dashboard

A dashboard is read in 5 seconds by someone who will not scroll. If a busy reader cannot grasp business status that fast, the design failed — no matter how accurate the numbers are. Your job here is arrangement, framing, and chart choice, producing a checkable artifact:

• dashboard.yaml — a tile manifest (each tile: metric, chart, comparison, owner, refresh, decision).
• a one-screen layout sketch placing the north-star top-left.

You do not decide which metrics matter (that is kpi-framework) and you do not wire the data source (that is analytics). You take a metric set that already exists and make it readable at a glance.

Run scripts/verify.sh dashboard.yaml before you hand off. It checks shape and discipline — tile count 1-9, exactly one north_star: true, the required keys on every tile, no banned charts — never whether the numbers are correct.

The Decision Test gate

No tile exists until its metric names the action it drives. Ask: what does someone do differently based on this number? If the answer is vague ("good to know", "shows we're growing"), the metric is vanity — cut it or convert it. A tile that changes no decision is decoration.

Vanity metric (Bad)	Actionable conversion (Good)	Decision it drives
Total followers	Follower-growth rate WoW	Double down or change content cadence
Total page views	Conversion rate by source	Reallocate spend to the converting channel
Cumulative signups	Activation rate (signup → key action)	Fix onboarding if activation drops
Total revenue (all-time)	MRR + net revenue retention	Whether to hire / extend runway
App downloads	7-day retention	Kill or scale the acquisition channel

Rule: an always-up counter ("total X") is almost always vanity. Convert it to a rate or ratio that can go down. Why: a number that only ever rises never triggers an action.

Tile budget: 5-9, exactly one north-star

Executive dashboards carry roughly 5 to 9 KPIs. Human working memory caps near ~7 items; more tiles cause analysis paralysis and the screen stops being a glance.

• Exactly one tile flagged north_star: true. Why: a glance needs a single first read.
• 4-8 supporting tiles around it. Total ≤ 9.

When a stakeholder demands 20 metrics, do not cram them — tier:

• Does this metric drive a decision the leadership team makes weekly? → main screen.
• Is it a diagnostic you only check after a main tile turns red? → drill-down view, linked from the parent tile.
• Is it commentary / context that needs prose? → route to reporting, not a tile.
• Is it a projection of a metric forward? → forecasting, not the live screen.
• Does the user want to slice it freely by dimension? → business-intelligence, not a fixed tile.

Framing every tile

The minimum viable tile is not a number. It is a number plus the context that makes it actionable:

value + comparison(target | prior | benchmark) + delta + units + date range
+ RYG status + owner + refresh cadence

Bad → Good:

Bad:   Churn: 4.1%
Good:  Monthly logo churn — 4.1%  ▲0.6pp vs prior month  (target ≤3.0%)  🔴
       Owner: Head of CS · Refresh: daily · Range: May 2026

Why each field earns its place:

• comparison — a number alone has no "is this good?"; target/prior/benchmark supplies it.
• delta — direction and magnitude of change is the second thing the eye wants.
• units + date range — "4.1%" of what, over what window; ambiguity kills trust.
• RYG status — lets the reader triage in the 5-second glance without reading the number.
• owner — every red tile needs an accountable human, or nothing happens.
• refresh cadence — tells the reader how stale the number can be; a "real-time" label on a weekly metric is a lie.

Chart per metric: pick by shape

Choose the chart from the shape of the question, not from what looks impressive. Full matrix and edge cases in references/chart-selection.md.

Metric shape	Chart	Why
Single headline number	Big-number tile + delta	Fastest read; the north-star usually lives here
Trend over time	Line	Slope shows direction instantly
Category comparison	Sorted horizontal bar	Length is easy to compare; sorting answers "who's worst"
Target vs actual	Bullet chart	Compact, comparable; replaces gauges/dials
Part-to-whole (≤5 parts)	Stacked bar or treemap	Pie only legible to 3-5 slices
Correlation	Scatter	Shows relationship between two measures
Distribution	Histogram	Shape of spread, not just average

Kill-list: no pie beyond 5 categories (sorted bar instead) and no gauge / dial (bullet chart instead) — verify.sh rejects both; no dual-axis, which invents correlations that aren't there; no 3D and no donut-with-center-number gimmicks.

