Module 9 · Data Analytics Foundations
From question to insight
A repeatable method for turning a vague business worry into a clear finding and a recommendation.
About 30 minutes
The problem
The sales director at Kolanut says: "Something's wrong in the North. Can you look into it?"
That isn't a question you can answer with data yet. This lesson is about the method analysts use to get from a worry like this to a finding someone can act on.
The concept
The question-to-insight method
- Clarify the business question. Who is asking, what decision are they facing, by when?
- Make it measurable. Name the measure, the comparison and the period.
- List the data needed, and check it exists.
- Analyse: start broad, then break down. Compare with a baseline.
- Explain the pattern: test possible reasons against the data.
- Recommend something specific, and say how you'd know if it worked.
Finding vs insight. A finding says what the data shows ("North West revenue fell 47%"). An insight adds why it matters and what to do ("North West shops are ordering half as often; if we win back the old order frequency, we recover about ₦14m per half-year").
Test more than one explanation. For a fall in revenue, the usual suspects are:
| Possible cause | What you'd see in the data |
|---|---|
| Fewer customers | Fewer distinct customers ordering |
| Customers ordering less often | Same customers, fewer order lines each |
| Smaller orders | Fewer packs per order line |
| Lower prices or bigger discounts | Lower price per pack or higher discount % |
Example
1. Clarify. The director is deciding whether to replace the North West sales approach before the next half-year budget.
2. Measurable question. How did North West revenue in January–June 2026 compare with January–June 2025, and what drove the change?
3. Data. orders.csv (dates, quantities, prices) joined to customers.csv (region).
4. Analyse.
| North West | H1 2025 | H1 2026 | Change |
|---|---|---|---|
| Revenue (₦m) | 31.1 | 16.6 | −46.6% |
| Customers who ordered | 10 | 11 | +1 |
| Order lines | 176 | 91 | −48% |
5. Explain. Customers didn't leave: 11 ordered in 2026, one more than in 2025. Prices rose in January 2026, so price cuts aren't the cause either. The fall is almost entirely order frequency: the same shops ordered about half as often.
6. Recommend. Find out why North West shops are ordering less often. Ask the regional rep and call the five largest accounts this month: are they buying from a competitor, or is delivery unreliable? Set a target of 150 order lines next half-year and track it monthly.
Walkthrough
Notice what the analysis did not do:
- It didn't stop at "revenue fell 47%". That's a finding, not an explanation.
- It didn't guess. Each explanation was checked against a number.
- It didn't claim more than the data shows. The data says what changed (frequency); only a conversation with customers can say why. A good recommendation names the next question as well as the next action.
Practice
Practice
Check the analysis yourself. How many order lines did North West customers place in January–June 2026? (Look up each order's customer region, then count 2026 order lines for North West.)
Practice
Which region's revenue grew by the most naira from H1 2025 to H1 2026? Use the table in lesson 6 (Data analysis).
Check your understanding
Answer every question to check.