Whenever someone asks me what I do, I tell them I communicate complicated things visually. Usually, more questions follow, and we get to a point where most people equate what I do with a tool they recognise:
I invariably just let out a long sigh and agree. In part, that is what I also do. But Data Visualisation is much more than pretty charts and much more than any tool in particular. Information Design has existed for a long time. At least since we as a society decided to organise information and report it to others. Back then, things were done using paper, pencil, and ink. As the printed press evolved, so did the techniques we could use to translate complicated ideas, concepts, theories and groups of figures into diagrams, charts, plots or drawings. Only recently, we started using computers to communicate our visual metaphors. And even more recently, big data became something we must untangle to make sense of the world. Hence the need for more sophisticated tools to help us achieve that.
But the rules of what makes good visual communication exist regardless of which method we are employing to transmit it. Have you ever thought there was a time when Line charts, Bars and Pie Charts didn’t even exist as a concept? It is easy to take these for granted when they come as templates in any contemporary digital tool - but there was once someone who grabbed a piece of paper and came up with the idea of modifying shapes and colours according to the data behind them as a way to visually represent a message to someone else.
What is it, then? What do I do if not just be an Excel wizard, a Tableau nerd or someone skilled with Power BI? In a fundamental way, I come up with ways to represent data visually: by flexing shapes, positioning them in a plot, and adding colours or textures to encode my findings using a visual vocabulary I share with my audience. I can do that using anything, be it physical or digital. With any amount of data, too - even though we got used to associating data visualisation with the idea of “speed to insight”, it isn’t always about massive datasets or making things quicker. Any data can be visualised, and the purpose can be other than simplifying or speeding up things.
One example of that in practice is the quantified-self movement. If you’ve never heard of it, it is about tracking personal data about things you do, such as habits. Have you ever seen a bullet journal’s habit or mood-tracking pages? They are data visualisations, as well: they represent how many times you hit a habit in a month or how sad you may have felt during a certain period of your life. You don’t need anything fancy - just a pen and a notebook. Another good example of data being visualised and experienced through something other than a software is data physicalisation, using, for example, play-doh or Legos to build physical representations of data and life. These can get all the way to interactive installations in a building or museum.
This is when I hear someone yelling at the back that this is all good, but it has no practical use in a corporate, big data environment, and it is hence a waste of time. They may argue that our agile overlords demand us to strip away all of this silly and creative nonsense and stick to what’s tried and tested. Preferably we’ll only use templates and very recognisable charts for everything. There’s no need to be fancy; quick and dirty will do. A viable product has to be minimum, after all.
While I understand the argument, I wholeheartedly disagree. Learning how to free our minds from software constraints and ideate and sketch creatively and freely are fantastic ways to incentivise us to learn more about how the tools we use work. When we have a vision of what we’re trying to achieve, we’ll look for ways to bend the tool to our will, resulting in us going deeper into its inner workings, testing out what works and what doesn’t. And while the solution may indeed just be a simple bar chart, the tentative will have taught us a bunch of other skills that we may use to achieve even better work more quickly. Not only that, but sketching helps us test ideas out and learn how arranging data in certain ways may be more or less adequate to communicate a point or message. We get better at the core skills of good data viz, such as hierarchy, placement, and composition, which can then be applied to any tools out there. These are often the blind spots in many unsuccessful and cluttered dashboard designs. Your dashboard isn’t unsuccessful because the analysts don’t know how to use a tool proficiently; more often than not, it is the ability to organise the information for effective communication that is lacking.
As someone who works mainly with corporate clients, that argument is a hard sell, unfortunately. But I always try to plan a little artsy seedling here and there, to see if it can flourish out of the hard concrete pavements I walk on. One way of doing that is to introduce people to the joys of collecting data about ourselves and sharing them with cute doodles, a lesson I learned from the two authors of today’s book recommendations.
Data Viz as a friendship bridge
Stefanie Posavec and Giorgia Lupi met at a design conference and figured they had a similar approach to how they see and communicate data; both favour analog techniques. The similarities didn’t end there: they’re the same age, and both moved across the Atlantic (albeit in opposite directions). So they decided to get to know each other better and forge a friendship around data viz and snail mail. Each week, they’d come up with a theme, track their personal data about it, and send the other a postcard with a drawn viz depicting the data they collected. The themes ranged from the number of times they checked their phones or the time to personal things like physical contact with others, their smiles to others or how much they say thank you. The result is compiled into a wonderful book called Dear Data.
