# Empowering Data Through Self-Service: Behind the Scenes of Our Data Platform

DevFeed: [Empowering Data Through Self-Service: Behind the Scenes of Our Data Platform](<https://devfeed.tech/articles/empowering-data-through-self-service-behind-the-scenes-of-our-data-platform-30789.md>)

Original publisher: [Read original article](<https://devblog.kogan.com/blog/empowering-data-through-self-service-behind-the-scenes-of-our-data-platform>)

Author: Karen Fehmer

Published: 2025-06-02T03:53:58Z

Content type: article

Language: en

Sources: [Kogan.com](<https://devfeed.tech/sources/kogan-com.md>)

Topics: [data](<https://devfeed.tech/topics/data.md>), [Architecture & Design](<https://devfeed.tech/topics/architecture-design.md>), [BigQuery](<https://devfeed.tech/topics/bigquery.md>), [CI/CD](<https://devfeed.tech/topics/cicd.md>), [GitHub Actions](<https://devfeed.tech/topics/github-actions.md>), [Git](<https://devfeed.tech/topics/git.md>)

Tags: [architecture](<https://devfeed.tech/tags/architecture.md>), [bigquery](<https://devfeed.tech/tags/bigquery.md>), [ci-cd](<https://devfeed.tech/tags/ci-cd.md>), [data](<https://devfeed.tech/tags/data.md>), [dbt](<https://devfeed.tech/tags/dbt.md>), [github-actions](<https://devfeed.tech/tags/github-actions.md>), [looker](<https://devfeed.tech/tags/looker.md>), [self-service](<https://devfeed.tech/tags/self-service.md>), [tableau](<https://devfeed.tech/tags/tableau.md>)

## AI overview

Kogan.com describes replacing a request-based BI model with a governed self-service data platform built around BigQuery, dbt, Looker, and Acryl (DataHub). The architecture uses layered models, GitHub Actions CI/CD, testing, documentation, and controlled environments to support trusted analysis and reporting.

## Source excerpt

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