Telemetry
Dashboard example

Account activation funnel dashboard

Measure durable account milestones in order and make the denominator explicit at every funnel step.

Reviewed by the Telemetry product team on . Decision, event grain, metrics, DataFusion SQL, synthetic result, assumptions, freshness, and interpretation boundary. Review standards and ownership

Row grain

One row per account with first or achieved milestone state.

Decision supported

Select one transition and cohort for qualitative and instrumentation review.

Metric contract

Put the denominator and units beside the chart

Adapt these names to your contract while preserving the declared grain. Inspect the linked event schema before mapping fields from production producers.

  • Signed-up accounts
  • Sources connected
  • First queries run
  • Dashboards created
Inspect the event schema
Complete DataFusion SQL

Review the query before adapting the fields

This public query is a starting definition. Add a bounded time filter, complete-bucket policy, environment, and minimum volume appropriate to your production event contract.

WITH milestones AS (
  SELECT
    account_id,
    MAX(CASE WHEN event_name = 'source_connected'
      AND status = 'success' THEN 1 ELSE 0 END) AS connected,
    MAX(CASE WHEN event_name = 'query_completed'
      AND status = 'success' THEN 1 ELSE 0 END) AS queried,
    MAX(CASE WHEN event_name = 'dashboard_created'
      AND status = 'success' THEN 1 ELSE 0 END) AS dashboarded
  FROM product_events
  GROUP BY account_id
)
SELECT 'signed_up' AS step, COUNT(*) AS accounts FROM accounts
UNION ALL
SELECT 'source_connected', SUM(connected) FROM milestones
UNION ALL
SELECT 'query_completed', SUM(queried) FROM milestones
UNION ALL
SELECT 'dashboard_created', SUM(dashboarded) FROM milestones;
Data assumptions

Prove these before publishing the result

  • Every milestone represents committed product state, not a UI click.
  • The same pseudonymous account identifier is used at every step.
  • Signup cohort and observation window are fixed before comparison.
Query review

Checks that keep the dashboard trustworthy

  • Use one account-level row before counting funnel steps.
  • Make each step denominator explicit when calculating conversion.
  • Segment by cohort before attributing a drop to onboarding changes.

Interpretation boundary

What this result cannot prove by itself

A funnel describes observed milestones, not why accounts stopped. Pair it with qualitative research and segment by cohort before changing onboarding.

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Which accounts and operations were directly represented in a declared incident window?

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Validate the definition with known data

Run a fixture with known success, failure, missing, duplicate, and boundary cases before connecting the query to a production dashboard or alert.

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