Data analysis

Make business reviews clearer with the right analysis

Imagine leaving your next review with a result the team can explain and a useful next step. Choosing a data analysis method that fits the question can help you get there, provided the records are complete and the limits are clear.

01

Get an answer the team can use

A useful review moves from a page of figures to a shared understanding of what needs attention. Start by writing the question in one sentence, then agree what the answer will help someone do. That keeps the analysis focused.

  • What happened? Summarise totals, rates or a typical value. This is descriptive analysis: it summarises the records you have.
  • Where should we look for an explanation? Break the result down by product, stage or period. This diagnostic work can identify patterns to investigate; it does not establish a cause.
  • How do groups differ? Compare the same measure over matching periods. Check group sizes and what each group contains before judging performance.
  • What might happen next? Try a forecast using earlier records, then check it against later results that were not used to build it. Keep uncertainty visible.

Use numbers for amounts and rates, dated records for trends, and notes or conversations to explore possible explanations. Five selected orders cannot describe every order, however complex the calculations. Missing records are not zeroes.

02

Compare branches without blaming the wrong one

Synthetic example, not client results: a retailer wants to focus its review of returns. For the same sales period, Branch A sold 200 units and received 20 back. Branch B sold 600 and received 30 back. Each returned unit is counted once, within the same 30-day window after its sale.

Branch B has more returns, but Branch A has the higher return rate: 10% compared with 5%. Dividing returned units by sold units makes the difference visible. The team now has a useful place to start its review instead of ranking branches by return counts alone.

That comparison does not show that Branch A provides worse service. Product mix, customer needs or other differences may matter. Check which products were returned and review the recorded reasons before deciding what to change. As NIST explains, an association does not prove cause and effect.

If one branch's return records are incomplete or its 30-day window has not ended, mark the comparison incomplete. A low recorded rate is not yet evidence of better performance.

03

Make the next review easier to act on

Use the question-to-method worksheet to record the question, available information, result, limits and person responsible for follow-up. It includes this retail example plus manufacturing and professional-services examples. All figures are invented; save or print the worksheet to plan your own analysis.

Datimore can help build business analysis and reporting around that routine. Our commercial performance case shows business measures and their context brought into one view; it does not prove the results of this example. Bring one recurring question and its source records. The aim is a review where people understand the answer, know its limits and can agree what to do next.

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