Why You Don't Believe In Facts, And How To Fix It


This is the transcript of a talk I gave at Re:publica in Berlin at the 9th of May 2017. You can find all the slides in that insanely fast GIF up there as PDFs on Github. You can also find a video recording of this talk on YouTube.

Hi! This is me, and every single one of you:


We’re standing on the ground of hard facts. (“Der Boden der Tatsachen”, as Germans would say.) Well, ok, there are no hard facts; let me rephrase that: We’re standing on the ground of concepts with different levels of certainty; concepts entangled by confidence intervals. For example, it is a kind-of-certain fact that approximately 97% of climate experts agree that humans are causing global warming.


When we’re accepting a “fact”, we start to believe in it. Eg we can believe that global warming is real. Beliefs are important: They let us marry people, let us found companies and research projects, and let us go to church every Sunday. And beliefs are the basis of every ethical discussion: We need to agree on what’s happening before we can discuss what we should do.


But it seems like in more and more cases, we don’t have a discussion of what we should do – but what is true in the first place. What is really happening? What is truth? What is the reality? What are the facts? It almost seems like some people stand on the ground of a completely different reality; sharing completely different facts. But of course, that’s not the case. The reality is still the same, pretty much.


What I’m currently doing with my life, is visualising the facts. I’m visualising data, often from the Census or Scientific Studies. And then I’m communicating it to the public and say “Here, look, these are the facts”; let’s have a discussion on these grounds. So when people don’t take them seriously, I have a problem. I’m not doing my job well.


Today I want to explore how I can do my job better. Am I just preaching to the choir with my data vis? Or is it possible to convince somebody with facts, numbers, science, reason?

To do so, I will explain first where false beliefs actually come from. Why don’t we all believe in the same? Then I want to explain why it’s so hard to get rid of false beliefs once they’re planted inside of you. And then we’ll use this knowledge to explore what we can do to believe more certain things – we in this room, and I as a data vis person.

A. Where false beliefs come from.

Let’s start with talking about the reasons for false beliefs. Of course there’s only one:


Fake News is something a lot of people have talked about. (One of the best things, in my opinion, can be found on First Draft News.) I actually think that sharing fabricated content and misinformation on social media is only a minor cause of having false beliefs, but a major symptom.


One of the actual big reasons is tribalization. We’re social animals living in different tribes (our family, our friend groups, our favourite online forum, our Twitter feed). And for social animals, social support is more important than believing the truth about something that’s far, far away like climate change.

In the first instance, beliefs can become a marker of identity. We belief what our family and our friends told us. When we share articles on Facebook, we want to demonstrate who we truly are. We want to define ourselves. “Defining” literally means “to limit, to bound”: We draw boundaries around our identity, also to attract like-minded people.


Showing who we are is especially important to demonstrate whose team we are on. We want to show the other tribe members (friends, forum, party, church) that we are good members of the tribe.


We could see that especially well in and after the election. When confronted with the photo of Obama’s inauguration from 2009 and Trump’s inauguration from 2017 and asked which photo had more people, lots of Trump supporters gave the wrong answer. They said that Trump’s inauguration hosted more people. Not because their perception was flawed. But because it was a way to show their support for Trump. It’s not truth vs lie, it’s their team vs the other team.


But showing off your beliefs with information doesn’t just gain us the trust of a team. It’s also a test to see who WE can trust. These people who unfriended us after we posted a long essay about how much we hate Trump? Yeah, it’s good they’re gone. We’ll definitely not drop by their birthday party this year.


Sadly, that happens more and more in the two-party-system in the US. It’s one thing that less and less government people agree with members of the other party:


It gets disturbing when you hear that 58% of Republicans and 55% of Democrats had a very unfavourable attitude towards the opposite party in 2016; up from 21%/17% in 1994. Less Democrats marry Republicans and the other way round. People of the two parties don’t trust each other as much as they did only a few years ago.

Interesting enough, for forming beliefs for the sake of tribalization, it doesn’t matter if we understand what we belief. They are too far away from us. Ask yourself: How does climate change work…exactly? Could you explain it? I know I couldn’t. But people from my tribes and I believe in it, and we know someone is from another tribe if she or he doesn’t.

The third reason for forming false beliefs I want to talk about are fallacies. This could be a talk on its own, so I will just mention one example: Generalising from anecdotal evidence. Which means seeing one instance and applying that to the whole concept. Saying “Man, it’s so cold and rainy for May. Global warming, yeah right” for example:

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B. Why it is so hard to get rid of them.

So once we got a false belief, we just keep it until somebody comes and says “Dude, that’s not right”, and then we get rid of it – right? Unfortunately not. We defend many beliefs like crazy.

