Author: David Ohnstad

I accepted a data PM role at $138K, only to discover the same company hired a PM for $185K with nearly identical responsibilities. The mistake? Negotiating within the wrong salary band. Here’s what I learned about positioning and anchoring.

Data product managers handle triple the stakeholder complexity but earn significantly less than technical PMs. David Ohnstad exposes the salary disparity and provides actionable strategies to negotiate fair compensation based on your skills and experience.

A unified analytics platform launched on schedule with perfect architecture—then failed immediately. The problem wasn’t technical. Three departments couldn’t agree on who decided what. This is the governance crisis destroying data products.

A data pipeline shipped on schedule with full stakeholder approval—but processed files in alphabetical order instead of chronological, invalidating 40% of analysis. This is the symptom, not the problem. Learn what data product managers actually need to solve.

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.