Averages on its own can be dangerously misleading.
KPI metrics are incredibly useful. They turn thousands of raw numbers into one meaningful indicator of performance. But while data aggregation is often the key to clarity, too much of it can actually blur the picture.
Average margin. Average daily revenue. Average monthly visitors. These are all common metrics, and they can give you a sense of what’s “typical.” But if your data has even a few big outliers, the average gets pulled in their direction and becomes meaningless.
Let’s say a billionaire walks into a room full of school kids. On average, they’re all millionaires. Except none of them even know how to count to a thousand. The average doesn’t tell the truth, the middle between extremes.
If your business has a few huge orders or a handful of outlier products, the average will mislead you. You’ll get a nice round number, but it won’t reflect reality.
If you want to prevent distortion, here are a few alternatives:
1. Add a histogram: A histogram shows how your values are distributed. It lets you see if the data skews toward one side or if there are multiple peaks. This gives you context, something the average alone can’t do.
2. Break it down by category: If one product category behaves very differently from others, don’t lump it all together. Show the average per category instead of a single total average. Segmenting often reveals patterns you’d otherwise miss.
3. Use the median: The median shows the middle value – the one that splits your data in half. It doesn’t get pulled around by extremes, which makes it much more reliable in datasets with outliers.
There’s no perfect solution. It’s worth trying different approaches and seeing which one gives you the clearest picture for your specific situation.
Don’t let a single number speak for your entire dataset. Especially not when that number can be so easily distorted. By using medians, segmenting your data, or showing how values are distributed, you’ll avoid being misled and thus avoid bad decisions.
