At Kogan.com, our data needs have grown alongside the business. As more teams relied on insights to move quickly, it became clear our request-based BI model couldn’t scale. We needed a platform that empowered teams to answer their own questions, trust the numbers, and move independently. That journey led us to build a self-service platform grounded in governance, transparency, and scalability—powered by dbt, Looker, and Acryl (DataHub).
Rethinking Our BI Model
We originally relied on Tableau. It served us well but had limitations: duplicated logic, inconsistent metrics, and limited collaboration with dbt. Tableau workbooks weren’t version-controlled, which made maintaining consistency difficult. To bridge modeling and reporting, we often created extra presentation tables in dbt, adding complexity. We needed a platform that integrated tightly with dbt and supported governed exploration.
A New Architecture: Modular, Transparent, Scalable
We redesigned the platform around a clean, modular flow: Raw Sources → BigQuery → dbt → Looker → Acryl (DataHub) Our data transformations are built in dbt, where we follow a layered modeling structure. While we use stg_ (staging) and int_ (intermediate) models primarily for data cleaning and standardization, the marts_ models are the ones that power our analysis and reporting. These models contain our fact and dimension tables, fully aligned with business logic and ready for consumption in Looker. We’ve integrated CI/CD pipelines using GitHub Actions, and every change is tested before deployment. This includes dbt tests, schema validations, and model documentation to ensure confidence at every layer.
Why Looker Was the Right Fit for Self-Service
Looker offered a structured, governed approach that aligned with our dbt-first architecture. LookML let us centralize business logic, version it with Git, and deploy changes through CI/CD. With support for multiple environments (UAT and Production), we can test safely before releasing to users. The Explore interface gives business users guided access to curated datasets—no SQL required. Users can drill down, apply filters, and explore confidently. This was a big shift from the Tableau model, which often required analyst support. Looker also includes row-level security, role-based access, and an AI assistant that supports natural language queries and chart generation—lowering the barrier for non-technical users. We’ve also developed internal dashboard standards—consistent layouts, filters, and naming conventions—to ensure usability and reduce support needs.
Bridging the Migration
We’re currently in the process of migrating our Tableau reports into Looker. While Looker is more efficient to build with, the migration isn’t just about re-creating dashboards—we’re using it as an opportunity to improve them. For each report we migrate, we review and sometimes refactor the associated dbt models to ensure the logic is clean, reusable, and well-documented. We also take time to redesign visual layouts to be more intuitive and self-service friendly—adding better filters, descriptive labels, and drill-down paths wherever we can. It’s not just a tech migration—it’s a platform and user experience upgrade.
Data Discovery and Observability with Acryl
Alongside dbt and Looker, Acryl (DataHub) has become the foundation of our metadata ecosystem. Acryl helps both technical and non-technical users understand what data exists, where it comes from, and who owns it. It provides searchable documentation, field descriptions, ownership metadata, and lineage tracing across dbt, BigQuery, and Looker. We also rely on Acryl’s observability features for monitoring anomalies and surfacing potential data quality issues. While it's not a test framework like dbt or a freshness tracker, Acryl helps us detect behavioral anomalies, unexpected changes, and broken relationships before they impact end users. Acryl's AI-powered documentation suggestions have also saved us time when onboarding new models or enhancing existing ones, especially for adding descriptions and tags at scale.
Lessons We’ve Learned
If there’s one takeaway, it’s that data tools need infrastructure—both technical and human. You can’t just launch Looker and expect adoption. You need a warehouse that reflects the business, models that users can trust, documentation that’s visible, and governance that feels supportive, not restrictive. We also learned that governance isn’t about locking things down—it’s about making things clear. When users understand what data means, how it’s calculated, and who owns it, they feel empowered, not limited.
Final Thoughts
We didn’t build a self-service platform just to save time—we built it to build trust. By aligning tools like dbt, Looker, and Acryl into a unified ecosystem, we’ve created something bigger than a data stack. We’ve created a culture where teams are empowered to explore, ask better questions, and make faster decisions—without sacrificing governance or quality. This transformation didn’t happen in a vacuum. It was made possible by the incredible efforts of my team—engineering, analytics, and enablement working hand-in-hand. The commitment to transparency, maintainability, and user empowerment is what brought this platform to life. We’re still learning. But we’re proud of how far we’ve come—and even more excited about where we’re going.
