Building a Robust Data Platform on the Road to Self-Service Analytics

At Malt, our central data team is at the core of stakeholder data requests, often becoming a bottleneck. Enabling self-service is crucial to removing this bottleneck, and scaling by providing the right tools for the right personas and usage.

To achieve this, we built strong and clean foundations in our data warehouse, organized in layers with a clean exposition layer. This requires collaboration between data engineers and analytics engineers.

We approached self-service by addressing different personas and needs:

- A self-service layer for business users in Looker through a generative AI app.

- Tools for the data team to unlock ad-hoc analysis and sharing.

- A self-service layer for data users via a dedicated AI assistant to help navigate the data warehouse.

The objective is to share our journey and our challenges. We'll detail how we tackled challenges from and organization perspective, and the tools we implemented. This should give keys and idea for anyone moving toward the self-service road.