Most People Like Fruit: the importance of data disaggregation.

I’m going to explain, using a fruit bowl, how we’re at risk of masking real problems — or worse, completely overlooking them — when we default to an aggregated view of data.

The fruit bowl

A bowl of fruit is put out in an office common area. At the end of the day, if there’s fruit left, it’s thrown out. The same fresh selection is put out the next morning. The fruit bowl programme is piloted for five days.

  1. There’s only kiwis left each day, therefore people must not like kiwis.

Deciding the fruit bowl’s fate

Budget cuts are introduced around the time the pilot finishes and the fruit bowl programme is put under intense scrutiny. The finance department looks closely at the numbers to see if it should be kept or cut.

Table 1
Graph 1
Table 2
Graph 2

So what?

The overall data in Table 1 is an aggregated view while data by fruit-type in Table 2 is disaggregated.

  • Disaggregated is when data is broken down into smaller units or sub-categories, so we can see unique differences that aren’t reflected in the aggregated view.

Data disaggregation in real life

In 2007, the World Health Organization (WHO) reported that data was rarely sex disaggregated when it came to epidemic-prone infectious diseases, limiting the understanding gender dynamics, identifying vulnerable groups, and developing appropriate responses.

Why isn’t data always disaggregated?

Let’s look at the fruit bowl example again. In Scenario 1, the programme director collected a high-level view of what was going on with the fruit bowl. In Scenario 2, they collected a more detailed view, broken down by fruit type.

Using both sides of my brain. Research, data analysis, data visualization, and illustration. 📈✏️💭

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