“I know Excel and Power BI” is a start. “Here is a small reporting problem I solved, and here are the checks I used” gives an interviewer much more to ask you about.
If you are a student, a recent graduate or an accountant moving towards analysis, you can build that second conversation without access to a company’s confidential records. Use a simple fictional business and make the reasoning visible.
Choose a question worth answering
Imagine a small distributor selling three product groups. Sales are rising, but the owner is unsure which group is contributing enough gross profit. Your project question is: where should the owner investigate margin performance?
Create a synthetic dataset covering several months. Include date, product group, units, sales value and cost. Keep the definitions clear. Gross profit in this exercise is sales less the included product cost; it is not the same as cash generated or net profit.
Make the raw data slightly imperfect
Introduce a few deliberate issues, such as inconsistent product names, an empty category and a date stored as text. Keep a separate note of the issues you planted. This lets you show that you can detect and handle problems, not just format a clean spreadsheet.
Do not create so many exceptions that the project becomes a data-cleansing marathon. The aim is to demonstrate a complete, understandable process.
Use Power Query for repeatable preparation
Microsoft describes Power Query as a way to connect to and transform data using recorded steps. Those steps can be applied again when the query runs, without rewriting the source data.
For this exercise, standardise names, set the appropriate data types and decide how to treat missing fields. Keep an exception table rather than silently dropping troublesome rows. Add a short explanation of each important decision.
You can begin with an Excel table or CSV. Microsoft’s import guidance explains the available starting points. Features and connectors vary across products and versions, so check your own setup.
Prove the numbers before building the dashboard
- Compare the raw row count with the prepared data and explain any difference.
- Reconcile total sales and cost before and after transformation.
- Check that every month appears and the date range is complete.
- Confirm the margin calculation on a few rows manually.
- Make missing or excluded records visible.
A small control sheet can make the project more convincing than an extra page of charts. It shows that you understand how an attractive dashboard can still be wrong.
Keep the first report to one page
Use Excel or Power BI to show sales, gross profit and margin by month and product group. Add a clear period label and explain the units. If Power BI is new to you, finish the Excel version first and then reproduce the same definitions in your model.
Write three observations and one question for the owner. For example, a product group’s sales may be growing while its margin rate falls. That is an observation. A supplier price increase is only a possible explanation until you have evidence.
Add AI without handing over the judgement
You could ask an approved AI tool to challenge your draft commentary or suggest questions you missed. Use your synthetic summary and check the response. Include one example where you corrected or rejected a suggestion. Do not describe the exercise as an automated forecasting system.
Package it for a useful conversation
Share the project brief, synthetic dataset, preparation steps, checks and a screenshot or exported report. Label the business and data as fictional. Explain what you would need to verify before adapting the approach to a real organisation.
Tell Finaxil which finance skills or career path you want to explore. Mention the project you are building and the step that feels hardest. That gives us a practical starting point for future learning and support discussions.
This is an original learning exercise. It is not a professional certification, an employer assessment or a guarantee of an interview.
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