Since we get so many beliefs from our tribes, questioning the belief means questioning the tribe. Beliefs are connected to the person who gave it to us, the situation we’re in, the memories, our upbringing etc. If a belief turns out to be untrue, then all of this is in doubt.


But there’s no need to get rid of the beliefs anyway. One quick Google search tells us that there is tons of evidence for our belief, and our tribe confirms us on a regular basis. And if one online forum tries to convince us of the opposite, we can just leave and can go to another one. The internet is truly a paradise for finding confirmation.

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Once we have a belief, we perceive information differently. Information that confirms our beliefs we agree easily with; we question it less and remember it better. The opposite of this Confirmation Bias is Motivated Skepticism: When we get confronted with information that oppose our beliefs, we apply more skepticism than normal. Everything seems untrustworthy suddenly: The author, the sources, the medium.


An example for that can be found in the following article, which explains why you should vaccinate your kids. As somebody who agrees with the premise, I also don’t question the sources: “International studies”, it says. Sure. And there’s this one study that basically explains that you have less respiratory problems when you don’t have the flu? Sounds about right:


Ok, fine. I’ll admit, I changed the original article a bit. The actual article can be found here and speaks against vaccines.

And suddenly every second word makes me angry and suspicious at the same time. “International studies”? Yeah right. I know which kind of studies THESE ones are. Certainly not peer-reviewed, I bet. And this journal called “Clinical Infectious Diseases”, they mention? I’m sure it has a super bad image in the community. I wouldn’t trust one article published in there. And naturalnews.com? Well, I’ll certainly unfriend everyone immediately who mentions this site on Facebook.


Confirmation Bias and Motivated Skepticism don’t just make us perceive information differently, but also strengthen our beliefs – in both directions. Consuming lots of confirming information (more investment) nourishes my belief:


It’s supposed to work also the other way round: If you show me information opposing my belief, my belief will become stronger. You might have heard of it as the Backfire Effect, but it’s not well proven scientifically. I dare you to find a good paper and I’ll change this blog post.

Close-mindedness is the last reason I’ll mention today. Some of us are it more, some are it less: We are different people, and some of us accept opposing information better than others.


I think it’s important to keep that in mind, to not give up on people (“Whyyyyy the heck do you not accept the truth?”).

C. How to believe more true things.

So after hearing about the obstacles that keep us from believing more true things…how can we still attempt to do so? The most obvious idea is to convince someone, brute force: “Hey, you believe x, but that’s not true. You should believe y.”


Often, that’s neither the most successful nor the most kind attempt. Let’s maybe not do that.


Instead, we can try to make them see the truth themselves. We can ask them questions (like Socrates back then): Why do you believe what you believe? How did you come to that conclusion? What would be the consequence of your thinking?

And we can point out the difference between the facts and their beliefs in a subtle way.


The closest data visualisation I’ve seen to this approach is Bloomberg’s “What’s Really Warming the World?”. It leads through different factors that “Climate Deniers” see as reasons for climate change: Is it sun activity? Deforestation? Ozone Pollution? No, it really is greenhouse gases.


The next idea, actively avoiding tribalization might be the most powerful approach against false beliefs, especially when we believe that tribalization is one of the main reasons in the first place. The easiest way to avoid tribalization is to find people with different opinions, and then to actively listen to what they have to say. Why do they believe the things they believe in? What are their rational reasons?


The German ZEIT Online is attempting conversations like that. Right now they have a call for people who want to meet different-minded people in their neighbourhood, on one afternoon in June.

During such conversations we can try to build understanding and empathy, which might let us discover some hidden truths and emotional reasons:


The third way to make us believe more true things is to change our attitude; towards ourselves and towards others. This attitude shift can happen in different ways.

We need to accept that there are millions of facts we don’t know, that not knowing things is normal (not devastating); that changing beliefs, therefore, is necessary to grow. Which means that having beliefs as marker of identity is not the most marvellous idea. Our beliefs come and go to help us act in this world; but we are not defined by them:


A great idea to realise this attitude is to get people in a truth-seeking mode. The excellent paper “Science Curiosity and Political Information Processing” by Kahan et al states that “individuals can use their reason for two ends—to form beliefs that evince who they are, and to form beliefs that are consistent with the best available scientific evidence.”

The paper show that people who are more intelligent are more likely to have extreme beliefs (true and false), which seems to be bad news. But it also shows that people who are more curious and driven by scientific thinking tend to believe more true things:


So we should make people more curious! Quizzes and games are a great way to do so; and of course I need to mention “You Draw It” from the New York Times Graphics team here. What a great coincidence that I wrote about curiosity in a blog post last month, where I explained the beauty of this very graphic.

