Free guide: career evidence
A procurement analytics portfolio project you can show in an interview
Your employer's spend data is confidential, so you cannot show it. A hiring manager still wants proof that you can reconcile data, build controls analysis and write a recommendation. A labelled practice project on fictional data gives them that proof without risking your job.
What employers at large corporates look for
- Every number traces to a formula or query someone else could check.
- Numbers reconcile before they are presented.
- Definitions are written down and used consistently.
- Findings, hypotheses and recommendations are kept apart.
- Limits of the data are stated, not hidden.
Showing those habits on practice data says more than a list of tools on a CV.
The five-item evidence pack
- A data dictionary: grain, currency, period and limitations of the dataset.
- A reconciled workbook with visible checks and a repeatable refresh.
- A report showing both spend-weighted and count-based PO measures.
- A one-page decision memo that separates findings, hypotheses and proposed actions.
- A short error log: one mistake you found, what it would have caused, and how you fixed it.
A truthful project description
"Built a practice procurement analytics model using a fictional 150-invoice dataset. Reconciled NGN 552,660,500, distinguished 82.03% spend-weighted PO coverage from 70.00% invoice-count coverage, and proposed evidence-led control reviews."
Never turn that into "managed NGN 552 million of spend" or "saved 38%". No business outcome happened, and an interviewer who asks one follow-up question will find out. Label the work as practice everywhere: on the CV, in screenshots and when you talk about it.
An interview answer that shows judgement
"I first considered adding non-PO and non-contracted exposure, found the overlap, and used a cross-tab instead. I also limited the recommendation because the data had no approval timestamps."
Talk about the decision and the evidence, not the tool. The overlap point is explained in the controls guide.
How to build it, in order
- Get a realistic practice dataset with known answers, so you can check your own work.
- Import it with Power Query and reconcile the total before any analysis.
- Build the core KPIs and the PO by contract matrix, and test them against expected values.
- Find one finding that changes a decision, such as a single invoice driving a large share of off-contract spend.
- Write the one-page memo, with the limits of the data stated.
- Publish screenshots with the fictional labels kept on, and the error log alongside.
Using AI to write it up, safely
AI can draft CV bullets from your verified findings, as long as you tell it the data is fictional and forbid it from implying an employer, savings or a credential. The prompts guide includes the exact prompt.
Go further
The Starter Kit teaches this method end to end on the practice dataset: a 54-page guide, a click-by-click Excel and Power BI build guide, 29 exercises and 3 case studies with worked answers, 28 AI prompts, a self-checking workbook and a 29-KPI dictionary.
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Free one-page KPI cheat sheet: download the PDF, no email needed.