Welcome to Part 2 of our “Actually Useful Maths for Data Work” series.
Written by our very own Anthony Fawkes, this five-part series will provide fundraisers, data managers, analysts, and CRM heroes holding charity operations together with easy-to-apply solutions that strip away the stress from maths. The aim is to show you how everyday calculations (and a bit of logic) can level up your data confidence.
Why Knowing Your Way Around A JOIN Means You’ve Got This
You know what’s funny? I’ve met plenty of database pros who can optimise a query faster than you can say “LEFT OUTER JOIN”- but the second someone asks for a median or a percentage change, they freeze. And I get it. “Maths” still carries the weight of exams, teachers, and those weird word problems with two trains leaving different stations. But here’s the thing: if you’re working with data, especially in fundraising, you’re already doing the hard bit. You’re thinking logically, spotting patterns, and translating raw numbers into something useful.
Quick Fixes That Change Everything
- Averages That Don’t Lie Let’s say a board member asks: “What’s our average donation?” You check the data. It’s £250. Except it isn’t. That figure’s been inflated by a handful of major donors.Most people gave £20. The median? £22.
- Spotting Drop-Off Before It Happens
You’re tracking monthly donor retention. It looks steady on paper – around 70%. All good? Maybe not. By finding the mode you might spot that Month 3 is when things fall apart. - Turning Raw Numbers Into Meaning
Answering questions like “Are we raising more from companies than last year?” or “Is this event pulling its weight?” isn’t about gut feeling-it’s about percentage change.
You’ve Already Got the Brain for This
You debug weird data issues. You think in logic. You spot patterns others miss. That is maths – it’s just not how we were taught to think of it.
How These Look in SQL, Excel, Power BI DAX
| Calculation | SQL | Excel Formula | Power BI DAX |
|---|---|---|---|
| Mean (Average) | AVG(DonationAmount) | =AVERAGE(A2:A100) | AVERAGE(‘Table'[DonationAmount]) |
| Median | Depends on SQL flavour | =MEDIAN(A2:A100) | MEDIAN(‘Table'[DonationAmount]) |
| Mode | Use frequency logic | =MODE.SNGL(A2:A100) | Use MAX + COUNTROWS or visuals |
| % Change | Custom calc | =(B2-A2)/A2*100 | DIVIDE([Current]-[Previous],[Previous],0)*100 |
| Sum | SUM(DonationAmount) | =SUM(A2:A100) | SUM(‘Table'[DonationAmount]) |
| Count | COUNT(*) | =COUNTA(A2:A100) | COUNT(‘Table'[Column]) |
A Word on Python
If your team’s embedding analytics into internal processes – things like automated retention flagging or income forecasting – Python helps automate logic, connect to CRMs, and build useful workflows.
TL;DR: You’ve Got This
If you can write SQL, you can understand percentages. If you can debug a NULL, you can spot a dodgy average. The difference between an “OK” report and a “let’s act on this” report? Often, it’s just the right metric.
Coming Up Next in the Series…
- Post 3: Forecasting for Fundraisers – Turning Gut Feelings into Data-backed Plans
- Future Topics: Attrition Modelling, and Python Automation
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