A good book often has a good subtitle. All Data Are Local has a good subtitle:
Thinking Critically in a Data-Driven Society
In the book, Yanni Alexander Loukissas uses four projects to discuss six lessons that help you become a better data practitioner. While reading the book, there were a few that resonated with me more than others. I’ll share a few of them with you in this post.
Data are plural
I am someone who tends to say data is instead of data are. I have noticed the difference in the way people use data in their sentences. At first, I thought it was just a preference thing. But after reading it in both this book, other books, and hearing about it talks, I slowly learned it’s not. Handling data as a plural concept is a better representation of what data are. Besides being a literal plural from the Latin datum, I now see a different reason to change the way I use it. Using data as a singular concept hides some of it’s complexity. Singular tends to work well with the misconception that data are objective, they are neutral, that ‘the data tells the story’. But data
is are hardly any of those things. They are complex.
So from now on, I’ll try to be a pluralist.
Moving backwards from your data set
The other thing that keeps coming back to me is the way I use data. I am a data practitioner. The starting point for a new project is often a data set. When I start working, I mostly move forward towards some sort of product (e.g. a report) that is based on the data. I hardly ever look back.
Now I do ask my client a thing or two about the data. But Yanni’s story made me realise that’s not enough. He shares a lesson that is really important:
Don’t just look at the data set, also look at the data settings
What I learned from this, is that I can spend some more time looking back at the origin of the data. There are various questions that can be looked into:
Why were they collected?
Who collected them?
Who was it collected from?
What data were not collected but is related?
What happened to my data before I received them?
Moving forward is easy, but moving backwards is important. And according to Yanni, it will also help you get a better understanding of the data, it’s societal meaning, and it’s potential value.
Friction is good
The last lesson that I enjoyed reading through was about friction. When I make data projects, I often try to extract a story from the data and turn the story into a nice clean and simple visual. But just as data are plural, data are often messy. There’s a complexity to the underlying data of a story. By incorporating some of that complexity into your story, the reader of that story will experience something that’s closer to nature of the data. Maybe there are some missing data? Maybe there are ways that the data were tweaked to make it presentable? I should think about ways that the complexity of data and process of transforming it into a visual can be part of the end product.
Walking back toward the data origin
A few years back, a speaker at an event shared some advice that could have seamlessly fit in this book:
Take time to hang out with your data
It’s something that I like doing. Hanging out with data helps you develop a better understanding of it. And All Data Are Local made me realise that I should walk the path from my data set to the data origin more often. It ‘s a nice and twisty path to becoming a better data practitioner. It’s an idea I tried to capture in this visual:
It’s inspired by both the book’s focus on data settings and the design of the cover.
p.s. after my first draft of this post, I had to change data is to are about 10 times. Maybe there is still one in there. It’s sometimes hard to unlearn and relearn a habit.