Data & Insights is where you collect and analyze validation data from various sources to test your Assumptions and inform product decisions. Transform raw data into actionable insights that guide your product development.
What is Data & Insights?
Data & Insights encompasses two key components for evidence-based product management:
Validation Data: Raw information collected from research activities, user feedback, experiments, and external sources that help test your product Assumptions.
Insights: Processed learnings and conclusions drawn from validation data that inform product decisions and strategic planning.
This feature helps you systematically gather evidence, organize research findings, and extract meaningful insights that validate or challenge your beliefs about users, market, and product-solution fit.
How to Access Data & Insights
Navigate to Data & Insights from the main sidebar by clicking Data & Insights, or visit /app/validation directly.
Data & Insights Section
Data Structure
Validation Data
Each validation record contains:
Content: Written observations, document text, or research notes
Source Information: Where the data originated (interviews, surveys, analytics, etc.)
File Attachments: Supporting documents, screenshots, or media files
External Links: URLs to related resources or tools
Tasks & Stories: Inform development work with research insights
Best Practices
1. Systematic Data Collection
Establish consistent processes for capturing validation data from all research activities.
2. Immediate Processing
Convert raw data into insights while context is fresh and details are clear.
3. Link to Assumptions
Always connect insights back to the specific Assumptions they support or challenge.
4. Maintain Quality
Focus on actionable insights that directly inform product decisions, not just interesting observations.
5. Use Multiple Sources
Validate insights with data from multiple sources and research methods for higher confidence.
6. Share Broadly
Make insights accessible to your entire team to inform decision-making across the organization.
7. Update Regularly
Continuously add new data and insights as you learn more about your users and market.
Data & Insights Success
The most effective validation combines systematic data collection with AI-powered insight generation. Use spells to accelerate analysis, but always validate findings with additional research and team discussion.
Integration with Other Platform Features
Research Foundation
Market Research: Validate market size and opportunity assumptions with real data
Customer Voice: Test beliefs about specific Personas with user feedback and research
Interviews: Generate insights from problem discovery conversations and user research
Validation Activities
Assumptions: Provide evidence that validates, invalidates, or requires more research
Testing Cycles: Create feedback loops between assumptions, validation activities, and insights
Development Planning
Tasks & Stories: Inform development priorities and feature requirements with validated insights
Decision Making: Support product strategy with evidence-based recommendations
Workflow Examples
Assumption Testing Workflow
Select Assumption: Choose a high-priority assumption to test
Design Validation: Plan research activities to gather relevant data
Collect Data: Use Interviews, surveys, or analytics to gather evidence
Generate Insights: Use spells or manual analysis to extract learnings
Update Assumptions: Mark assumptions as validated, invalidated, or needing more research
Plan Next Steps: Create Tasks & Stories based on validated insights
Feature Validation Workflow
Feature Concept: Define what you want to build and why
Create Assumptions: Document beliefs about user needs and feature value
Prototype Testing: Build simple prototypes and gather user feedback
Collect Feedback: Capture user reactions, usage data, and behavioral insights
Generate Insights: Analyze feedback to understand feature viability and improvements
Refine or Pivot: Update feature plans based on validation insights
User Research Synthesis
Conduct Interviews: Run problem discovery sessions with target users
Upload Transcripts: Add interview recordings or notes to validation data
Extract Insights: Use AI to identify themes, quotes, and key findings
Update Personas: Refine user understanding based on research insights
Identify Opportunities: Discover new problems and needs for future development
Data & Insights Section
Insights Section