Every analytical question lives in one of three layers. Confuse them and you get answers to questions nobody asked.

## Layer 1: Descriptive — What Happened

Descriptive analytics names a fact. It doesn't explain it.

><b>Revenue was $2.4M in March, up 8% from February.

This is where most dashboards live. Useful as ground truth, useless as a decision on its own.

The trap: describing data and calling it analysis. **"Revenue is up"** is not an insight — it's a starting point.

## Layer 2: Diagnostic — Why It Happened

Diagnostic analytics finds the cause behind the number.

><b>Revenue grew 8% in March because enterprise expanded 14% — SMB actually shrank by 6%.

Same fact, now broken open. This is where most real decisions begin: not from the headline, but from the breakdown that explains it.

The trap: stopping at the first plausible cause. Diagnostic work requires drilling — one level of segmentation is rarely enough.

## Layer 3: Predictive — What Will Happen

Predictive analytics projects a number forward under stated assumptions.

><b>If SMB churn keeps climbing at 6% per month, total MRR flattens in Q3.

This is the layer that drives action — it forces a position. Will the current trend hold? What breaks first? What changes the curve?

The trap: predicting confidently on noisy data. A confident prediction without a stated assumption is a hallucination in a suit.

## Mapping Questions to Layers



AI assistants are strongest at layers 1 and 2 and need careful framing for layer 3.



A trend, a model, and a stated assumption.

**Takeaway:** Before asking any analytical question, name its layer. The layer determines what kind of answer is even possible.

A hands-on course for non-technical managers and operators who need to turn raw business data into decisions, not summaries. You'll learn how to frame analytical questions sharply, read spreadsheets and internal docs through AI, combine internal numbers with market context, and build your own analyst agent that produces decision-ready briefs end-to-end.

Introduction to business analytics through the lens of AI — why raw data doesn't make decisions, the three layers of analytical questions, and where an AI assistant actually fits in the workflow.



Train the framing reflex. Turn vague business questions into sharp ones, pick the metric that actually matters, and lock down time windows, segments, and comparisons before touching any data.



Hands-on labs with the three sources every operator hits: sales spreadsheets, strategy docs in Notion, and market context from the web. Single-tool labs first, then a combined investigation.



Move from running labs to building your own. Write the persona, wire up the tools, and create an agent that delivers weekly KPI briefs and pre-meeting decision packs — finishing with a capstone Quarterly Business Review.



The Three Layers: Descriptive, Diagnostic, Predictive

Layer 1: Descriptive — What Happened

Layer 2: Diagnostic — Why It Happened

Layer 3: Predictive — What Will Happen

Mapping Questions to Layers