Why this matters
As an AI Product Manager, you turn product goals into actionable data plans. Clear data requirements prevent wasted engineering, unblock model development, and keep your product compliant and fair. You will routinely:
- Scope features into measurable outcomes and the data needed to achieve them.
- Map internal and external data sources, check coverage and freshness, and plan integrations.
- Balance accuracy, latency, cost, and privacy constraints.
- Define labels and ground truth strategies so your team can train and evaluate models.
- De-risk launches by identifying data gaps early.
Concept explained simply
Defining data needs means answering: What decision or experience are we enabling, and what signals, labels, and constraints do we need to make it work safely and reliably?
Mental model: The 9-Block Data Canvas
- Outcome / Target: What will the model decide or predict? Define the target/label clearly.
- User & Context: Who is affected? In what workflow or environment?
- Signals / Features: Which inputs indicate the target? List required, nice-to-have, and proxy signals.
- Labels / Ground Truth: How will you get correct answers for training and evaluation?
- Sources: Internal, external, user-generated, synthetic, or human-in-the-loop (annotation).
- Coverage & Volume: Languages, regions, device types, seasonality; estimated data size.
- Freshness & Latency: Real-time vs batch; acceptable delay from data generation to model use.
- Quality & Bias: Accuracy, completeness, noise; fairness across segments; drift detection.
- Privacy, Rights, Cost: PII handling, consent, licenses/usage rights, retention, budget.
Tip: Turn goals into data with one sentence
Use: "To achieve [outcome], the system needs [signals] from [sources], with [freshness/latency], labeled by [method], while meeting [privacy/rights] and [budget]."
Worked examples
Example 1: Spam detection in a messaging app
- Outcome/Target: Predict if a message is spam (binary label).
- Signals: Message text features, sender reputation, sending velocity, link domains, prior recipient interactions.
- Labels: User reports, moderation decisions, historical blocklists.
- Sources: Internal message logs, moderation tools, domain reputation lists (external).
- Coverage/Volume: High-volume, multilingual; requires language detection.
- Freshness/Latency: Near real-time scoring (<100 ms) to protect inbox.
- Quality/Bias: Balance false positives/negatives; fairness across languages.
- Privacy/Rights/Cost: PII handling; store only features needed; rights for third-party domain lists.
Example 2: E-commerce recommendations
- Outcome/Target: Rank products to maximize next-session purchase probability.
- Signals: Browsing history, cart events, item attributes, price, stock, similarity embeddings.
- Labels: Click-through, add-to-cart, purchase within 24 hours.
- Sources: Internal clickstream, product catalog, image/text embeddings.
- Coverage/Volume: Millions of users/products; cold-start for new items.
- Freshness/Latency: Batch candidate generation hourly; real-time re-ranking at request time.
- Quality/Bias: Avoid popularity bias over niche categories; seasonality awareness.
- Privacy/Rights/Cost: First-party data; minimize PII; retention policy enforcement.
Example 3: Support ticket routing
- Outcome/Target: Predict best queue (billing/tech/sales) to reduce resolution time.
- Signals: Ticket text, product metadata, customer tier, prior issue codes.
- Labels: Final queue assigned by human or resolved category.
- Sources: Helpdesk system, CRM, knowledge base tags.
- Coverage/Volume: Multilingual; spikes during releases.
- Freshness/Latency: Seconds-level latency acceptable.
- Quality/Bias: Avoid misrouting VIP or minority-language tickets.
- Privacy/Rights/Cost: Contractual controls on CRM data; redact PII before training.
How to scope your data needs (step-by-step)
- Write the target: One sentence with metric window (e.g., "purchase within 24h").
- List 5–10 candidate signals: Mark must-have vs nice-to-have and expected direction of influence.
- Identify labels: Where do correct answers come from? Define positive/negative rules.
- Inventory sources: Internal tables/logs, vendors, user input, synthetic/augmented data.
- Decide freshness/latency: Real-time, micro-batch, or daily.
- Check quality and bias risks: How will you measure and mitigate?
- Check privacy/rights/cost: PII, consent, licenses, retention, budget.
- Draft acceptance criteria: Minimum coverage, null rates, on-time SLAs, basic performance baseline.
- Assign owners: Data producers, stewards, and an integration timeline.
