AI did not end the data literacy crisis. It changed it. As machines move from providing information to interpreting it and recommending what to do, the scarce skill is becoming judgment: knowing what to trust, what to question, and what decisions are still ours to make.
Evidence does not make decisions. People interpret it, weigh it, challenge it, and make judgments. A courtroom reveals what leaders can learn about evidence, expertise, confidence, and data-informed decision-making.
Feeling sure doesn't mean you're right. Learn five simple ways to question what feels true, spot what might be influencing you, and make better decisions about what to believe.
Sometimes the hardest part of creating an effective data visualization isn't the data or the tool — it's knowing what structure fits the story. This guide provides reusable patterns, or design templates, for common analytic goals.
Think of these patterns as recipes: they provide a proven structure that you can adapt to your specific data and audience needs. Each pattern is suited to particular types of analytical questions and goals.
For each design pattern, we'll cover:
Goal: Show change over time, especially due to an intervention or key event.
Example: Department Store Sales Before and After Renovation
Side-by-side bars make it easy to compare before and after values across multiple categories. This works well when you have multiple metrics or categories to compare.
Dumbbell plots emphasize the change by connecting the before and after points. This format works especially well when you want to focus on the magnitude and direction of change.
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