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Why sketching can make you better at communicating data, Featuring Data Sketches.

written by human, not by AI

Like anyone into data visualisation, I love taking a peek at other practitioners’ processes. There are countless ways to go from raw data to a curated visual, and it’s always insightful to see what bits and pieces are similar or different in the ways we work and how that can influence the end results. As I wrote before, I also love people who try new approaches to data visualisation beyond the tested, tried and pragmatic ways of portraying data on screens. This includes alternative and novel ways of visualising data, be it digitally or physically, and even data art.

Even if it may seem like standardised data viz, such as business dashboards, and outrageously alternative data art have nothing in common, they are more alike than you’d think! Both styles work from data; both go through a process of discovering, asking questions, and investigating potential hypotheses. Both styles have to work through complex, dirty and incomplete data. Both styles consider how to use visual symbols to encode data. Both styles think of their potential audiences, what may interest them and how to best catch their attention while providing them with information. What differs is the style they choose to go along these process points and what the output ultimately looks like.

And there are heaps of things both sides of the data visualisation spectrum add to the field and can learn from one another. Dismissing either is like choosing just one flavour of ice cream to eat your whole life, ignoring all the deliciousness you’d leave out of your experience for no particular reason other than “it’s seems different”. Even better is to try a bit of all flavour, get a general sense of what good ice cream tastes like and figure that you can mix and match! Yum!

And mix and match, I do! Yes, most of my work is corporate dashboards, but my process includes more creative input than the average data analyst may feel comfortable with - and that’s a consequence of eating lots of ice cream - err, I mean, of diving into different areas within data viz and Design, and learning from them.

One of the best ways to access more creative visualisation work is through community projects that often become marvellous books. This is the case of today’s book recommendation: Data Sketches, by Nadieh Bremer and Shirley Wu.

Creative outlet and learning

Going on a creative outlet tangent is probably one of the most effective ways to learn new skills. I learned watercolour painting from scratch, just to draw a data visualisation self-portrait. I studied colour theory just because I wanted to know which hue variation of grey would sit better as the background elements on a dashboard I was building for myself. I decided to get back into ink drawing by joining an Inktober challenge.

Often, client and business work has a time crunch tick-tocking your creative flow away from you. But in a personal project, you get to make the rules. And it is even better if someone else goes along the journey with you! This book came to be from one of these side projects. Two data viz friends decided to collaborate on building visualisations around 12 different topics for a total of 24 awe-inspiring pieces of work, all available for you to savour with your eyes on their website.

They also share their processes, from inspiration to data collection, sketching, and coding. They both use tools like D3.js, React, HTML5 and Illustrator, and they share tips, ideation, and how they achieved their final results in detail. It is a fascinating read if you want to take on more creative data visualisation work, but it is also super interesting if you, like me, are just curious about what else is out there beyond the constraints of Business Intelligence tools and processes.

They don’t go into coding snippets and how-to tutorials - this is more focused on the flow of turning data into visuals, with details of the journey and thought process. This approach makes for a very accessible book for all levels of interest and knowledge. Whether you’re a seasoned data viz professional, just starting out, or you just really want a gorgeous centre-piece data viz-themed book for your coffee table, this book has you covered.

One of the best things is to see their evolution across their 12 projects, not only in terms of technique but also how their processes would become more established as time went by. It is also beautiful to see how they take from each other, and slowly, each of them starts adopting little things from the other. Shirley Wu mentioned one such thing when she began to adopt sketches before going into code with her data for proof of concepts. You can hear more about that and other tidbits from their book at this awesome live session with the authors:

(Shirley starts to talk about sketching at about 15m30s in.)

Sketching and Wireframing

Their processes are deeply creative, and both make use of multiple design techniques to organise and abstract often imperfect datasets into impressive visuals through code. One of my favourite takeaways from the book have more to do with the ideation process and their sketches.

I love sketching. And I brought this with me as a habit when I started working with data. In the business data & analytics universe, sketching is often seen as an unnecessary step. The argument goes that if you can create something straight away into your tool of choice, why would you waste time doodling before you begin. Although I see where this thought comes from - data analysts often come from technology-adjacent fields - putting ideas to paper helps us materialise them more effectively.