But Dear Data is so much more than just 104 postcards. Throughout the year, we see how their styles evolve, their different approaches to the same subject, and how the inspiration to depict data may come from anywhere: from modern art, astronomy, flowers, or music. It is an awe-inspiring collection of ways we can collect and display data. One feature I absolutely love in their postcard is the choice of using one side only for the visualisation piece, while the other is often a messy intertwine of annotations and legends, explaining how to decode all the little ways they came up with to encode their data. It feels like deciphering a puzzle meant to be read only by a friend, like a personal journal with a code and a key, full of personal secrets. Dear Data takes the human aspect of data to the next level, not only because it is about drawing and exchanging little pieces of life with another person but because it brings out all the wonderfulness of messy data collection and the flawed nature of data as a proxy to real life. The imperfections of the visualisations add to the whole thing: data is rarely pristine. The work of analysing large amounts of information and coming up with a cohesive story from it is far from linear. It is a raw and unfiltered glimpse of the processes that go through any data analyst or information designer’s mind while creating data viz, regardless of tools.
The book is also incredibly accessible: no skill or affinity with technology is required to enjoy the journey these two people took with themselves, their data and each other. That is why I believe this is one of the best entry points for anyone interested in learning data visualisation. I understand the anxiety of learning how to create charts with a software, but understanding that visualising data is about creating and sharing a language with others while uncovering and explaining a topic goes way beyond in making you an effective analyst than just learning the step-by-step sequence of actions to take in a tool. It helps you see the world differently and more critically - and all of these are outstanding skills that will set you apart as an analyst.
There is also a case for beauty and delight. A case for embracing the little complexities of everyday things. In an almost poetic way, the case for slowing down and paying attention to the small rituals and situations that makes us who we are. It is a very rare sight in an age where instant communication and disposable content are abundant. Giorgia and Stefanie capture small frames of their lives and materialise them. It is a collection of what a year in someone else’s lives look like, akin to a photo album, but made of data and art. How more beautiful can that be? It is no wonder that the project ended up in the Museum of Metropolitan Art in New York.
Observing, Collecting and Drawing with Data
A couple of years after Dear Data was published, Giorgia Lupi and Stefanie Posavec released a follow-up, in the shape of a guided journal, entitled Observe, Collect, Draw. This is their response to everyone who was feeling inspired by Dear Data but wasn’t sure how they could apply similar principles to their own life and work. Through this journal, we’re guided through exercises on how to develop our designer’s eye, how to observe the world around us and collect data from it and how to create our own visual language.
There are also brief introductory sections about what data is, why it matters, and how working within constraints can make us more creative. It touches a bit into the method of crafting data viz from a humanistic perspective. All of these skills are crucial in understanding how Data Visualisation works. But beyond that, this is yet another incredibly accessible book for anyone who’s into journaling, tracking their lives and quantifying themselves. Along the book, you get prompts and suggestions to develop your own data visualisations about yourself, your life and the things around you.
It is so inspiring that I myself got inspired to go beyond the book’s limits. Here’s my first play into one of the prompts about drawing a data portrait. This is my partner’s and a friend's data portraits:
I loved the concept so much that I fell into a rabbit hole of modern art references and ended up learning watercolours just to create my own data viz self-portrait. You can see a bit of the process on my Behance.
Should you read it?
I highly recommend Observe, Collect, Draw as a starting point for anyone transitioning into Data Visualisation, especially if you’re passionate about the part where you communicate data visually but are feeling overwhelmed by all the tools available out there. Pen and paper are your friends and good tools as any to get started! Dust off your colourful pens and have fun while honing your visual design skills. Beyond any tool skills, this will help you become a better designer, a more humanistic analyst and understand the limitations and possibilities of working with data at different levels. It is easy to get tangled in big data and software madness, and this is an absolutely essential primer into what data visualisation is actually about: people, our behaviours, their lives and how we all make sense of the complexities of the world around us.
If you want a glimpse into how two highly skilled information designers untangle all of those things around them in weekly bites, get Dear Data. It is an incredibly inspiring book, and it will help you learn how the same subject and data can mean different things to different people and how each one of us visualises and translates what we experience in unique ways beyond templates and pre-sets.
None of these books are meant to be read in one sitting. They are slow books, different from the usual management-style books. They’re meant to be books you spend time with, slowly, as you progress in your data journey. They also make fantastic gifts for the data and design people in your life.
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
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