Blog

Most data product teams say yes to requests their architecture can’t support, leading to costly scope creep and failed deliverables. Understanding governance prevents this trap and keeps roadmaps realistic and aligned with technical constraints.

When budget constraints force you to choose between data quality, pipeline performance, and new features, most PMs default to reactive prioritization. David Ohnstad shares the framework that separates winning trade-off decisions from costly mistakes.

A fully-funded $180K data product with executive sponsorship and 40-person kickoff collapsed in 90 days. David Ohnstad breaks down the organizational failures that killed this initiative and the structural lessons every data PM needs to know before scaling.

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.