How to Pivot Data in SQL: PIVOT vs CASE WHEN
Turn rows into columns in SQL with CASE WHEN or PIVOT. Copy-paste examples for PostgreSQL, SQL Server, Snowflake and DuckDB, plus how to unpivot.
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Revenue Operations, payments and data analytics — practical notes from running RevOps and analytics at scale.
ROAS vs ROI vs MER: Which Marketing Metric to Trust
ROAS vs ROI vs MER explained with one worked example. Learn what each measures, how to find your break-even ROAS, and when to trust each one.
Spec-Driven Development with Claude Code: My Setup
How I use spec-driven development with Claude Code to build a real site: the six docs, the rules that keep chat out of them, and a real session.
AI Transformation Starts With Your Data, Not the Model
Why most AI transformation programmes stall, what the research says, and a practical data-readiness checklist analysts can run first.
First-Principles Thinking for Data Analysts
What first-principles thinking means for a data analyst, with worked examples: questioning a metric, a benchmark and a dashboard request.
SQL Interview Questions Analysts Are Actually Asked
The recurring SQL interview patterns behind most data analyst interviews, how to approach each, and how interviewers actually grade the answers.
RICE Prioritization: Scoring a Roadmap Without Bias
How the RICE scoring framework works, a worked example ranking three features, and where it breaks down as a decision tool.
UTM Parameters Explained: A Naming Convention That Works
What each UTM parameter does, the case-consistency trap that splits your reports, and a simple naming convention that keeps tracking usable.
CAC Payback Period: How to Calculate It, What's a Good Number
How to calculate CAC payback period from gross margin, why it differs from LTV:CAC, and what counts as healthy at different SaaS stages.
How to Value an Early-Stage SaaS Company: Methods Compared
Revenue multiples vs discounted cash flow for early-stage SaaS valuation. What each method assumes, when each breaks down, and how they relate.
Break-Even Analysis for Pricing, Step by Step
Calculate break-even units, margin of safety, and the extra volume a discount actually requires to stay profit-neutral, with worked examples.
SaaS Metrics That Matter: MRR, Churn, LTV and NRR Explained
MRR, churn, NRR, quick ratio and Rule of 40 explained in plain English, with a worked example showing why revenue growth alone can hide trouble.
KPI Dashboard Best Practices: Build One People Actually Open
Most dashboards get built, launched and forgotten. Design for decisions, keep the metric count low, define every number, keep data fresh and review usage.
Common A/B Testing Mistakes That Invalidate Results
The mistakes that quietly invalidate an A/B test even when the math is done correctly — peeking early, underpowered samples, and more.
Claude Code for Data Analysis: A Beginner Setup
How an analyst can set up Claude Code to work with CSV files, SQL and Python safely. Project folder, context file, permissions, Git and a first session.
Chi-Square Test Explained: When to Use It and How to Read It
What the chi-square test measures, how to read observed vs expected counts, and when it's more useful than a simple two-proportion test.
A/B Test Sample Size and Significance: A Plain-English Guide
How to calculate the sample size an A/B test needs and how to read the result once it's done, with a worked example and a free calculator.
Building a Simple Data Analyst AI Agent With Claude
What separates an AI agent from a chatbot for analyst work, a minimal worked example, and the guardrails to put around it before it touches real data.
Text-to-SQL Tools Compared: What Works, What Doesn't
General chat assistants vs BI copilots vs dedicated text-to-SQL products. What each does well, where each breaks, and how to evaluate one.
Why LLMs Hallucinate Numbers in Analytics (and How to Catch It)
LLMs predict plausible text, not computed results. See why they invent numbers in analytics work, the patterns to watch for, and how to catch it before it reaches a report.
Data Analyst Portfolio Projects That Get Interviews
What hiring managers look for in a data analyst portfolio, how to choose three strong projects, and how to present each one around a business problem.
Power BI vs Tableau: How an Analyst Actually Chooses
Power BI vs Tableau compared on cost, modeling, visual flexibility and learning curve, with a practical way to decide between them.
DAX Variables (VAR): Why They Fix Half Your Measures
VAR...RETURN makes DAX measures faster, easier to debug and easier to read. See the syntax, the performance reason, and the one gotcha with context transition.
