Data Business Model Requirements

A framework for building successful data businesses, focusing on key requirements and common pitfalls based on CB Insights' experience.

Core Philosophy

• Data itself has no intrinsic value
• Value comes from what you can do with the data
• Must drive specific outcomes/edge for customers

Key Success Requirements (ECO Framework)

1. Edge

• Define clear advantage data will provide to customers
• Must solve specific business problem or decision
• Examples of valuable edges:
  • Sourcing deals for VCs/PE firms
  • Predicting quarterly earnings for investors
  • Benchmarking critical business metrics

2. Collection Feasibility

• Must have reliable way to gather data
• Avoid dependency on single data source/"hacks"
• Multiple collection methods reduce risk
• Common collection methods:
  • Manual data entry/research
  • Automated scraping
  • Interviews
  • Surveys

3. Opportunity Size

• Focus on industries with money to spend:
  • Financial services
  • AI companies
  • Sales/marketing/adtech
• Target buyers who make large dollar decisions
• Avoid dying industries or low-budget departments

Common Pitfalls

• Building for "nice to have" vs "need to have" use cases
• Relying on single data source or collection method
• Not considering buyer's ability/willingness to pay
• Targeting wrong industries or decision makers
• Assuming data has inherent value without clear edge

Business Model Considerations

• Subscription model creates ongoing delivery requirements
• Must maintain consistent data quality and freshness
• Consider pricing aligned with value delivered
• Example pricing tiers:
  • Basic data access
  • Data + analysis/calls
  • Premium real-time alerts