The Archive
Sorted by who pays the bill. CFO essays first (cost, capital allocation, portfolio economics), then CEO essays (strategy, decision architecture), then Ops essays (running the data portfolio). Republished on-domain where you see the Read the essay link; the rest live on Cooking Data.
Most CEOs underestimate data team cost by 25-40%. Learn how to calculate the real number, including the hidden hours your business teams spend on workarounds.
Read the essayMost CEOs measure data costs, tools, salaries, infrastructure. They miss the largest line item: capacity lost to friction. Here's how to find it, name it, and price it.
Read the essayEvery data team pays an invisible tax, the recurring cost of misaligned tools, duplicated dashboards, and unanswered questions. The number is bigger than you think.
Read the essayMost data budgets are 80% Run and 20% Build, and the Run side is where the friction hides. Here's how to flip the ratio.
Read on Cooking DataIf you can't price a single decision your data delivers, you can't manage the portfolio. PCU-V gives you the unit economics CFOs can audit.
Read on Cooking DataYou don't have a Snowflake problem, you have a question-quality problem. The cheapest query is the one you don't need to run.
Read on Cooking DataHow to reframe data investment in language a CFO can actually defend in a board meeting.
Read on Cooking DataThe four data-portfolio signals PE partners should price into a deal, and the three that destroy value post-close.
Read on Cooking DataMost data strategies are obsolete by March. Here's the structural reason your initiative list drifts, and a shorter-cycle approach that keeps it on track.
Read the essayAI tools amplify what's already in your data infrastructure, including the problems. What to check before your next AI data initiative gets approved.
Read the essayData utilization and ROI sound right but produce no actionable signal. Here's what to measure instead, and why it changes the budget conversation entirely.
Read the essayCompanies invest millions in data architecture and get back dashboards nobody uses. The fix isn't more pipelines, it's mapping the decisions first.
Read the essayWaiting for clean data is the most expensive mistake a CEO can make. Here's how to find signal in the noise you already have.
Read on Cooking DataHow to spot, and dismantle, the dashboards, rituals, and 'data-driven' performances that produce activity but no decisions.
Read on Cooking DataWhat we look for in the first session, the four signals that predict whether your data portfolio will compound or decay.
Read on Cooking DataMost AI initiatives die because they inherit the same friction the data team has been paying for years. Here's the order of operations.
Read on Cooking DataYour data team is fully utilized but the roadmap isn't moving. Here's why, and the one question that reveals where the capacity is actually going.
Read the essayTwo filters that remove politics from data prioritization: how much business time does this save, and what does it add to the ongoing maintenance burden?
Read the essayEvery data product your team ships is still on the balance sheet. Here's why the maintenance cost compounds silently, and what to do about it.
Read the essayA practical scorecard for CEOs who want to measure how much of their data team's capacity is being drained by friction, and how much is shifting from new value to keeping the lights on.
Read on Cooking DataWhen your official platform becomes a ghost town and Excel runs the business, the problem isn't the tools, it's the trust.
Read on Cooking DataA walkthrough of the methodology I use with growth-stage founders, Strategic Baseline, Portfolio Measurement, and the 3-5-7 Cycle, and why each layer exists.
Read on Cooking DataTickets closed, dashboards shipped, queries run, none of them measure what matters. Here's what does.
Read on Cooking Data