In Matt Haig’s new novel The Life Impossible, a boy asks, “Who would win in a fight, five hundred small cats or one single tiger?”

This analogy highlights a challenge I often encounter when discussing data analysis: we often believe we have a “Tiger”—a single, powerful recommendation ready to drive action—but instead find ourselves with “500 cats”: a collection of disconnected stats that fail to coalesce into a clear, actionable outcome. This gap between expectation and reality underscores the ongoing tension between the promise of data and its practical application in decision-making.

Going a little deeper, the “500 cats” represent traditional data analysis: numerous insights, each interesting or valuable in its own way but rarely reach their full potential in supporting strategic decision-making. The “Tiger,” by contrast, embodies decision science: a single, focused analysis purposefully aligned to drive decisions and deliver precise, measurable outcomes. This shift from the science of inputs (data and analysis) to the science of outputs (recommendations and actions) emphasizes the critical role of interpretation of analyses—a necessary prescriptive layer—in turning raw data into meaningful decisions.¹

When you reflect on the state of data and insights in your organization, do you see “500 cats” or a “Tiger”? And is that helping or hindering your ability to drive meaningful outcomes?

1. Since Data Scientists Don’t Do Business Decisions — Businesses Need Decision Scientists ↩︎