data-informed-decisions

Use data to make better decisions, not to avoid making them.

How to use

• /data-informed-decisions Apply data-informed decision constraints to this conversation.
• /data-informed-decisions <situation> Evaluate data for the described product decision.

Constraints

Data Role

• Data should INFORM decisions, not MAKE them. The PM still owns the judgment call.
• MUST combine quantitative data (what users do) with qualitative data (why they do it)
• SHOULD use data to narrow options, then use judgment for the final call
• NEVER let a dashboard substitute for product thinking
• MUST know what question the data answers before looking at the data

When Data Helps

• Measuring what happened after a change (A/B tests, before/after comparisons)
• Identifying patterns in behavior (usage frequency, feature adoption, funnel conversion)
• Sizing opportunities (how many users are affected, how often)
• Validating or invalidating hypotheses with actual behavior
• SHOULD start with a hypothesis, then check data — not the other way around

When Data Misleads

• Small sample sizes: MUST know when the data isn't statistically significant
• Survivor bias: the users you can measure are the ones who stayed. The ones who left are invisible.
• Correlation ≠ causation: users who use Feature X retain better, but Feature X might not cause retention
• Metric gaming: optimizing a metric while the underlying experience gets worse
• MUST check if the metric captures what you think it captures. Proxy metrics can be dangerous.

Balancing Qual and Quant

• Quantitative tells you what is happening and how much
• Qualitative tells you why it's happening and what it means
• MUST use both. Data without context leads to wrong conclusions.
• SHOULD lead with qualitative when exploring new territory, quantitative when optimizing known territory
• NEVER dismiss user testimony because it doesn't match the numbers — investigate the gap

Data Humility

• MUST acknowledge when the data is inconclusive
• SHOULD distinguish between "the data shows X" and "we believe X based on limited data"
• MUST be honest about data quality, gaps, and instrumentation issues
• NEVER cherry-pick data to support a predetermined conclusion
• SHOULD present conflicting data points rather than hiding them

Anti-Patterns

• Data Worship: refusing to make decisions without perfect data
• The Vanity Dashboard: tracking metrics that look good but don't correlate with business health
• Post-Hoc Rationalization: making a gut decision then finding data to justify it
• Metric Tunnel Vision: optimizing one number while everything else degrades
• Ignoring Data: making decisions purely on intuition when relevant data exists