Defining success

Demystifying Decision Science

Akshay Swaminathan

PD Soros Fellow at Stanford University School of Medicine

Success in Decision Science

Great models aren't enough without impact

  • Sophisticated analysis is only valuable if it's used and acted upon

Undefined success leads to wasted effort

  • Without shared goals, insights may be ignored, misunderstood, or go unused

Success must be agreed upon

  • Align with your stakeholder or customer on what a "successful outcome" looks like - before the work begins

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Demystifying Decision Science

Understand what success looks like to your customer

Be customer-focused

  • Focus on the problems they need solved
  • Clarify what outcomes would be considered a win

 

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Success is not just about accuracy

  • The real value lies in how the model supports better decisions
  • A restaurant chain may care more about reducing waste and targeting marketing than about model precision
Demystifying Decision Science

Measures of success

  • Performance: Model accuracy, precision, recall, or other technical indicators
  • Time: Whether the project was delivered when it was needed
  • Cost: Whether you stayed within budget
  • Quality: Code clarity, documentation, and reproducibility
  • Stakeholder satisfaction: Was the work well-communicated and useful to the audience?
  • Business impact: Did the project contribute to strategic goals or measurable improvement?

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Demystifying Decision Science

Minimum Viable Product (MVP)

Start simple

  • The MVP is the simplest version of your solution that delivers core value
  • Answer one key question and be ready to implement

Use MVPs to build momentum and trust

  • Agree on success metrics at the beginning
  • Communicate, track, and report progress
  • Demonstrate early wins and build support

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Demystifying Decision Science

Let's practice!

Demystifying Decision Science

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