In all organizations of any size, data teams are striving to maximize their impact: data engineers by implementing efficient pipelines to make data usable, ML engineers and data scientists by using that clean data to enhance the organization's offering, and data analysts by creating reports and visualizations to generate insights and inform product decisions. Unfortunately, the questions from business stakeholders are infinite, resulting in many ad-hoc requests that often reduce the velocity and efficiency of data teams. How do you solve that and let the data focus on strategic goals that increase the value of their contribution? By empowering business users with the tools to look into the data and answering their own questions. We will look into how the combined power of dbt and Apache Superset can enable business teams with any level of familiarity with BI tools to look into datasets and generate insights on their own. We will first demonstrate how data engineers can create assets using dbt for analytics use cases, then how these assets can be imported into Superset to enable exploration, and lastly we will dive into the features of Superset that business stakeholders can leverage to answer their questions, resulting in an optimal usage of the data team's bandwidth. Over the course of this presentation, we will use as a case study a funnel analysis to understand the patient journey into a website offering medical assistance.