Data Requirements in Your PRD: What Analytics Needs From You Before Build

A PRD's data requirements section must name the exact events, properties, and identifiers analytics needs before a single screen gets built — plus the success metric and guardrail metric that define whether the feature worked. Skip it, and you'll ship a feature with no way to prove, weeks later, whether it actually did anything.

Quick answer: Before build, a PRD's data requirements section needs a named event schema (what fires, when, with which properties), a success metric with a target, and a guardrail metric that catches regressions. Anything vaguer than that and "did it work?" becomes a guess.

What "Data Requirements" Actually Means in a PRD

A data requirements section specifies exactly what analytics must capture — event names, trigger conditions, properties, user identity, and the specific metrics that define success and failure — written precisely enough that an engineer could implement it without a single clarifying question. It is not the same document as your goals section.

A complete section covers, at minimum:

• Event name and trigger condition — what fires, and exactly when
• Properties — the attributes attached to the event for segmentation
• Identity strategy — anonymous vs. logged-in, and how sessions get stitched to a person
• Success metric — the number, and the numeric target, that defines "it worked"
• Guardrail metric — the number that must not get worse
• Owner and review cadence — who checks the number, and how often

Leave any one of these out and "we'll figure out the numbers later" quietly becomes the plan.

Most PRDs treat metrics as decoration near the top instead: a line like "success = higher engagement," sandwiched between the problem statement and the mockups. That's a goal, not a data requirement. A real data requirement names the event, the properties attached to it, who owns the resulting number, and the threshold that counts as pass or fail.

As our masterclass on writing PRDs for the AI era argues, a spec earns its value by making decisions falsifiable. A data requirements section is where that promise gets tested rather than just asserted — it's the difference between a PRD that reads well and one an engineer can actually build and measure against.

Why Skipping This Section Costs You the One Thing a Launch Needs

Skipping data requirements costs you the one thing a launch is supposed to produce: proof of whether it worked. Teams that treat analytics as a follow-up task routinely launch, discover mid-flight that a key event was never instrumented, and end up debating outcomes from incomplete logs instead of evidence.

This is the exact trap Eric Ries named in The Lean Startup: a vanity metric goes up regardless of what you do, while an actionable metric ties to a specific input you controlled. A PRD without data requirements defaults to vanity metrics, because they're usually already tracked. The actionable ones — did this cohort, exposed to this change, do this specific thing — are the ones nobody thought to instrument.

Web analytics veteran Avinash Kaushik has spent two decades warning against dashboards full of numbers nobody can act on. A data requirements section is where a PM decides, in advance, which numbers will actually change a decision — everything else is rigor-shaped noise.

Grounding that decision starts before you touch a metric name. Per our complete guide to Jobs-to-Be-Done, you can't know which behavior counts as "success" until you know the job the customer hired the feature to do. A saved-search feature hired to reduce anxious re-checking should be measured differently than one hired purely to save time.

Retrofitting analytics after launch typically costs you:

1. A launch window with no comparison data to look back on
2. Analysts reconstructing intent from logs never designed to answer the question
3. A second engineering cycle to add the events that should have shipped on day one
4. A PM defending a feature with anecdote instead of evidence, in the exact meeting where evidence mattered most

The Anatomy of a Tracking Plan: Events, Properties, and Identity

A tracking plan is the literal artifact analytics builds from: a table of every event, its trigger condition, the properties attached to it, and the identity model tying it to a user — organized so an engineer can implement it and an analyst can query it without guessing at intent.

The term itself comes from Segment (now part of Twilio), which popularized "tracking plan" as a named artifact specifically because so many implementations shipped with inconsistent event names across platforms — the same action logged as button_click on web and buttonTapped on mobile, un-joinable without a cleanup project nobody had budgeted for.

Six components recur across almost any tracking plan, whether the feature is a single button or a full workflow:

Written this specifically, an engineer can implement the event in an afternoon, and an analyst can answer "did it work?" without a follow-up meeting to reverse-engineer intent.

Not every metric category deserves equal weight, either. Google's HEART framework — Happiness, Engagement, Adoption, Retention, Task success, developed by UX researchers Kerry Rodden, Hilary Hutchinson, and Xin Fu — is a useful checklist for a data requirements section that over-indexes on Adoption (did people click it) while saying nothing about Task success (did it actually let them do the thing).

Anchor events to where the user actually is

Events mean little without context for where in the experience they happen. Mapping them against the emotion curve described in our guide to the customer journey keeps a tracking plan honest about stage. A drop-off event during onboarding tells a different story than the identical click on a power user's tenth session, even though the raw log looks the same.

AI features need their own data requirements

A feature built around a model's output can't be measured the same way as a button. Beyond adoption events, an AI feature's data requirements should specify what counts as a good output, how overrides or disagreement get logged, and what a "the model was wrong" signal looks like in the schema — the specifics of that structure are covered in our PRD template for AI features.

Prodinja's Spec Studio treats these as first-class fields inside an AI-feature spec. Anything framed as an automated quality judgment on that output — an evals pass, a context-critique — is presented as a simulated preview of what that review flow could feel like, never as a working model grading itself.

