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Python & Django

Getting Answers From Data You Already Have

Practical data analysis for a small business with thousands of records: answering real questions by counting, grouping and comparing, then presenting it.

Updated 2 min readBy SpiderHunts Technologies

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Quick answer — TL;DR

Most small business questions are answered by counting, grouping and comparing. Sophisticated methods on small data produce confident answers that are mostly noise.

Counting answers most questions

Which customers are worth most, which products actually make money, where orders come from, what changed this quarter. Those are counting and grouping questions, not modelling ones.

The analysis that changes decisions in a small business is nearly always simple. The sophisticated work is usually a way of avoiding a conclusion the simple work already gave you.

Questions worth answering

  1. Which customers generate the most profit, not the most revenue?
  2. Which products are actually profitable after returns and handling?
  3. Where do good customers come from, as opposed to all customers?
  4. What is the repeat rate, and how has it moved?
  5. What changed between this period and the same period last year?

Small data needs care

  • One large order distorts a monthly average
  • Seasonality dominates in most businesses
  • Small samples produce large apparent changes
  • Correlation with fifty records is usually coincidence
  • Compare against the same period last year, not last month

Where the data actually is

SourceContains
Accounting systemRevenue, costs, customers
Order or job systemWhat was sold and when
Website analyticsWhere visitors came from
CRMThe sales process
Support systemWhat goes wrong and how often

Combining two of those usually produces the interesting answers, and it is frequently the first time anyone has looked at them together.

Present it so it changes something

An analysis that produces a chart and no decision was not worth doing. State what you found, what it implies and what you would do differently.

One clear finding acted on beats five interesting observations filed away.

FAQ

Frequently asked questions

The questions readers ask us after this guide.

Still have a question?

Ask us directly — a senior engineer will get back to you.

Ask about your project

Do we need a data warehouse?

Not at small business volumes. Exporting from two systems into one place for analysis is frequently sufficient.

Should we hire an analyst?

Usually not before there is a recurring set of questions worth answering. Start with a specific question and see whether the answers change decisions.

How much data do we need?

Enough to see a pattern beyond noise. For seasonal businesses that means at least two full years.

What tools?

Python and a spreadsheet cover most small business analysis. Sophisticated tooling rarely changes the conclusions at this scale.

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