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  • Writer's pictureT. @ Data Rocks

Hans Rosling’s Factfulness or how I learned to love visual storytelling.

In 2006 I was an International Relations student, working as an intern at a big bank with asset management operations. I had just given up studying Design because career prospects weren’t clear enough. I loved Design, but my main priority was to get a well-paying job as soon as possible and to bring money back home to pay the bills so I could continue my education. I was the first person in my family to go to university, and I couldn’t botch it by doing something out of pure love - I had to be pragmatic. Very few things are more pragmatic than being an intern in a bank dealing with corporate clients. If a person doesn’t come from a background of privilege, choices are often guided and limited by their surrounding circumstances, and this was my context.

I had no knowledge of the terms data visualisation, information design or data storytelling. I didn’t know data was more than the statistics I learned in Economics classes. I barely knew what Excel was. But with the powers hindsight gives me, I realised that my inclination to explain and explore things visually just needed a nudge in the right direction - something to tell me it was ok to be the person whose notebook photocopies were highly sought-after in exam week, because of all the little diagrams I came up with in my visual note-taking.

That nudge came in the form of a TED talk by Hans Rosling, where he challenged our perceptions of the world using statistics and his incredible data storytelling skills. That 20-minute talk made me realise that my choices weren’t necessarily an either/or: either Design or working at a bank. Either having a career or drawing. It could be both. I could be as cool as Hans and use my love for well-designed things alongside my statistics. I could make my internship reports look cool with charts in Excel.

Beautiful statistics and magic washing machines.

Hans Rosling was a Swedish professor of international health, academic and outstanding public speaker who invested a good portion of his career in helping people challenge their worldviews through data storytelling.

If you’ve never seen the man in action (or even if you have), take a few minutes to be mesmerised by how he involves the audience with statistics and plays with their biases in his classic TED talk:

Hans Rosling had a way of bringing people along with him on the journey when talking about complex concepts - something incredibly challenging to do - and he did it with such grace it looked effortless.

There was also that other time when he talked about magical washing machines and how it has impacted society development, wealth distribution, education, and energy usage, all while just telling us a tale about his family:

You get the gist. I could watch Hans Rosling speak all day, and I can’t miss a chance to recommend his work. Hans passed away in 2017. Hans’ son Ola Rosling and his wife, Anna Rosling Rönnlund, worked with Hans to develop the data visualisation tools you see in the videos, and they have continued much of his work. You can check it all out at the Gapminder Foundation website.

The book: Factfulness

Factfulness: Ten Reasons We’re Wrong About the World – and Why Things Are Better Than You Think was released in 2018 and is co-authored by Hans, Ola and Anna. It is a book of unwavering optimism, which has drawn some criticism, saying it cherry-picks only cases where bad things are in decline and good things are on the rise. But the other kind of book is so common ( bad things on the rise, good things dwindling) that it is a breath of fresh air to learn that humanity doesn’t totally suck all of the time. Just some of the time.

If the news depresses you, Factfulness may be a good antidote.

The book presents a framework for understanding and interpreting the world and argues that people have a tendency to hold onto overly-simplistic and often incorrect views of the world due to various cognitive biases. It promotes a more nuanced view of international statistics.

It is not an instructional book that will tell you how to work in a specific field. It won’t teach you data storytelling per se. But it is perhaps the best compilation of examples of what compelling data storytelling looks like.

Factfulness has received international praise and awards, the works. It is a classic, and everyone interested in consuming or producing information (that’s everybody nowadays, really) should read it.

(not sure if you can tell I’m a big fan of this book).

Factfulness 10 rules of thumb and how I use them as a Data Visualisation practitioner

One of the things I like to do when I read a book packed with practical information like Factfulness is to take note of what I learned and think of situations where I can apply each lesson.

In my personal opinion, the most impactful contribution of this book lies in the way it defines “10 dramatic instincts” and the resulting “10 rules of thumb” that can help us think more factually about the world around us.

