Case Study 02
Building a Self-Serve Analytics Culture with Metabase: A Case Study for Startups Without a Data Team
Health tech · ~50 employees · 11 non-technical users now making decisions from data, without asking anyone for help
Results at a Glance
0 → 1
Metabase built and adopted from scratch
11
non-technical users actively using self-serve analytics
4 departments
onboarded: marketing, sales, operations, C-level
The Challenge: Data Existed. Nobody Could Use It.
After building a solid dbt data foundation for the client (read that case study here), the next problem was clear: the data was clean, modelled, and ready, but only one person in the company could actually get to it. The CEO was still relying on AI to query PostgreSQL directly. Everyone else had no way in at all. The goal was to change that. Not by hiring a data analyst to field requests, but by giving the whole team a tool they could use themselves.
How We Built a Self-Serve Analytics Layer with Metabase
The client had no Metabase instance before this engagement. We set it up from scratch, connecting it directly to the dbt models already in place so every dashboard and report was built on clean, documented, reliable data.
From the start, the setup was designed to be manageable without a dedicated data team. The client runs on Metabase's most basic plan, and departments share user accounts in a way that follows data governance best practices, keeping costs low without compromising access or accountability.
Once the tool was live, the real work began: getting 11 non-technical people across marketing, sales, operations, and C-level to actually use it. We ran three hands-on workshop sessions using the client's own data, not generic demos.
Each session followed the same structure. The first half covered the concepts: how Metabase works, what each feature does, how to think about data in a self-serve context. The second half was where it got practical. We took the team's real questions and built the answers together, live, inside their own data. Real dashboards, real filters, real outputs that matched how each department actually works. By the end of each session the team walked away with things they could use the next day, not just the knowledge of how to build them eventually.
Alongside the sessions, we built a full set of how-to documentation covering everything the team needed to operate independently: how to name reports, how to organise dashboards, how to share data across teams, and how to keep things consistent as the tool grew. These docs live in Notion, where the client already runs most of their internal knowledge base, and in Git READMEs tied to the data models themselves.
By the end, the CEO had shifted from querying PostgreSQL with AI to pulling up reports directly in Metabase. Every department lead had built their own dashboards. The team had the documentation to keep it running without outside help.
Results
- 11 non-technical users across 4 departments actively using Metabase to make daily decisions
- The CEO now leverages live reports instead of querying PostgreSQL directly
- Self-serve analytics working in practice, not just in theory: marketing, sales, operations, and C-level all operating independently inside the tool
- Full how-to documentation in Notion and Git so the team can onboard new members and maintain the setup without external support
- A lean, low-cost stack that requires no dedicated data person to maintain
“They structured our dbt, reduced platform costs, and left documentation so thorough our team kept building on it. No dependencies, no technical debts. More than a one-time delivery, it became the foundation for reliable metrics and data-driven decisions we're still evolving today. Professional, collaborative, and genuinely focused on long-term value.”
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