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

At Malt, our central Data team is at the heart of stakeholder data requests, often becoming a bottleneck. Enabling self-service is crucial to eliminate this bottleneck and to scale by providing the right tools to the right people for the right use cases.

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

We have approached self-service by taking into account different profiles and needs:

- A self-service layer for business users in Looker via a generative AI application.

- Tools for the Data team to unlock ad hoc analyses and sharing.

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

The goal is to share our journey and our challenges. We will detail how we approached the challenges from an organizational perspective and the tools we implemented. This should provide keys and ideas for anyone heading towards self-service.