Data Instrumentation & Telemetry Plan for Discovery

Purpose

This template helps teams design instrumentation that turns experiments and prototypes into reliable, interpretable signals for decision-making. Use it to map metrics to hypotheses, define required events and properties, set tagging and retention rules, assign ownership, and run a QA checklist before launch.

How to use this template

1. Start by writing the experiment hypothesis and the decision you'll make if the signal is positive, negative, or unclear.
2. Map one or more primary metrics to that hypothesis and list supporting guardrail and quality metrics.
3. Define the concrete events/properties required to compute those metrics, then instrument and QA them before launching.

1. Map metrics to hypotheses

Every metric should have a clear job: what decision does it support? Capture the hypothesis, the success criterion, and the metrics that provide the signal.

2. Required events & properties

List the events and properties you must capture to compute your metrics. Be explicit about types, formats, and cardinality.

Example event schema (illustrative)

{
  "event_name": "signup_complete",
  "user_id": "12345",
  "anon_id": "abcde-789",
  "experiment_id": "exp_homepage_primary_cta",
  "variant": "B",
  "timestamp": "2026-08-25T10:05:00Z",
  "context": {"page": "homepage", "platform": "web"},
  "properties": {"plan_type": "free", "referrer": "email_campaign"}
}

3. Tagging and naming conventions

Consistent names make reuse and cross-experiment analysis possible. Keep names short, hierarchical, and stable.

• Use prefixes or namespaces: experiment., product., system.
• Event names: use snake_case or kebab-case and prefer verbs for events (e.g., signup_complete, checkout_started).
• Properties: use clear nouns (e.g., plan_type, experiment_id, variant).
• Version events and properties when the schema changes (e.g., event_v2) rather than silently repurposing fields.
• Record enumeration values centrally (allowed variants, plan types) and update the data catalog when values change.

4. Retention, privacy & security

• Identify and remove or pseudonymize PII. Do not store raw sensitive identifiers unless required; use hashed or tokenized identifiers where possible.
• Annotate events with consent flags (e.g., consent_tracking:true) and honor them in downstream processing.
• Set retention windows appropriate to the experiment: short tests may keep raw events for weeks; product signals that inform long-term decisions may require longer retention but stricter access controls.
• Document deletion processes for user deletion requests and test data cleanup rules.

5. Monitoring & alerts

Create automated checks that surface instrumentation and data-quality issues quickly.

• Volume checks: alert if expected event volumes drop >30% or variant assignment skew exceeds planned bounds.
• Freshness checks: alert if data ingestion lag exceeds X minutes/hours.
• Schema checks: alert on missing required properties or unexpected types.
• Statistical sanity checks: alert for extreme or impossible values (e.g., negative revenue).
• Ownership & runbooks: for each alert define an owner, on-call rota, severity, and a short runbook describing triage steps.

6. Dashboards, ownership & SLAs

Identify the canonical dashboard and its owner. A dashboard is only useful if someone keeps it accurate and answers questions that arise.

• Dashboard owner: person or team responsible for metric definitions and updates.
• Data steward: owner of the raw instrumentation and pipeline (often engineering or telemetry team).
• Update cadence & SLA: who updates the dashboard when schema or experiment definition changes, and within what timeframe.
• Link dashboards to the experiment plan and to metric definitions in the data catalog.

7. Pre-launch QA checklist

Run these checks for every experiment or prototype before full launch. Consider turning this checklist into an interactive form so results are recorded.

1. Hypothesis and decision rule documented and reviewed.
2. Primary and guardrail metrics specified and computable from defined events/properties.
3. Events fire end-to-end in staging and production for test users.
4. Experiment assignment recorded reliably in events (experiment_id and variant present).
5. Sampling and exposure logic verified for expected distribution across variants.
6. Data pipeline ingestion validated (no schema errors or high-latency jobs).
7. Pre-launch smoke dashboard shows expected baseline numbers for control/test.
8. Alerting configured for volume, freshness, and schema issues; owners assigned.
9. Privacy and retention rules reviewed and documented; PII handled according to policy.
10. Rollout and rollback plan documented, including safe cutover and monitoring window.

8. Examples for common experiments

9. Instrumentation patterns & best practices

• Make experiment assignment deterministic and record it on every relevant event.
• Emit idempotent events (include event_id) to simplify deduplication.
• Prefer simple, well-defined derived metrics in the analytics layer rather than trying to compute complex ratios on the fly from inconsistent raw events.
• Keep a single source of truth for experiment metadata (experiment_id, hypothesis, start/end dates) and reference it in processing pipelines and dashboards.

10. Glossary (short)

• Primary metric: the main signal used to accept/reject a hypothesis.
• Guardrail metric: a safety or quality metric to ensure improvements aren’t harmful.
• Event-level metric: counts or properties tied to discrete events (e.g., clicks).
• User-level metric: aggregated per user (e.g., retention, monthly active users).
• Vanity metric: a number that looks good but doesn’t inform decisions.

Closing notes

This template is intentionally practical: document decisions, instrument deliberately, test thoroughly, and assign owners. Consider packaging this plan with a reusable checklist, an interactive QA form, and a canonical experiment metadata store so teams can copy and adapt the structure across domains.