Power BI design: model first, then calculation groups
A practical guide to Power BI design: star schema, calculation groups, field parameters and dashboard layout habits that keep reports fast and easy to maintain.
Notes & writing
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Revenue Operations, payments and data analytics — practical notes from running RevOps and analytics at scale.
Power BI Copilot: what it can and can't do today
What Copilot in Power BI does well, where it breaks, and how to test it on your own semantic model before business users start trusting its answers.
Sales forecast accuracy: measure it, stop sandbagging
Learn how to measure sales forecast accuracy with WAPE and bias, why a perfect team total can hide rep-level errors, and how to spot sandbagging in your data.
DuckDB for analysts: local analytics without a warehouse
Query CSV, Parquet and Excel files with plain SQL on your own machine using DuckDB. Practical examples, plus honest limits on concurrency and shared use.
Git and an LLM for Power BI: a new way to build DAX and M
Save Power BI as text, version it in GitHub and connect an LLM to write DAX and M. Why this workflow changes how analysts build, review and trust their models.
Why AI analysts fail on bad metric definitions
AI can write valid SQL in seconds, but it can't know which definition of revenue your CFO approved. Why a governed metrics layer matters, with benchmark data.
SQL window functions: 10 patterns for real analysis
Window functions keep your rows while you calculate across them. Learn ten patterns: ranking, deduplication, running totals, LAG, cohorts and streaks.
RevOps Benchmarks 2026: What Gartner Says, What to Trust
RevOps benchmark reports are everywhere, but few show their sources. See what Gartner reports, how to vet a benchmark, and which metrics to track yourself.
Cohort-Based Revenue Forecasting for E-commerce, Step by Step
Learn to forecast e-commerce revenue from customer cohorts. Includes SQL, a worked backtest, and the common mistake that cost 9% forecast accuracy.
The RevOps Data Quality Audit: A 10-Point Checklist
Bad CRM data quietly breaks forecasts and reports. Use this 10-point revenue data audit with ready-to-run SQL checks to find and fix the biggest gaps.
Network Tokens and Authorization Rates: What the Data Says
Do network tokens raise card approval rates? Compare Visa's 4.8% and Mastercard-cited 10.3-point figures, and learn why the numbers differ.
Why False Declines Cost E-commerce More Than You Think
False declines turn good customers away. See what the data says about their cost, why they happen, and how to measure and reduce them in e-commerce.
Attribution vs. Incrementality vs. MMM: Which to Use When
Attribution, incrementality tests and marketing mix modeling answer different questions. Learn when to use each, with evidence from field experiments.
Credit Card Decline Codes Explained in Plain English
What do Visa, Mastercard and Amex decline codes mean? A plain-English guide to the most common codes and what merchants should do next.