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Read More →Download the business problems for data analysts PDF from Uncodemy: how to turn vague questions into measurable analysis, with worked examples.
This page offers Uncodemy's data analytics business problems guide as a PDF download. Analysts are rarely handed a clean question. This guide shows how to turn a vague request such as sales are down into a precise question, a metric, a plan for breaking the problem down and a recommendation, with examples of the questions interviewees ask.
Download the complete problem-framing guide as a PDF. Fill in the short form and the file will download automatically.
A problem-framing framework that turns vague requests into measurable analysis, with worked examples, SQL patterns and interview-style practice.
Managers rarely say please calculate monthly churn by cohort. They say customers seem to be leaving, or marketing is expensive, or why are deliveries late. Your first job as an analyst is to turn this into a question that data can answer.
If you skip this step you risk spending days producing something technically correct that does not help. Framing is also a skill that interviewers test, because it separates people who can run queries from people who can solve problems.
| Metric | Break it down as | Where to look |
|---|---|---|
| Revenue | Customers × orders per customer × average order value | Which factor changed? |
| Profit | Revenue minus costs, by product line | Is the issue price, volume or cost? |
| Churn | By signup cohort, plan, channel and usage | Which group leaves fastest? |
| Conversion | Funnel steps from visit to purchase | Where is the biggest drop-off? |
| Delivery time | Order to dispatch plus dispatch to delivery | Which stage is slow? |
Suppose the request is sales are down this month. Clarify first: down compared with what, last month or last year, and by how much? Define the metric: net revenue excluding returns.
Break it down: revenue equals number of orders times average order value, so first check which of them fell. If orders fell, split by channel, region and new versus returning customers. If average order value fell, check discounts, product mix and basket size.
Each branch suggests a cause to test: a stopped campaign, a stock shortage, a competitor's offer. The analysis then follows the branch with the largest drop.
A SQL pattern comparing two months. Table and column names are illustrative.
SELECT strftime("%Y-%m", order_date) AS month,
COUNT(*) AS orders,
ROUND(AVG(order_total), 2) AS avg_order_value,
ROUND(SUM(order_total), 2) AS revenue
FROM orders
WHERE strftime("%Y-%m", order_date) IN ("2026-08", "2026-09")
GROUP BY strftime("%Y-%m", order_date);| Topic | Question |
|---|---|
| Scope | Which product, region or customer group is affected? |
| Time | When did it start, and is it seasonal? |
| Definition | How exactly is this metric calculated here? |
| Comparison | Compared with what: previous period, last year or a target? |
| Actions | What decision will this analysis support, and who decides? |
| Constraints | What data exists, and by when is the answer needed? |
State your understanding of the problem and ask one or two clarifying questions. Name the metric and its components. Lay out a short plan: the data you need, the cuts you would make and the order in which you would look. Say what you would conclude under each result and what recommendation might follow.
Speak in structure, for example first, second, third, because a clear structure matters more than a perfect answer.
Practice with everyday examples: why is a local shop busy on certain days, why did an app update annoy users. Write the metric, the breakdown and the data you would need. Doing this ten times will make the framework automatic when a real problem appears.
| Part | One line to write |
|---|---|
| Problem | Restate it in one sentence with the metric and comparison |
| Metric | Name the formula and its components |
| Hypotheses | Two or three likely causes |
| Data | Tables and fields you need |
| Analysis | The cuts you would make and in what order |
| Recommendation | Action, owner, timing and how success is measured |
Suppose orders on a food-delivery style app fell ten percent week on week. A structured answer might run like this. First confirm the drop is real, not a tracking fault, by checking another source such as payments.
Then split orders by platform, city and new versus returning users to see where it is concentrated. If the drop sits in one city, look at supply issues such as fewer delivery partners, a local event or weather. If it sits in new users, look at marketing spend and conversion in the sign-up funnel.
Each branch has a clear data check, and your final recommendation follows the branch the data supports.
Ready to solve real business problems with data? Explore Uncodemy's Data Analytics course and learn from industry trainers.