# Building a BI Dashboard That's Actually Useful

You opened your BI tool, dragged in some charts, and shipped a dashboard. A month later, nobody's looking at it - except to screenshot a number that goes up and to the right for a slide. The data is correct, the charts are pretty, and it changes exactly zero decisions. It's wall art.

This guide is about the other kind of dashboard: the one a real person opens on a Monday because it tells them something they need to *act* on. The trick is not better charts. It's working backward from a decision someone actually makes, choosing metrics that move that decision, and laying it out so the answer hits the eye first.

We'll stay tool-agnostic - everything here applies whether you're in Metabase, Looker, Power BI, or a spreadsheet - because the thinking is what's missing, not the software.

## How to read this
- **Need to fix a dashboard nobody uses right now?** Jump to [Phase 3: Designing One People Actually Use](03-designing-one-people-use.md) and start with the trap checklist near the top.
- **Want it to finally make sense?** Read in order - each phase builds on the last. We start with what BI even *is*, then which metrics earn a place, then how to lay them out.

## The phases
1. **[What BI Actually Is](01-what-bi-actually-is.md)** - business intelligence is helping people make decisions with data. A dashboard is an answer to a recurring question. Start from the decision and work backward to the metric.
2. **[Metrics That Inform vs Vanity Metrics](02-metrics-that-inform.md)** - a useful metric changes a decision; a vanity metric only feels good. The right aggregation, the right denominator, and the context (comparison, target, trend) that makes a number mean something.
3. **[Designing One People Actually Use](03-designing-one-people-use.md)** - layout for the eye, the right chart for the question, and ⚠️ the traps that quietly mislead: bad axes, wrong aggregation, no context, and the dashboard with no owner.

> Deeper material - where the numbers *come from* (modeling, joins, the warehouse) and how to write the queries behind each tile - lives in its own guides. See [Warehouses vs Lakes](/guides/warehouses-vs-lakes) and [Querying Basics: SELECT and WHERE](/guides/querying-basics-select-where). This guide is about turning data you already have into decisions.