metrics-dashboard

name	metrics-dashboard
description	Design a product metrics dashboard — North Star + input metrics + guardrails — that the team actually uses to make decisions. Distinct from north-star-metric (defines THE one number) by focusing on the full dashboard architecture: layers, owners, cadence, visualization, and the discipline that prevents 30-chart dashboards.
license	MIT + Commons Clause
metadata	{"version":"1.0.0","author":"borghei","category":"project-management","domain":"product-discovery","updated":"2026-05-27T00:00:00.000Z","python-tools":"dashboard_designer.py","tech-stack":"metrics, dashboard, north-star, hexagonal-metrics"}

Metrics Dashboard

A dashboard architecture skill: which metrics go where, at which cadence, for which audience, with which visualization. Focused on producing the ONE artifact a team uses to make decisions — not the 30-chart dashboard nobody opens.

When to use this skill

• New product / feature launch — what to instrument and watch
• Existing dashboard audit — what to cut, add, refactor
• Team-level OKR tracking — operational dashboard for the team
• Exec readouts — board / monthly business review dashboard
• Cross-functional alignment — what does "success" look like?

The 4 dashboard layers

1. North Star — 1 metric that summarizes value delivered
2. Input metrics (3-5) — the drivers of NS
3. Guardrails (3-5) — what we DON'T want to sacrifice (counter-metrics)
4. Operational metrics (4-8 per team) — what we actually act on weekly

A dashboard ≠ all metrics. A dashboard = these 11-22 metrics presented for fast decision-making.

Workflow

Step 1 — Confirm the North Star

Already defined? Use it. Not defined? See project-management/execution/north-star-metric.

A good NS:

• Behavioral or business
• Moves week-over-week
• Hard to game without delivering real value
• One number

Step 2 — Decompose to input metrics

For each NS, identify 3-5 inputs whose combined movement drives it.

Example for NS "Weekly Active Companies × Messages Sent per Company":

• Acquisition rate
• Activation rate (% reaching 50 messages in 14 days)
• Retention rate (W4 cohort)
• Expansion (adds users / channels)

Step 3 — Identify guardrails

What could move the NS up while damaging the underlying value?

Example guardrails:

• Spam rate (if NS = messages, more messages can include spam)
• User-reported complaints
• Power-user churn (vs total churn)
• Support ticket volume
• Latency / error rate

Step 4 — Identify operational metrics per team

The 4-8 metrics each team needs to act weekly:

• Growth team: funnel conversion, channel CAC, signup quality
• Retention team: cohort retention, save-room saves
• Platform team: SLO posture, on-call health, deploy freq
• Trust & safety: spam reports, removed accounts, false-positive rate

Step 5 — Define visualization + cadence per metric

Each metric needs:

• Visualization: line chart / funnel / cohort heatmap / bar
• Comparison: vs prior period / vs target / vs cohort baseline
• Refresh cadence: real-time / hourly / daily / weekly / monthly
• Owner: named team

Step 6 — Run dashboard_designer.py

Audit: too many top-level metrics, no guardrails, vanity metrics, missing owners, missing comparisons.

python3 project-management/discovery/metrics-dashboard/scripts/dashboard_designer.py \
  --input dashboard_spec.json --format markdown

Step 7 — Sunset stale metrics

Quarterly: kill metrics no team looked at. Dashboards rot; pruning is healthy.

Decision frameworks

Top-level metric count

Audience	Max top-level	Why
Board / exec	5-8	Limited attention; high signal/noise
Functional team	4-8	Actionable; weekly review
All-hands	3-5	Communicable; team rallies
Individual contributor	1-3	Their direct impact

Visualization fit

Question	Best visualization
Is it changing over time?	Line chart
How much vs target?	Gauge / bullet
Drop-off at each step?	Funnel
Retention over time?	Cohort heatmap
Distribution?	Histogram
Composition?	Stacked area / pie (rare)
Comparison across groups?	Grouped bar
Relationship?	Scatter

Avoid pie charts beyond 3 slices. Avoid 3D charts always.

Vanity vs actionable test

For each candidate metric: "If this moved up 10% next week, what would we do?"

• Have answer → actionable; keep
• No answer → vanity; cut

Comparison discipline

Every chart needs a comparison anchor:

• vs prior period (week / month / quarter)
• vs target
• vs cohort baseline
• vs competitor benchmark (rare; usually unreliable)

A chart with no comparison is a number floating in space.

Common engagements

"Build us a dashboard for the new product line"

1. Confirm North Star.
2. Decompose to 3-5 inputs.
3. Identify 3-5 guardrails.
4. Per team: 4-8 operational metrics.
5. Spec viz + cadence + owner per metric.
6. Pilot for 4 weeks; cut what nobody opens.

"Audit our existing dashboard"

1. List every metric currently shown.
2. Tag each: NS / input / guardrail / operational / vanity.
3. Cut all vanity.
4. Cut operational that no team looks at.
5. Add missing guardrails.
6. Limit each audience to its max.

"Help us track an OKR"

1. Map OKR to metric: KR → metric.
2. KR should be the metric.
3. Inputs = what moves the KR.
4. Guardrails = what we won't sacrifice.

Anti-patterns to avoid

• 30+ metrics on one screen. Decision-making dies.
• No guardrails. NS optimization without counter-balance.
• All metrics for all audiences. Exec doesn't need eng team metrics.
• No comparisons. Numbers without context.
• Real-time everything. Most metrics don't need it (and it's expensive).
• No owner per metric. Orphan metrics rot.
• Vanity metrics (page views, signups alone). Not action-driving.
• No cadence on review. Dashboard exists; team doesn't use it.

References

• references/dashboard-architecture.md — layers, cadence, visualization patterns
• references/dashboard-anti-patterns.md — common failures + fixes

Related skills

• project-management/execution/north-star-metric — define THE one number
• product-team/product-analytics — metric tree + cohort + funnel
• product-team/ab-test-setup — experimentation
• c-level-advisor/chief-data-officer-advisor — platform context