Data requirements checklist
- Target/label is specific, measurable, time-bound.
- Signals defined with owner and expected availability.
- Labeling plan exists (rules or annotation) with quality control.
- Sources mapped: internal, external, synthetic; access path and costs known.
- Freshness and latency agreed with engineering.
- Quality metrics (nulls, duplicates, drift) and monitoring plan.
- Privacy, usage rights, and retention reviewed.
- Risks and mitigations documented (e.g., cold-start).
- Acceptance criteria and owners confirmed.
Exercises
Do these short exercises. They mirror the graded exercises below. Hints and solutions are collapsible.
Exercise 1: Draft a Data Canvas for send-time optimization
Scenario: Your app wants to send notifications when users are most likely to open within 2 hours.
- Define the target/label.
- List 6–8 signals (must-have vs nice-to-have).
- Propose sources and freshness/latency.
- Call out privacy/rights constraints.
- Write acceptance criteria for a pilot.
Hints
- Use historical open/click times as labels.
- Timezone and device activity are strong signals.
- Freshness likely daily with real-time overrides.
Example solution outline
See solution in the "Exercises" section below (Exercise 1).
Exercise 2: Source evaluation under constraints
Scenario: You need user interests to improve recommendations, with weekly updates, global coverage, minimal PII, and commercial usage rights.
- Compare three hypothetical sources on coverage, freshness, rights, cost, and risk.
- Select a combination and list mitigations for top risks.
Hints
- Favor first-party data when possible for rights and freshness.
- Be cautious about scraping legality and quality drift.
Example solution outline
See solution in the "Exercises" section below (Exercise 2).
Common mistakes and self-check
- Vague targets: If two people can interpret your label differently, it is not ready. Self-check: Can you compute it from raw data today?
- Ignoring labels: Features without labels stall training. Self-check: Where do ground truth and negatives come from?
- Over-collecting PII: Storing data "just in case" increases risk. Self-check: Can you achieve the outcome with aggregated or anonymized signals?
- Underestimating freshness: Stale data ruins real-time experiences. Self-check: What is the maximum acceptable delay?
- Coverage blind spots: Model fails for certain languages/regions. Self-check: Do you have enough examples per key segment?
- Vendor rights confusion: Licenses may forbid model training. Self-check: Do you have explicit rights for training, inference, and redistribution?
- No owner: Data drifts because no one is accountable. Self-check: Who fixes breaks and monitors quality?
Practical projects
- Create a one-page Data Canvas for a current or hypothetical feature and review it with engineering and legal.
- Run a data source inventory: list internal tables, logs, and external vendors; score each on coverage, freshness, rights, and cost.
- Define a minimal labeling plan: sampling strategy, guidelines, and QA checks for 200–500 examples.
- Draft basic data acceptance criteria and a monitoring plan (null rates, duplicates, drift alert thresholds).
Who this is for
AI Product Managers, product leaders, data product owners, and cross-functional partners (analytics, data engineering, ML) who scope AI features and need clear, practical data plans.
Prerequisites
- Basic understanding of supervised vs unsupervised learning and evaluation metrics.
- Comfort reading simple dashboards or queries (SQL helpful but not required).
- Awareness of privacy concepts (PII, consent, retention) and internal data policies.
Learning path
- Before: Problem framing for ML, metrics and experimentation basics.
- This subskill: Define targets, signals, labels, sources, and constraints.
- Next: Data collection and pipelines, annotation operations, governance and compliance, offline/online evaluation, and model iteration.
Next steps
- Book a 30-minute working session with engineering to validate freshness/latency and data access paths.
- Meet with legal/security to confirm usage rights and retention for any external data.
- Set milestones: data extraction, first EDA snapshot, labeling pilot, and baseline model by agreed dates.
Mini challenge
In 10 minutes, draft a Data Canvas for an "AI meeting notes summarizer" feature. List the label, top signals (audio, transcripts, speaker turns), sources (internal recordings, ASR outputs), freshness needs (post-meeting within 5 minutes), and privacy constraints (consent, redaction). Compare two options for labels: human-edited summaries vs thumbs-up/down feedback.
Quick Test and progress saving
The quick test for this subskill is available to everyone. Only logged-in users have their progress saved automatically.