Newsletter

Is your AI doing what everyone thinks it's doing?

Most organizations don't know what their AI systems actually optimize for. They know what they paid for, what the vendor promised, what the dashboard shows. But ask them to trace a specific decision through its real-world impact, and watch the confidence … Read full article

Most data initiatives fail because companies focus on the wrong problems. They obsess over data products and governance frameworks while neglecting basic organizational misalignment.

The pitch sounds compelling: clean the data, wrap it in APIs and metadata, assign responsibilities, then watch the profits grow. It's an appealing narrative because it … Read full article

In a world drowning in metrics, the crucial skill is synthesizing them into decisions—knowing which numbers to trust, ignore, and compress.

Data enhances decisions but can't provide the final verdict. Raw numbers offer candor without counsel: they can't resolve trade-offs or absorb context. Dashboards illuminate, but judgment decides where to … Read full article

Yesterday we labeled everything AI; today we might too often forget what Machine Learning and statistics do well.

We've managed a peculiar feat in AI: simultaneously overhyping and underappreciating the technology. For years, the industry branded every statistics tools as "Artificial Intelligence" despite their limited resemblance to genuine intelligence. This … Read full article

Some data silos are organizational wisdom, not corporate sin.

In the world of data, "silo" has become the all-purpose insult. Executives rail against it, software vendors peddle “silo-busting” tools, and managers are convinced that if only they could tip every departmental datapoint into a unified structure, truth would reveal itself.