Thoughts on analytics engineering, BI strategy, and working with data. --- ## Talks > [!abstract] Analysts 2049: Redefining the Role of the Analyst in the Age of AI > Presented at **dbt Coalesce 2025**. How AI is reshaping what it means to be a data analyst - and what the role looks like going forward. <iframe width="100%" height="400" src="https://www.youtube.com/embed/-rFXXMdbqHA" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> --- ## Articles > [!example] [[writing/BI as Code|BI as Code: Picking Data Viz Tools When an Agent Writes the Code]] > Feature checklists stop mattering once a coding agent writes the dashboard. The four buckets of the AI-era data viz landscape, what I build client dashboards with, and where dbt Charts fits. > [!example] [[writing/Why Your Metrics Dont Match|Why Your Metrics Don't Match (And How a Semantic Layer Fixes It)]] > Every analytics team eventually hits the moment where two dashboards show different numbers for the same metric. Here's why it happens and what to do about it. > [!example] [[writing/BI Migration as Governance|BI Migration Is a Governance Opportunity, Not a Lift-and-Shift]] > When you're forced to migrate BI platforms, resist the urge to replicate what you have. It's the best chance you'll get to clean up years of accumulated technical debt. > [!example] [[writing/Forecasting for Humans|Building Forecasting Models That Stakeholders Actually Trust]] > A technically brilliant model that nobody believes is worse than a simple one they'll act on. How to design forecasts that balance rigor with accessibility.