How I build a dashboard executives actually open

I’ve seen more beautifully engineered dashboards die of neglect than for any technical reason. They sit bookmarked but unopened, ghost towns of analytics. The mistake isn't the tech; it's the premise. We build what we can show, not what a leader—or increasingly, an automated agent—needs to act on.

My process flips this around. It’s less about the BI tool and more about a series of focused conversations to build a tool so tight a decision-maker can't ignore it. The goal is to drive the next action, whether that action is taken by a human or a machine.

Start With the Decision, Not the Data

My first meeting about a new dashboard never involves a schema diagram. I have one goal: find the decision being made in the dark. I'll ask, "What action do you need to take next Monday that feels like a guess right now?" This moves the conversation from "let's plot our data" to "let's build a diagnostic tool."

The key is to avoid cognitive overload. A focused dashboard that answers one critical question is infinitely more valuable than a sprawling one that answers none. We must deliver a clear signal first. We can earn the right to add more later.

The First Prototype Is One Slide

I never start in a BI tool. It locks in assumptions and makes iteration painfully slow. My first deliverable is a wireframe in a presentation tool—a single slide with simple shapes and fake, but realistic, numbers. This is a direct application of the Minimum Viable Product thinking from Eric Ries's The Lean Startup; it's about maximizing learning with minimal effort.

This makes feedback cheap. Anyone can critique a box on a slide; they are hesitant to question something they know took an engineer two weeks to build. We can iterate five times in thirty minutes. Once the stakeholder says, "Yes, if you give me this with real numbers, I will use it to decide X," that slide becomes our contract. Only then do I think about architecture.

Build the Most Boring Pipeline Possible

With an approved design, the temptation is to over-engineer the backend. For a v1 dashboard, this is usually a mistake. My principle is to build the most boring and reliable pipeline that works. Often, it's a nightly SQL script materializing a single table. It’s not fancy, but it’s cheap, debuggable at 3am, and easy to throw away.

This isn't always the right answer, of course. For operational systems monitoring real-time fraud or the health of a production AI agent, a simple batch pipeline is the wrong tool. But the latency of most strategic business decisions is measured in days, not milliseconds. Match the architecture to the decision loop.

Monitoring the New Workforce: AI Agents

The dashboards I build today serve a new audience: the autonomous agents and the architects who manage them. When your workforce is a fleet of LLM agents, the core questions change. The executive dashboard still tracks revenue, but the architect's dashboard tracks the agent fleet's operational health.

The metrics look different. We track things like:

• Token Burn Rate: The direct cost of operations.
• Task Completion Latency: How fast agents are solving problems.
• Tool-Use Failure Rate: How often agents fail to use their tools correctly.
• Hallucination & Grounding Score: The quality and reliability of agent output.

These are the new KPIs. They are leading indicators for the business metrics an executive tracks. An agent fleet with a high failure rate will eventually impact customer satisfaction. The architecture must surface both views from a unified data backend.

The Trap of a Single Health Score

A common request is to distill everything into one "health score." This is an understandable desire for simplicity, but it's dangerous. It falls prey to what economist Charles Goodhart observed, now known as Goodhart's Law: "When a measure becomes a target, it ceases to be a good measure."

Chasing a single green number encourages people to game the system at the expense of true health. Is user growth green because we're acquiring low-value users and burning cash? The score won't say. I always pair a primary metric with its counter-metrics. Weekly Active Users must be shown alongside Cost Per Acquisition and Week 1 Retention. This presents an honest, balanced view that leads to durable decisions.

Curate Ruthlessly or Die by Clutter

The work isn't done at launch. A project ends; a product evolves. I instrument dashboards to see what gets used. If a chart isn't used to make a decision for an entire quarter, I kill it. This ruthless curation is the only way to maintain focus.

This practice is a form of maximizing what the great data visualizer Edward Tufte called the "data-ink ratio"—the amount of ink on a page dedicated to showing data. By removing non-essential elements, we increase the clarity and impact of what remains. A dashboard's purpose is to provide clarity, and clarity comes from subtraction, not addition.

Concrete Takeaways

• Frame every dashboard around the specific decisions it must drive for its intended user, whether human or AI.
• Prototype the interface on a single slide with fake numbers to get fast, cheap feedback on logic and layout before writing any code.
• Start with the simplest, most reliable data pipeline that can work. Match architectural complexity to the latency of the decision being made.
• When monitoring AI agents, focus on operational metrics like cost, latency, and reliability as leading indicators for business outcomes.
• Never rely on a single health score. Pair every primary metric with counter-metrics to reveal the hidden trade-offs and tell an honest story.
• Aggressively remove any feature or metric that is not actively used to make decisions. A dashboard's value is in its focus.