Data Business Success Factors

A framework for building successful data businesses based on three key criteria that create valuable market opportunities.

Core Success Criteria

• High Consideration

  • Customers are taking significant risk or spending substantial money
  • There's meaningful "skin in the game" in the decision
  • Makes customers willing to pay for quality information

• Opacity

  • Information is hard to find or access
  • Often indicated by people asking in Slack groups "does anyone know X?"
  • Creates natural demand for organized, accessible data

• Variable SKU

  • Products/services can't be easily compared in a simple table
  • More complex than comparing standard features (unlike phones or cars)
  • Example: Comparing different AI radiology software solutions
  • Complexity creates need for detailed analysis and insights

Building Data Assets

• Focus on "dirty work" others won't do

  • Be willing to manually clean data in spreadsheets
  • Don't rely solely on ML/AI solutions
  • Build moats through pain tolerance for tedious work

• Data Collection Methods

  • Manual outreach and calls
  • Mechanical Turk for data gathering
  • Hiring people to make inquiries
  • Building databases from public information

Monetization Strategy

• Price high from the start

  • Avoid low pricing that attracts difficult customers
  • Low prices can signal low quality
  • Example: Raised price from $395/month to much higher

• Target enterprise customers

  • Focus on customers who understand data value
  • Avoid individual users who may be high maintenance
  • Build relationships with decision makers who have budget

Common Mistakes

• Underpricing services

  • Attracts wrong customer base
  • Creates support burden
  • Harder to raise prices later

• Over-relying on technology

  • Thinking ML/AI will solve everything
  • Avoiding necessary manual work
  • Missing opportunities for human insight