When Quantification Loses Its Meaning
Dyson vacuums display analytics now.
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.
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.