Data-Driven Decision Making: An Introduction

From Gut Feelings to Ground Truth

Intuition can spark ideas, but data helps validate direction before time and budget are committed. When leaders pair curiosity with evidence, teams execute faster, argue less, and learn more from every experiment.

A Short Story: The Coffee Shop That Listened to Data

A neighborhood café analyzed receipts and weather patterns, then shifted pastry baking to cooler morning hours. Waste dropped by 23%, regulars noticed fresher croissants, and the owner used savings to fund a cozy reading nook.

Common Myths, Kindly Debunked

Myth one: data replaces creativity. Reality: it amplifies it. Myth two: only big companies can do this. Reality: simple spreadsheets can reveal powerful patterns. Share a myth you have heard, and let’s unpack it together.

The Core Ingredients of Solid Data Decisions

Consistency, accuracy, completeness, and timeliness turn noisy signals into usable insights. Define owners, document sources, and set refresh cadences. Invite your team to co-create a simple data dictionary and keep it living.

The Core Ingredients of Solid Data Decisions

Pick a North Star that reflects customer value, then map supporting metrics. Fewer, better signals beat dashboard sprawl. Ask: what would change if this metric moved? If nothing changes, reconsider tracking it.

Getting Started: A Practical, Low-Lift Playbook

Frame decisions as testable questions: who, what, where, when, and why. Define success, guardrails, and acceptable trade-offs. Post your decision question in the comments, and we will help refine it together.

Getting Started: A Practical, Low-Lift Playbook

Pull only the data you need, then fix duplicates, missing values, and odd outliers. Add context from customer interviews or frontline notes. Numbers gain meaning when paired with real-world observations and stories.

Correlation vs Causation, Explained Simply

Two lines moving together does not prove one drives the other. Look for mechanisms, temporal order, and controlled comparisons. Share a chart you question, and we will explore causal alternatives together.

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Sampling, Survivorship, and Silent Data

Who is missing from your dataset? Happy customers speak loudly; silent churners do not. Seek negative examples and edge cases. Tell us a time missing data misled you, so others can learn from it.

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Confirmation Bias and Counterfactual Thinking

We all favor evidence that agrees with our hopes. Pre-commit to disconfirming tests and ask, “What would we expect to see if we are wrong?” Try it this week and report back your experience.

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