Category: Data Product Management

Most data product job descriptions demand SQL, Python, and BI tools expertise—yet none measure what actually matters: whether anyone uses the product. David Ohnstad’s analysis reveals the misalignment sabotaging team quality.

A seasoned B2C SaaS PM with five years experience felt lost building a data product. Discover why data product management demands a fundamentally different skillset, mindset, and approach than shipping features to millions of users.

A Fortune 500 hiring manager rejected three data PMs who shipped eight-figure products—because they couldn’t write SQL. David Ohnstad exposes the gap between what companies think they need and what actually builds successful data products.

The best data product managers aren’t always the ones who can write SQL. David Ohnstad reveals why hiring non-technical PMs often yields better outcomes than promoting engineers into management—and what hiring managers are getting wrong.

A technically perfect data product can fail spectacularly if end users don’t understand it. David Ohnstad’s seven-month health scoring model crashed to zero usage in 90 days—not because the data was wrong, but because the customer success team couldn’t explain what it meant.

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.