Most orgs don’t have a data problem—they have a decision problem. This executive memo reveals why dashboards and training won’t fix culture, and what leaders must do differently to turn data into real decisions.
Most AI efforts fail not because of bad tech, but because of bad design. This article reframes AI as an organizational challenge, not just a data science project. Learn what business leaders must rethink to turn AI from a pilot into a competitive advantage.
Think your org is data-informed? Think again. Most companies are still making bad decisions—just with dashboards attached. Discover why traditional data literacy fails—and what it really takes to build a culture where data drives action.
A collection of key analytical principles to help you create more effective and insightful data visualizations
Don't rely on totals or averages alone—break your data down by relevant subgroups to uncover hidden insights.
When data fluctuates rapidly, use smoothing techniques like rolling averages to make patterns clearer.
Don't just chart what happened—compare it to what should happen with benchmarks, goals, or standards.
Don't hide variation behind averages—differences often reveal valuable patterns or inconsistencies.
Data doesn't exist in a vacuum—major events can shift patterns in ways the chart alone won't explain.
Outliers may represent important signals—extreme success, failure, or exception cases worth investigating.
A single number means little without something to compare it to—comparative framing adds meaning.
Averages and totals can be misleading—they flatten the data and hide important differences.
When two things appear related, a third factor might be influencing both—correlation isn't causation.
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