Factfulness 10 Dramatic Instincts poster. It depicts the ten dramatic instincts: The Gap Instinct; The Negativity Instinct; The Straight Line Instinct; The Fear Instinct; The Size Instinct; The Generalization Instinct; The Destiny Instinct; The Single Perspective Instinct; The Blame Instinct; The Urgency Instinct.

These rules are helpful not only to global statistics but to every piece of information we consume and produce. They serve as a concise yet powerful guideline to address common misconceptions when working with any data, regardless if you’re a beginner or a more seasoned data professional.

They can also help us avoid getting misled by misinformation, as many deliberately harmful pieces floating around do not stand a further critical analysis following the guidelines proposed in the book.

Factfulness 10 Rules of Thumb poster. It depicts the ten Rules of Thumb to take control of your dramatic instincts: Locate the majority; Expect negative news; Imagine bending lines; Calculate the risk; Check the proportions; Question your categories; Notice slow changes; Use multiple tools; Resist pointing fingers; Take small steps.

As a Data Viz professional, the 10 rules of thumb from Factfulness are incredibly handy when you need a basic frame of reference for analysing data or for when you’re planning to communicate complex topics to a general audience - be it your boss, a C-suite team, or your followers on social media.

I was always getting back to it, so I turned the rules into a personal checklist that helped to guide me when I was getting started with data work. After a while, these little guidelines become second nature, but they’re always a good reminder.

  1. Mind the Gap: Remember that you might overestimate the differences between groups, individuals or countries. Try to gather data to get a more accurate view. What is the gap made of? What explains it? What’s the context?

  2. The cup can be half full, too: Remember that you might get hyper-focused on the negative aspects of a situation and overestimate the prevalence of adverse events. Try to gather a more balanced view by seeking out positive information as well. Are things really as bad as they seem to be?

  3. The forecasting mantra: Past performance doesn’t guarantee future results. Remember that you might assume that things will always get better or worse at a steady rate, following a trend. Try to identify any potential factors that could influence the trend. Is there seasonality? Are there one-off events? What’s the chance of a perceived trend just being random?

  4. Feel the fear and do it anyway: Remember that you might overestimate danger and the likelihood of negative events happening to you personally. Try to assess risks and opportunities more objectively. What is the actual probability of such events? Can they be prevented? What is the worst that can realistically happen? Is it enough to stop me from proceeding with a decision?

  5. Context matters: Remember that you might overestimate the size of large groups and underestimate the size of small groups. Try to gather data on the size and characteristics of different groups. How do they compare to other similar groups? What is the concentration of these groups in the data? Are they outliers?

  6. Don’t extrapolate from singular statements: Remember that you might make broad, blanket statements about groups of people based on limited information. Try to gather specific information about individuals and avoid making generalisations. Frame your problem as a Falsiable hypothesis and see if it still holds true after further consideration.

  7. The only constant is change: Remember that you might assume that things will always stay the same and that people’s circumstances are fixed. Try to identify potential changes and consider how you can adapt to them.

  8. Diversity matters: Remember that you might see a situation from only one perspective and overlook other points of view. Try to seek out diverse viewpoints and consider multiple perspectives.

  9. Don’t play the blame game: Remember that you might be tempted to assign blame to someone or something else and overlook the role of chance or complexity. Try to focus on finding solutions rather than pointing fingers.

  10. Take a deep breath: Remember that you might feel a sense of urgency and act impulsively in response to a perceived crisis. Try to take a step back and assess the situation objectively before making a decision.

Should you read it?

Factfulness is a book about how we perceive the world and how those perceptions may be misinformed and deeply biased. But the book does that with a positive twist and lays out a few “rules of thumb” to help us develop our critical minds when assessing data that are presented to us.

I recommend this to anyone, seriously. But if you have a particular interest in learning more about the power of data storytelling and how we can use data for good, this is a must-read. I hope you enjoy it as much as I do.

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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