My data science methodology follows five stages: Business Alignment, Insight Discovery, Real-World Modeling, Decision Enablement, and Solution Scaling. BIRDS.
Many data scientists find it strange that Insight Discovery comes before modeling. After all, doesn't insight emerge from the model?
Simplistically speaking, yes. But here's the deeper truth: the model IS the insight.
By the time you reach Real-World Modeling, you've (hopefully) already discovered what the real business problem even is and how it works – what matters, how variables relate, what drives outcomes. The model doesn't generate that understanding, it encodes it. Your feature engineering, architecture choices, and problem formulation crystallize the insights you've already built.
What most people call "insights from the model" – predictions, scores, recommendations – are really "just" operational outputs. They're the model doing its job, enabling decisions. Important? Absolutely! But, as important as predictions and recommendations are, they're derivative, not foundational.
Yes, models can surprise you. But the BIRDS Framework emphasizes intentional understanding over hoping for chance discoveries. Business Alignment and Insight Discovery build the foundation. Decision Enablement is where insight becomes impact.
Happy to hear your thoughts about this – or anything else you're curious about. quique@databirds.ai
← Back to Blog