Sketching has many benefits. When you start working on a piece of data visualisation, you may have multiple ideas about how to represent the data you have.

  • Sketching and wireframing can help you decide which ideas to pursue before spending valuable time coding an idea that will not land well with clients or stakeholders.

  • Sketches and prototypes are also invaluable tools to help you discuss these ideas and abstractions with people that may not share the same technical vocabulary and jargon as you do.

  • Often it’s easier to show the users what you mean in an ideation session than trying to explain many technicalities and abstractions.

In Data Sketches, the sketching is mostly done with either pen and paper or digital notebooks - which digitally mimic pen and paper (like in an iPad). It is a very organic process. And although most of the sketches inform the final developments, there are instances where they were wild guesses and changed dramatically as the data came in and further development happened. Sketching, in these cases, helps to weed out the bad ideas from the good ones.

“But I can’t draw!” - this is the other objection I hear. Fear not, fellow data person! Sketching with pen and paper is just one way of adding this step to your process. A more polished and scalable alternative can be borrowed from web design in the form of wireframing.

Wireframing is the act of digitally sketching your ideas of what an interface would look like on screen with a low-fidelity visual mock-up. Tools like Balsamiq can be a good entry point. For quite some time, I used PowerPoint to draw basic wireframes. Let’s just say it was not ideal.

I have been using Figma to create wireframes for my client work since 2019 when I first heard of it from Robert Crocker. He showed me how he used it to make his dashboards in Tableau, and I have been working with Figma ever since with both Tableau and Power BI (he also told me I should start a blog. Thanks Rob!).

You don’t have to use Figma, but compared to other options like Sketch or Adobe XD, it is a very accessible tool with a fully featured free version, cloud-based, and an easy learning curve. The Figma community has endless hours of material you can learn from on YouTube and their website.

Whichever tool you choose, wireframing can help you:

  • To create prototypes of what a dashboard could look like, with mock-up charts that are not linked to any data - they’re just very quick and basic drawings to get a general idea across.

  • To help you better articulate your design decisions, why is something placed in a certain place and not another? Is there a better way to arrange the layout?

  • Discuss design elements with clients and stakeholders and ensure they can have a visual point of reference of what good looks like before you start development.

  • To create awesome backgrounds that can be used alongside the charts within tools like Power BI and Tableau.

  • To document data visualisation guidelines and rules for typography, colour palettes, layouts and templates to ensure other developers and analysts can keep visual consistency across multiple pieces of work - this can also be called a Design System.

Once you are satisfied with your wireframes, development gets easier as you have a vision of what the final product you’re developing should look like. It will serve as a blueprint and guide, so you know you’re on the right track. If you’ve never tried it, I strongly recommend you do! Even if it feels a bit counter-intuitive. It’s as easy as picking a piece of paper and drawing your vision of where each item on your dashboard should be and why and discussing it with your colleagues, clients and stakeholders.

Should you read it?

Data Sketches is a fantastic book if you’re interested in seeing how other data visualisation designers put their work together and what their process looks like. It is filled with gorgeous visualisations and very accessible explanations of how they came to be. If you’re curious about the more creative, outlandish use of alternative visuals to depict data, this will be straight-up your cup of tea! And speaking of tea, this is one of those books you want in your living room, next to all the Taschen art ones - it is that gorgeous.

Data Sketches are also a document for the future. It is about two friends who share an interest and embark on a journey where they learn a lot from each other while creating awesome things. It documents how data visualisers of our time experiment outside their day-to-day jobs. How they envision the language they’re using to represent data relevant to them at this particular point in time. If anything, it is a historical record of what our field’s landscape looks like today, and it will surely be one of those pieces worth revising a few decades from now.

Always check your local library first to see if any of the books I recommend are available. If they’re not, consider donating a copy!


Get a copy at your local library | Amazon

You can see all Data Sketches visualisations here.


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written by human, not by AI

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