Common Sense in Data

Later today (12pm ET), I'm joining my friends Robin Hunt and Gini von Courter for our monthly LinkedIn Live sessions we call "Data Fundamentals Unpacked". Today's topic is "Common Sense". And it got me thinking...

"Common" means shared by most, nearly all, people. "Sense", in this case, is about?judgement, understanding, basic principles. But here's the thing, there really is no "common" sense in data communications. The fact is, aside from 1 + 1 = 2, which pretty much everyone does know, just about everything else in data understanding is on a HUGE spectrum.

So if you're working with data, you can't assume that literally everyone in your audience will understand what you mean when you say "the correlation between these two variables is strong", for example. What does correlation mean, exactly? And define "strong". Depending on your audience's data literacy and the context, there may be potentially zero understanding of what that means all the way up to great expertise that could lead to a spirited debate about your statement.

Audiences can be very diverse. And you need to know how diverse they are, what is "common" to them, and what might require more explanation and hand-holding for your content to be successful with them.

So how do you figure this out? You need to talk to your audience and discover their level of data literacy so you know how much explanation and context to provide when you speak to them about data.

To be safe, if you're not absolutely certain they're as data literate as you need them to be to understand every single little detail in whatever it is you've prepared for them, create a nice rich deep appendix for your content that includes all of that information so they can learn what they need to learn right within your content, rather than hoping they'll go somewhere else to learn it and then come back to your information later.

And most importantly, do not suffer from the curse of knowledge. To you, correlation is like white noise. It's so obvious that you literally might not even think of the idea that it's a concept that not everyone knows. You have to look at your work VERY critically and think VERY carefully about every little thing with a "beginner's mind" to try to detect what might be old hat for you, but could be potentially novel to someone else. Does your audience know how to read a scatter plot? Do they understand what GDP stands for? Do they know why you're emphasizing that the median value is higher than the mean, and why they should care?

Just remember "common" sense really isn't so "common", after all. And remember that you have "uncommon" sense when it comes to data. Be extra considerate of your audience, try to understand what they know and what they need from you, and do your best to provide it in as helpful and unobtrusive a way as possible. This will help them make "sense" of what you are sharing with them and lead to a "common" understanding of your data, which is your goal, after all!

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