate, and the role of the data team is more strategic than ever.

The good news? With strong semantics, thoughtful interfaces, and LLMs, self-service analytics is finally possible at scale. That’s been hard to deliver in the past, but the tech is here—and Sigma brings it together in a single platform. Your BI stack becomes a productivity engine, not just another tool to maintain.

Legacy BI is breaking under modern pressure

Let’s face it—traditional BI tools weren’t designed for how data is used today. They’re rigid, slow, and overly complex—even for data teams. And for business users? Forget it.

If you want to move fast, adapt to change, or build with AI, legacy BI is a dead end.

You need to be able to push updates without breaking downstream work. You need to empower users without walking them through three layers of abstraction. And you need to stop duct-taping tools together just to answer basic questions.

A modern, best-in-class analytics environment is built on three key pillars: 

1. Semantics: Define your logic once—metrics, relationships, dimensions—and make it reusable and consistent across your organization.
2. Interfaces for humans: Whether it’s a spreadsheet, SQL editor, or chatbot, the interface should work for your users.
3. AI that’s grounded in your logic: Not bolted on. Built-in, and deeply integrated with the semantics you’ve defined.

Put all three together, and you get governed self-service that actually scales. That’s the shift—from maintaining dashboards to building an ecosystem. From reactive BI to proactive, strategic analytics. And from legacy complexity to modern simplicity.

Data models are everything you’ve been waiting for: flexible yet governed

Once you have that solid foundation, data models are how you operationalize it.

They’re at the core of how Sigma makes governed self-service and AI-ready exploration possible. With data models, you define logic once—in one central place—and reuse it everywhere. Every workbook, every dashboard, every downstream analysis pulls from that same source of truth.

But governance doesn’t mean rigidity. Sigma’s data models are built for developers and flexible enough to adapt to how your team works. You can create version tags, validate changes, integrate with dbt, and manage everything in one central place. At the same time, business users can do lightweight modeling and create custom calculations in a workbook—still backed by the same trusted semantics.

You don’t have to choose between control and agility. You get both.

And when you define metrics inside those models, that’s when everything changes.

We call it metric-first exploration. Instead of starting with tables and hoping users figure out what to do, we start with metrics they already care about—pipeline, leads, and revenue.

From there, Sigma does the heavy lifting. It surfaces instant breakdowns—by region, channel, campaign, sales rep. It’s fast, intuitive, and grounded in the truth your team defined.

More importantly, it meets users where they are. They’re not thinking in terms of joins or dimensions. They’re thinking in terms of KPIs. So that’s where we start—and the insights follow naturally.

If your BI tool can’t scale, your strategy won’t either

If you’re leading a data team, your job isn’t to manage dashboards. It’s to drive business impact.

That means choosing a platform that helps you deliver insights faster, more consistently, and at scale. A platform that’s ready for AI. One that brings together semantics, modeling, and a familiar interface into a unified system. One that your business users will actually use.

That’s what Sigma is. And if you’re building your analytics ecosystem today, this is where you start.