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Data definitions are crucial for accurate metrics, and their absence can lead to believable but wrong numbers that nobody questions.
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Data definitions are crucial for accurate metrics, and their absence can lead to believable but wrong numbers that nobody questions. Centralizing definitions and putting business logic in versioned, tested models can prevent metric disputes and ensure trust in data-driven decisions.