You can explain the evidence, the assumption underneath it, and what would make you change the recommendation.
FOR PEOPLE WHO HAVE TO DEFEND THE ANSWER
Walk into the meeting knowing why you trust the answer.
AI can produce a polished analysis in minutes. Your job is deciding whether the question, evidence, definitions, assumptions, and recommendation are strong enough to act on.
AI suggests. Analyst decides.
◯ AI selects an answer. ✕ You challenge it. ◯ You decide what actually matters.
Get to a credible first answer sooner, without rebuilding every first pass by hand.
Find the definition, exclusion, context, or missing fact that could overturn the result.
Move from an interesting pattern to a decision the business can actually use.
Explain what you know, what you checked, and what could still change your mind.
THE MOMENT THIS IS FOR
The analysis is finished. The decision is not.
You do not need another dashboard to tell you the result looks convincing. You need a reliable way to challenge the answer before somebody else does.
Before it goes into the deck, you know what the model could not know unless you supplied it.
You check whether a definition, process, source system, or reporting rule changed before you explain the pattern.
THE MECHANISM
Seven checkpoints between a plausible answer and a defensible decision.
The Seven C's of Successful Data Analysis gives you a repeatable place to look when an answer feels finished but the decision still carries risk.
Choose the right question.
The decision the analysis must support is never stated, so everyone can answer a nearby question beautifully.
THE METHOD IN REAL LIFE
My Netflix model was mathematically right and commercially wrong.
The deciding cost lived outside the dataset. More analysis of the data I had would only have made the wrong answer more convincing.
Watch analyses like this free →Streaming delivery cost pennies compared with mailing DVDs, so the model valued them highly.
Some streaming agreements triggered content payments that my dataset could not see.
No amount of additional analysis on the data I had would have found it.
WHY THIS IS MORE THAN AN AI WORKFLOW
The analytical method predates the current AI cycle. The Accidental Analyst was already being taught as a way to think.
Tableau co-founder and Stanford professor Pat Hanrahan put the book at the center of a Tableau Conference presentation and discussed its influence on how he approached teaching analytics at Stanford. The tools are changing. The need to know why you trust the answer is not.
WHEN YOU WANT ANOTHER SET OF EYES
Bring the analysis you need to defend.
The free series teaches the method. The Working Lab applies it repeatedly to the forecast, dashboard, recommendation, or AI-assisted answer already on your desk, while the decision can still change.
AI explanation: slower pipeline conversion is the primary driver.
- Stage definition changed in July.
- Two enterprise renewals slipped in timing but are not lost.
- The AI summary treated both effects as conversion decline.
Rebuild the comparison on a consistent stage definition, separate timing slippage from true loss, then decide whether conversion actually deteriorated.
Two live 75-minute sessions every week. Attend either or both.
FROM THE WORKING LAB
What participants found once they went looking outside the frame.
YOUR NEXT ANALYSIS
Before the answer leaves your hands, know what would change your mind.
Learn the Seven C's free. If you want live judgment applied to the work you already own, bring it to the Working Lab.