Case Study: How Barbora Got One Version of the Truth and Cut Marketing Data Warehouse Costs by About 75%

Barbora is one of the largest e-commerce businesses in the Baltics. Like many fast-growing retailers, it had plenty of data and plenty of dashboards, but its marketing numbers rarely agreed with each other. We helped Barbora build one trusted data foundation on the Reflective Data platform, and then put AI on top of it.

At a glance

• Client: Barbora, a large e-commerce business in the Baltics
• What we built: a dbt project and semantic layer on our platform, interactive dashboards in Explore and a custom MCP server
• Result: metrics that finally match across sources, about 75% lower data warehousing costs in the marketing department, and AI access to the same trusted data in Slack and Claude

The challenge

For a long time, the numbers coming from different sources did not match. The same question about marketing performance could produce different answers depending on which report, tool or query you used. This is a familiar problem in e-commerce, where the data comes from many places and every platform defines its metrics a little differently.

The effects were practical. Marketing and leadership spent time debating which number was right instead of deciding what to do next. Analysts spent hours reconciling reports. And with every new reporting need, queries against the warehouse grew, along with the bill.

Barbora also wanted to use AI to get answers faster. But pointing AI at inconsistent, unmodelled data would only have produced faster confusion. The foundation had to come first.

The solution

We built the solution in three steps, all on the Reflective Data platform.

1. A dbt project and semantic layer

We built Barbora’s dbt project on our hosted dbt, so every metric and dimension is defined once, in code, tested and version-controlled. A semantic layer sits on top, so a number means the same thing wherever it appears. The models also produce compact, aggregated reporting tables, so reports no longer need to scan large volumes of raw data.

2. Interactive dashboards in Explore

On top of that layer we built a set of interactive dashboards using Explore. Teams can look at the same governed metrics themselves, filter and slice them, and no longer depend on a one-off report for every question.

3. An MCP server for their data

We then built an MCP server that gives AI tools access to the same governed data through the semantic layer. Barbora’s people now use it directly in Slack and in Claude, and it also powers their weekly stakeholder emails. The answer in Slack, the number in a dashboard and the figure in the email all come from the same definitions.

The results

Metrics that finally match

The biggest win was one that had been missing for years: the numbers from different sources now match. Barbora’s marketing team and leadership were delighted to see it. Discussions moved from “which number is right?” to “what should we do about it?”.

About 75% lower warehousing costs in marketing

Because reports and queries now read modelled, aggregated tables instead of raw data, they are far more efficient. Barbora saved about 75% on data warehousing costs in the marketing department.

AI where the team already works

With a trusted data layer in place, AI became useful instead of risky. People get answers in Slack and Claude, and stakeholders receive weekly emails powered by the same governed data, without anyone assembling them by hand.

Why it worked

• Foundation first. We fixed the definitions and the models before adding dashboards or AI, so everything built on top inherited the same logic.
• One layer for every tool. Dashboards, Slack, Claude and email all read from the semantic layer, so there is only one version of each number.
• Efficient by design. Reporting tables mean queries touch far less data, which is better for speed and for cost.

Want to know more about the approach? Read why a semantic layer is essential for giving AI access to your data.

Get the same for your business

If your numbers do not match across tools, or you want AI you can trust on your data, we can help. Request a demo of the platform, or talk to one of our experts about our data build and semantic layer and AI services.