Hello! As you may have noticed from my Reading List page here, I like to read. Recently, with the new job, I was looking for a book that talked about Data Visualization. While searching, I came across “Storytelling with Data”, and it was not the first time I saw it. After checking a few reviews, I decided to invest my time reading it. Turns out it was a great decision! I liked it so much that I wanted to talk about it here, so here it comes, grab your reading glasses.
Who should read this book?
I believe this book is great for beginners to BI and Data Visualization. However, it does not require any fancy tools. In fact, the author claims to have used Excel in all the examples. In short, if you work with and have to present data to others, this will be a valuable reading.
What’s covered in this book?
In some of the main chapters, you’ll find information about how to pick the right design for the story you are trying to tell. Bellow is a very brief summary for some chapters covered.
The importance of context
Sometimes we get so into the analytics part of the visualization, the we can easily forget the context behind it. Which is why it’s good t think about who’s this data for, what they are planning to do with it and which questions are trying to be answered by this viz. If you don’t considered this, then you’re just plotting numbers on a report that people will not know what to do with.
Key points: engage with the audience, leave some prompts with call to actions that will guide them to where they should look to understand what is being presented.
Sometimes, the shine things are not the best. As a BI dev, you may want t go crazy and try something that is rarely used, because it’s cool. However, when you do this, you let go of the main objective of your work: you’re not doing this for yourself. Somebody or some team requested this, and people are more at ease with things they already know how to operate. If they only use excel and visualize data with tables, they may not be interested in seeing a area or stacked bar, because that translates to extra brain effort.
Key points: avoid pizza and donuts charts always, because our eyes can misinterpret them. When possible, stick to familiar forms for the audience (bars, line graphs etc).
This is a hard one for me. I find it one of the most difficult things to do, which is eliminate what is unnecessary. If you want to tell too much at once, you end up not telling anything at all. It’s a good practice to keep it short, keep it simple. Answer what is being asked, and remember you do not need to make the audience take the analyzing journey with you. That was your job! Their precious time will be used to see the final results. You can brag about the hard work you do in a meeting with your managers, for example (performance reviews exists for this!)
Key points: Only show what’s important for the context of the story, remove repetitive things and summarize when possible. Even considere plain big old numbers to highlight very specific topics instead of showing a whole graph for it (for example, when showing a product price range through time, instead of using a graph, just say “Product X had an increase of XX% in x years.”)
When looking at a data visualization, it’s easy to get lost. If it’s too colourful, too bright, or dull, you may not know what is the key point being presented to you. As mentioned, the audience is not there to go through the analysis with you. That part should be done and you can call out to the audience’s attention by using:
- color: you could tone everything down, for example by making use of gray tones, and use 1 single color that would pick someone’s attention because it stands out so much.
- bold letters: bold letters are good because they don’t make your viz look clutter as underline may do, and are more perceptive than italic.
- sizing: you can make things bigger to call attention, just make sure it’s an appropriate size.
Think like a designer
Wearing your design hats! She mentions in the book that people tend to think that beautiful is in fact perceived as more efficient. This may be a neglected point when you don’t have much time to put into the design, but taking this extra step shows respect for your audience and your data
Key points: who doesn’t like pretty things???
Storytelling is what ties it all together. You could have done a great analysis, found awesome stuff about the data, presented it beautifully, but if you’re missing the context because you can’t put it into a “story”, and just like that, you may have lost the audience.
Heard of Death by Powerpoint? Yeah. Creating a narrative that guides the people to your findings, using their language, showing the steps, involving them and asking them to follow along with you is important.
Key points: she really got me thinking about the prompts we can make to the audience. Using questions is an awesome way of inviting people to think about what they are seeing. And when that is narrated by a story, it’s more compelling. It’s hard to follow numbers on a page, it’s easy to see a story develop itself in front of you.
Would I recommend this book?
It’s clear by now that I loved it. It feels like I took a shortcut to avoiding some headaches that would have come with experience in BI. With that being said, don’t go in thinking you’ll see technical stuff about how to actually create vizes, specially with a tool like Tableau or Power BI, there is nothing in there about this.
Cole has a blog called (brace yourselves) Storytelling with Data! It’s full of resources and has more info about the workshops and material they have.
That was it for me. I’m excited to check books that were mentioned on this one, and if they’re interesting I may write a review for them too. See you soon!
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