Layout & data-ink

Lay tiles out for the Z / F scan pattern: the eye starts top-left, sweeps right, drops down. Put the north-star top-left; group related KPIs together (acquisition cluster, revenue cluster, health cluster).

Apply the data-ink ratio: if a pixel shows no new information, remove it. Strip heavy borders, drop shadows, gradients, 3D, background textures — they raise cognitive load without adding meaning.

Color is meaning, not decoration. Use a status palette (red/yellow/green) only for status. A rainbow of tile colors with no semantic meaning makes the screen harder, not friendlier.

Before:  9 boxed-and-shadowed tiles, each a different bright color,
         3 pie charts, two gauges, no clear starting point.
After:   north-star top-left (big number + sparkline);
         flat tiles, hairline separators, neutral palette;
         RYG reserved for status; bullets replace gauges;
         sorted bar replaces the pies.

The artifact: dashboard.yaml

Emit a manifest a BI tool, a web app, or a human can build from. Short schema here; full field reference in references/tile-schema.md.

# dashboard.yaml — one screen, one north-star
title: SaaS Exec Dashboard
date_range: "2026-05"
tiles:
  - id: waa
    north_star: true
    metric: Weekly Active Accounts
    chart: big_number          # + sparkline trend
    value_source: warehouse.fct_active_accounts
    units: accounts
    comparison: { type: target, value: 1200 }
    delta: vs_prior_week
    status_logic: "green ≥ target; yellow ≥ 90%; red < 90%"
    owner: VP Product
    refresh: daily
    decision: "If below 90% of target two weeks running, escalate activation work."
  - id: nrr
    metric: Net Revenue Retention
    chart: big_number
    comparison: { type: benchmark, value: 110 }   # %
    units: percent
    owner: VP Sales
    refresh: monthly
    decision: "Below 100% triggers a save/expansion review."
  - id: churn
    metric: Monthly Logo Churn
    chart: line
    comparison: { type: target, value: 3.0 }
    units: percent
    owner: Head of CS
    refresh: daily
    decision: "Above target investigates cohort + reason codes."
  - id: runway
    metric: Cash Runway
    chart: bullet                      # actual vs target months
    comparison: { type: target, value: 18 }
    units: months
    owner: CFO
    refresh: monthly
    decision: "Under 12 months opens fundraise / cost review."
  - id: pipeline
    metric: New Pipeline by Source
    chart: sorted_bar
    comparison: { type: prior, value: last_month }
    units: currency
    owner: Head of Marketing
    refresh: weekly
    decision: "Shift spend toward the top-converting source."
layout:
  north_star: top_left
  scan: Z
  groups: [growth, revenue, health]

The keys verify.sh requires on every tile: metric, chart, comparison, owner, refresh, decision.

Anti-patterns

Anti-pattern	Why it fails	Do instead
20 tiles on one screen	Past ~7, no one can read it in 5 seconds	Cut to 5-9; tier the rest to a drill-down
Vanity metrics (totals, followers)	Never trigger an action	Apply the Decision Test; convert to rates
Bare numbers, no comparison	No "is this good?" signal	Add target / prior / benchmark + delta
Pie chart with 8 slices	Slices become unreadable	Sorted horizontal bar
Gauges and dials	Waste space, resist comparison	Bullet chart
Rainbow tile colors	Color reads as meaning when it isn't	Neutral tiles; RYG for status only
"Real-time" on everything	False precision; refresh-cost theater	Match refresh to decision cadence
It's actually a report	Prose + commentary isn't a glance	Move narrative to reporting
No north-star	Reader has no entry point	One north_star: true tile, top-left

Handoff

Beyond the routes named above: reading out an experiment or variant significance → ab-testing; visual polish, spacing and type scale of the rendered screen → design; pulling or joining the underlying numbers in a sheet → spreadsheet-ops.