Data Trust Framework

A framework for building successful data businesses by establishing trust relationships before collecting sensitive data, based on insights from Anand Sanwal's experience with CB Insights.

Core Data Business Framework ("ECO")

• Edge - Define the competitive advantage upfront

  • Must solve a specific problem for customers
  • Data itself has no intrinsic value without clear use case
  • Focus on outcomes data will drive for customers

• Collection Feasibility - How to gather the data

  • Build trusted relationships with data sources
  • Avoid single-source dependencies that create business risk
  • Start with manual collection if needed, then automate
  • Consider multiple collection methods for resilience

• Opportunity Size - Target the right markets

  • Financial services
  • AI companies
  • Sales/marketing/adtech
  • Focus on verticals where data drives high-value decisions

Key Success Factors

• Trust Building

  • People must trust how you'll use their data
  • Anonymize and aggregate sensitive information
  • Be clear about data usage and protection
  • Leverage existing relationships and credibility

• Target Buyers

  • Focus on customers making large financial decisions
  • Look for buyers who can derive clear ROI from data
  • Avoid nice-to-have use cases like basic benchmarking
  • Target industries with budget for data purchases

• Collection Methods

  • 7 different ways to collect data (not all specified in transcript)
  • Start manual/"data janitor work" if needed
  • Build more sophisticated collection over time
  • Maintain multiple collection channels

Common Mistakes to Avoid

• Assuming data has inherent value without clear use case
• Relying on single data source or collection method
• Targeting buyers who won't derive enough value
• Focusing on nice-to-have vs must-have data
• Not establishing trust before collecting sensitive data
• Building for markets too small to support data business