If the feature introduces new entities, like a saved alert or a subscription tier, Prodinja's Data Modelling tool can carry those from entity definitions to SQL DDL. That keeps the product schema and the tracking-plan schema using the same nouns instead of drifting apart.

From User Story to Event Schema: A Worked Walkthrough

Turning a user story into an event schema takes four steps: state the job the feature serves, name the single behavior that proves the job got done, define the event schema around that behavior, then set a numeric target and a guardrail before writing a line of code.

Apply it to a hypothetical "saved search alerts" feature in a marketplace product:

1. State the job. "As a frequent apartment hunter, I want to be notified when new listings match my saved search, so I can stop manually re-checking every day."
2. Name the proof behavior. Not "used the feature" — that's too vague to instrument or refute. The proof behavior is: created an alert and clicked through to a listing that alert surfaced within seven days.
3. Define the event schema around that behavior, end to end — creation, delivery, and engagement, not just the first click.
4. Set a target and a guardrail. A PM might set a target like "a meaningful share of alert-creators click through on an alerted listing within 30 days," with a guardrail that manual search volume among alert users doesn't collapse for the wrong reason — frustration rather than confident substitution.

Applied, the same components produce a schema an engineer can build against directly:

Notice what's missing: a generic feature_used event. That's the single most common tracking-plan mistake — one catch-all event confirms the feature was touched, not what happened, to whom, or whether it mattered.

Who Has to Sign Off Before a Single Line of Tracking Code Ships

Four roles need to review a data requirements section before build: the analytics engineer, who confirms the properties can actually be captured cleanly; privacy or legal counsel; design, for naming consistency; and an engineering lead, who scopes the cost of new events.

• Analytics engineer — confirms each property can be captured cleanly at the point specified, and flags anything that duplicates an existing event under a different name
• Privacy or legal counsel — confirms the lawful basis and retention window for any property touching personal data
• Design — confirms event and property names match the language already used in the UI, not a separate internal vocabulary
• Engineering lead — scopes the build cost of new events, especially backend-triggered ones like a notification job

Privacy review isn't optional paperwork. Under regulations like the EU's GDPR and California's CCPA, any property capturing personal data needs a documented lawful basis and a defined retention window. GDPR enforcement alone has produced fines reaching into the hundreds of millions of euros for data-handling violations since 2018 — a mislabeled analytics property is a mundane, entirely avoidable way to end up in that conversation.

Design alignment matters more than it sounds. If the UI calls the feature "Alert" but engineering's internal model calls it "Subscription," a PM who doesn't force the naming into agreement inherits a permanent translation tax every time someone reads a dashboard.

Not every metric earns the same scrutiny in that review. A metric your competitors already report and users already expect can usually piggyback on events you already track. A metric meant to prove out genuine differentiation needs its own dedicated event and a named owner checking it weekly — the same distinction our guide to specifying competitive parity versus differentiation draws for the features themselves, applied here to the metrics that prove those features out.

Specs Drift. A Living PRD Is How You Catch It.

Tracking plans rot the moment engineering renames an event, adds an undocumented property, or ships an experiment that quietly changes the funnel the PRD described — and a static document frozen at kickoff can't flag any of it. A living PRD that diffs the data requirements section is what catches drift while it's still cheap to fix.

Drift shows up in a few recurring shapes:

• An engineer renames an event to match a different team's naming convention; the PRD is never updated to match
• An A/B test quietly changes the trigger condition behind a guardrail metric
• A property gets added mid-build for a one-off debugging need, then quietly becomes load-bearing for a dashboard nobody documented

This is exactly the failure mode our piece on why a living PRD always stays current calls out: a spec is only useful if reading it later still matches what got built. A data requirements section is one of the sections most likely to drift silently, because unlike a UI mock, nobody visually notices when an event's payload quietly changes shape.

Prodinja's Spec Studio is built around exactly this problem. It's a living PRD — sections, inline comments, and PR-style diffs you explicitly approve or reject, rather than edits that quietly overwrite what came before.

Applied to a data requirements section, a proposed rename of search_alert_created to alert_created_v2 shows up as a diff for the PM to approve, not a silent change buried three commits deep in an engineering ticket. Readiness gates can require that section be signed off before a spec moves to "ready to build," and the engineering hand-off export carries the final tracking plan verbatim into what engineering implements from.

Key Takeaways

• Data requirements are a PRD section, not an afterthought — name events, properties, identity, a success metric, and a guardrail metric before build starts, not after launch.
• A goal is not a data requirement. "Success = more engagement" is a wish; an event schema with a numeric target and an owner is a requirement.
• Ban the catch-all event. A generic feature_used event tells you the feature was touched, not what happened, to whom, or whether it mattered.
• Rigor should scale with differentiation. Parity metrics can reuse existing events; metrics proving out real differentiation need dedicated events and a named weekly owner.
• Privacy review happens before instrumentation ships, not after a regulator or a user asks an uncomfortable question about it.
• Tracking plans drift on their own — event renames, quietly-added properties, and experiment-driven funnel changes accumulate silently unless something forces the change into view.
• A living PRD with approve-or-reject diffs is the mechanism that catches drift, not a static document nobody revisits after kickoff.