Getting the entire organization to be truly data-driven is challenging, and one of the key enablers for that is self-serve BI. But what does self-serve BI actually mean, and what are the principles you need to follow? Let’s break it down.

At its core, the goal of self-serve BI is to enable data-driven decisions by providing easy access to data and intuitive visualizations, without relying on the centralized BI team for every ad-hoc request. Easier said than done, so here are some practical guidelines:

1. Differentiate Between Operational Intelligence and Business Intelligence

Operational intelligence is typically owned by teams focused on optimizing their own processes and resource use—whether it’s sales teams analyzing funnels or marketing reviewing campaign performance. These teams often rely on third-party tools like Salesforce or HubSpot and sometimes require custom back-office systems to track their operations.

Business Intelligence, on the other hand, integrates data from various operational systems and applies business logic to inform company-wide performance, decision-making, and strategy. These datasets and logic should be centralized and usually owned by the BI team.

2. BI as a Platform Team

Like platform teams in software engineering, BI should own and provide the core data infrastructure. That includes:

• Data lakes / warehouses and certified datasets for business analysis.
• ETL pipelines, data monitoring, and orchestration tools.
• Services like mentorship, governance, and standards enforcement.

3. Invest in Data Modeling

A strong data model is essential for self-serve BI. Star schema makes it easy to separate dimensions, facts, and aggregations, simplifying joins. For less technical users, consider flattening data into wide views so they can work with fewer datasets and avoid complex joins.

4. Invest in a Data Catalog

Make it simple for users to find the right data. A data catalog helps people understand what datasets are available and how to use them effectively.

5. Decentralization is Key

BI teams should establish the guidelines, processes, and controls (e.g., what business logic should be centralized, how changes to datasets are managed). But the goal is to empower teams to operate independently on BI’s infrastructure and standards, not micromanage every aspect.

6. Self-Serve Doesn’t Mean Chaos

Think of it like managing code: you need to define what belongs in shared libraries or services, who the contributors are, who can approve changes, and what’s private code or sandboxed. Apply the same logic to BI—define certified tools, datasets, standards, and identify agents in other teams to help enforce these guidelines.

7. SQL is a Core Skill

Basic SQL is essential for self-serve BI. Many problems users think require complex UIs can be solved with simple SQL queries. While I believe GenAI will make data access even easier and more personalized, SQL remains a critical skill for any data-driven organization.