Organizations waste millions on dashboards, models, and automation each year. If a data project does not improve human effectiveness, it has failed. As practitioners, we have seen success only when technology serves people, and failure whenever people are expected to serve the machine.

This failure is reflected in the broader landscape. Gartner projects that by 2027, 80% of data and analytics governance initiatives will fail because they lack a real or even manufactured crisis to anchor them. Meanwhile, headlines on mass layoffs have intensified concerns that automation is being used to replace people rather than empower them. These dynamics follow a familiar pattern: leaders pursue automation and scale, teams inherit layers of complexity, and frontline workers retreat to spreadsheets when new systems fail to address the everyday questions that matter most.

The question then becomes: "Why do so many projects stall?" The answer lies in a fundamental confusion between building and using. Teams produce dashboards by the dozen, measure everything, and mistake quantity for quality. Employees are drowning in numbers and starving for usable insight. Industry reports repeatedly show that the

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