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The 7 Truths of Data Science and Business Analytics
1. The Core Objective Is of Data Science and Business Analytics is Decision Making. The core objective of data science is clarity. We build models to make better decisions based on reality — not the illusion of it. We move from descriptions of past reality to predictive models of future reality.
2. Infrastructure Is Not Insight. Dashboards, machine learning, and AI are tools, not destinations. If they don’t improve decision-making, they’re just sophistication theatre.
3. Every Model Is an Imperfect Mirror. A model is never the truth — only a useful approximation. It carries bias, simplifications, and inherent error. But when we overfit, we trade generalization for illusion. Misleading through model massaging is a breach of trust, a distortion of truth, and a failure of integrity.
4. All Models Simplify. Some Oversimplify. Every model reduces complexity to make it manageable — that's its purpose. But when simplification loses the signal, the model becomes noise. Today’s techniques often push for abstraction, dimensionality reduction, or black-box tuning — to the point where we no longer understand what the model is doing. If we lose mathematical clarity, and we are unable to link features to outcomes, we lose the right to call it insight.
5. Variables Are Not What They Seem. Whether continuous, categorical, static, or dynamic — all variables are discrete events in a quantized space-time. We model one decision, one moment at a time.
6. Business Reality Is Measurable. In a company, reality isn’t philosophical — it’s observable, tangible, and stored. It lives in two places: master data (who we are) and transactional data (what we do). Reality is dynamic in time and space, complex, layered, non-deterministic, always probabilistic, non-linear, fundamentally messy, entangled, and profoundly human.
7. No Data, No Decisions. Without reliable data, every insight is guesswork. Good strategy is rooted in data integrity, quality, and trust. The foundation matters.