Category: Data Product Management

A VP of Analytics delivered 47 dashboards in 11 months. Six months later, only four were still used. According to Gartner, 82% of enterprise analytics initiatives fail to drive sustained decision-making. The problem isn’t execution—it’s solving for the wrong outcome entirely.

A customer health scoring model built perfectly—but used by almost nobody. David Ohnstad explores why organizations invest in data product management frameworks only after costly failures, and what actually drives adoption at scale.

Technical perfection isn’t enough. David Ohnstad reveals why most data products succeed in the lab but fail in the real world—and what separates the 29% that actually get used from the rest.

A 14-person data team shipped two products in 18 months. The problem wasn’t headcount—it was ownership. When nobody owns data quality for the churn model, nothing ships. Learn what actually breaks data teams and how to fix it.

A data PM shipped dashboards, automated pipelines, and cut query latency 40%—yet the role failed. The problem wasn’t execution. It was where the role sat in the org chart. David Ohnstad reveals why reporting lines determine success.

A nine-month dashboard project launched to 400 employees. Twelve logged in twice. One user was the intern who wrote the docs. David Ohnstad explains the critical gap between building data products and building products people want to use.

Sometimes shipping a customer-facing product means sacrificing it. David Ohnstad shares how Veeam killed a $250K analytics initiative mid-development to rebuild the metadata infrastructure every team depended on—and why that was the right call.

When sales ops, marketing, and engineering all need urgent data solutions, how do you decide what gets built first? Learn David Ohnstad’s framework for evaluating competing requests and building the right roadmap.

Most data mesh initiatives fail because organizations build platforms instead of products. Fannie Mae’s success with Amazon Redshift proves that treating data domains as products—with clear ownership and cross-functional teams—is the difference between stalling and shipping.

When seventeen data requests pile up and engineering pipelines keep getting deferred, you’ve hit the prioritization wall. David Ohnstad explores why teams struggle to balance speed and scale—and what actually works to break the deadlock.