When Quantification Loses Its Meaning

Are you measuring just to measure?

Megan Dibble

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Dyson vacuums display analytics now.

Source: Dyson

My first reaction to seeing this as a data enthusiast was, “Yay, data!” And then immediately after, “but… why?”

What am I supposed to do with this detailed information about the microns of dirt I am sucking up?

When we throw numbers out into the world — to our project stakeholders, to our users, to anyone — we want them to be meaningful. We want our analysis to drive change, empower our business, and create an impact.

So when does quantifying lose its meaning?

When there isn’t a clear message

Giving numbers and metrics to your audience without a message is like putting ingredients on the table when you were supposed to cook dinner.

Source: Matillion

Aside from the above dashboard not being the prettiest, a lot of information is thrown at the viewers. And what it needs is a message for the audience. While some reds and greens might indicate bad vs. good, it’s unclear what numbers are in the range of what we would expect, what numbers we should pay attention to, etc.

This dashboard is also trying to answer too many questions at once — it would be challenging to get any message across when using this many data points on different subjects.

When qualitative data provides insights

In the past, I thought that business decisions could only be justified if there was quantitative data behind them. Over the last year, I’ve seen more situations where qualitative data has made an impact.

Take, for example, comments on social media content for your product. You could take all the comments, analyze sentiment, and report on a positive sentiment score.

Photo by abillion on Unsplash

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Megan Dibble

Data Journalist @ Alteryx. I mostly write about data science and career advice. Occasionally I’m funny. Find me on LinkedIn!