Power BI Row-Level Security: A Practical Setup
Set up Power BI row-level security step by step, static and dynamic, plus the pitfalls that let data leak past a security role.
What Is a Semantic Layer, and Why AI Analytics Needs One
A semantic layer defines your metrics once so people, dashboards and AI all use the same numbers. Learn what it is, what it changes for text-to-SQL, and how to start small.
Why Your SQL Query Is Slow: Reading an Execution Plan
Learn to read EXPLAIN output, spot the difference between a scan and an index, and fix the handful of causes behind most slow SQL queries.
Excel vs SQL vs Power BI: How an Analyst Chooses
Which tool for which job? A practical way to choose between Excel, SQL and Power BI, based on data size, repeatability, audience and who needs to reuse the work.
How to Validate AI-Generated Analysis Before It Reaches Your Boss
A practical checklist for checking AI-generated analysis: reconcile totals, trace every number, test edge cases and label what is still an assumption.
GROUP BY vs HAVING vs WHERE: The Order That Actually Matters
WHERE filters rows, GROUP BY collapses them into groups, HAVING filters the groups. See SQL's real execution order and where each clause belongs.
SQL Joins Explained: Inner, Left, Right and Full
What INNER, LEFT, RIGHT and FULL joins actually do, why rows duplicate or disappear, and how to pick the right one for a report.
Data Storytelling for Non-Technical Stakeholders
How to present analysis so busy, non-technical people act on it. A simple structure, slide habits, and the mistakes that make good analysis get ignored.
Metric Definition Doc: What to Write Down (With a Template)
Stop dashboard debates with a one-page metric definition. Includes a copy-paste template, a filled-in example and the owner, grain and exclusion rules that matter.
AI for Data Analysts: Which Tasks to Hand Over, Which to Keep
A practical split of analyst work into tasks AI does well, tasks it can help with under supervision, and tasks that should stay yours, with examples.
Slow Power BI Report? 7 Fixes in Order of Payoff
A slow Power BI report usually has a few clear causes. Measure with Performance Analyzer first, then apply these seven fixes, biggest payoff first.
Prompting an LLM for SQL You Can Trust
How to get reliable SQL from ChatGPT, Claude or Copilot. What context to give, how to use examples, and the checks that catch wrong joins and made-up columns.
Power BI Time Intelligence: YTD, YoY and Rolling 12 Months
Build year-to-date, year-over-year and rolling 12-month measures in DAX. Includes the date table rules that make time intelligence work, with copy-paste measures.
DAX CALCULATE Explained: Filter Context in Plain English
CALCULATE is the most important DAX function and the most confusing. Learn filter context, row context and context transition with simple examples.
Power Query vs DAX: Where Each Step Belongs
Not sure whether to clean data in Power Query or calculate in DAX? Use this simple rule, with examples for calculated columns, measures and the M language.
Power BI Star Schema for Beginners: A Practical Walkthrough
What a star schema is, why Microsoft recommends it for Power BI, and how to reshape a flat table into facts and dimensions step by step.
SQL CTE vs Subquery vs Temp Table: Which to Use
CTE, subquery or temp table? A plain-English comparison for analysts, with examples, the performance trade-offs, and a simple rule for choosing.
SQL Date Functions: Fiscal Calendars, Week Starts and Time Zones
Handle dates in SQL without wrong totals. DATE_TRUNC, week starts, fiscal years and time zones explained for analysts, with queries and a calendar table.
How to Find and Fix Duplicate Rows in SQL
Find duplicate rows in SQL with GROUP BY and ROW_NUMBER, then remove them safely. Copy-paste queries, the traps to avoid, and how to stop duplicates coming back.
Cohort Retention Analysis in SQL, Step by Step
Build a cohort retention table in SQL in three steps. Includes a full PostgreSQL query, how to read the triangle, and the mistakes that skew retention.
SQL Cheat Sheet for Data Analysts: 15 Queries You'll Reuse
A practical SQL cheat sheet for data analysts. Fifteen copy-paste queries for filtering, joins, aggregates, CTEs, windows and dates, with plain-English notes.
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.
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.