I can also think of three more “changes in attitude” that are closely related to data vis: Showing complexity (including uncertainty), and planting doubt. The latter means explaining that the world is not as simply measured as we sometimes assume it is.

But let’s focus on showing complexity. I would assume that most data visualisations out there are bar charts. Which brings two problems with it: First, every kind of data looks the same; and second, it simplifies extremely.

And hey, that’s good. Simplifying is our job, among many things. But more and more often I appreciate approaches like the one from Accurat, who show the complexity of the data out there better than any bar charts. The form gives a more honest view of the world. Our beliefs are already simplified enough: Forms that remind us that the facts are not that simple could become more and more important.


The last approach to believe more true things is to remind ourselves of our skin in the game. Having skin in the game means to be directly influenced by the outcomes of something; in this case our beliefs. We have skin in the game when we think that it doesn’t rain, but it does – because we’ll get wet. We get immediate feedback.


But we don’t have skin in the game with most tribe-defining beliefs like climate change, pizza gate, voter fraud. We are not effected by the outcome:


Remember that awesome “Science Curiosity and Political Information Processing”-paper? It states that “Farmers … have been observed to use information on climate change to form identity-congruent beliefs when they are behaving as citizens but to form truth-convergent ones when they are engaging in the task of farming, where they have an end—succeeding as farmers—that can be satisfied only with that form of information processing”. Which basically means: When you ask a Republican farmer if he believes in climate change, he will probably say no – until this belief gets important for his goals to be a better farmer. Reminding ourselves and others of the skin in the game and the personal ways they can benefit from information will make them listen.

D. What does it all mean?

Wow, you’ve made it! That was one long article. Here are all the points we’ve encountered while you were reading this article; and believe me, each category could be filled with far more reasons, causes and examples:


But what should we do now, after having all these information? Is it a good option to not believe in anything anymore?

I’d say no. I disagree with people who say we need to build beliefs carefully. Beliefs are important for acting in this world – and I’d rather want that we act than not act. In my opinion, we should have grand beliefs. But these beliefs show be flexible like a gummy bear: image

Strong opinions, loosely held. That’s a phrase I really like and that I’d like to apply to beliefs as well: Strong beliefs, loosely held. We want big beliefs to act. But when we encounter new facts, we want them to update easily.


Thank you! If you have thoughts, new categorisations and critique, please email me: (lisacharlotterost@gmail.com) or look me up me on Twitter (@lisacrost).


Embracing Curiosity


Curiosity. Some people have it a lot, some have it a little. All the data (vis) people I know have tons of it. “I wonder if there’s data out there for it..”, “We could also try to find out…”, “Maybe, if we can prove with data that…” are common phrases you can hear wherever data people gather.

That’s why more than two years ago, I declared curiosity as one of the four core traits of data vis designer:


But although I love the feeling of curiosity while working with data myself, I’ve never considered the curiosity of the data vis reader. That is, until I encountered Tim Harford’s article “The Problem With Facts” a few weeks ago. He cites a paper that studied the effect of curiosity on reasoning:


Robin Kwong transformed this idea in his article “How do we create a need for news?”:


Help people to explore the world? Help them to learn about it? Not giving final answers to people, but making them ask questions? That seems like a goal worth pursuing for journalism.

But I’m conflicted.

On the one hand: Yes, curiosity is amazing. Why? Often, curiosity is directed towards something very specific. It comes with a concrete goal: Finding out what happens when you jump in that puddle. Finding out if your best friend has a new boyfriend. Finding out if there’s a regional pattern with unemployment rates in your country.

Having these intrinsic motivations and acting to get there is one of the best feelings in the world. It’s what people talk about when they talk about “Flow”. You forget the time. You feel productive. Your work feels important and meaningful. There’s just you and the goal. And everything you do is to reduce the obstacles between you and the goal. And when you reach the goal, a mini-Christmas is happening.


I want readers to get that feeling when they interact with my data visualisations. I want them to feel curious about the data. I want them to ask questions, and to find answers. I want them to feel empowered, and to really grasp the data.

On the other hand, I want to make sure that they get the most important statements in my data vis. I want to save them the time to figure out these points on their own. They shouldn’t learn how to use a completely new data vis tool, just to get to an underwhelming point after half a day of digging. My job is to spend all that time with the data for them, so that they don’t need to spend that time themselves. I want to deliver answers to them, fast.

A compromise is needed. I think I need to say good bye to the idea that the most efficient data communication is always the best way to go. For people to care about my data, I need to make them curious, first – to the right degree:


There is a sweet spot between my current “get the reader the information the fastest, even if that means it’s a bar chart”-mindset and the too-open field of wonder in which the reader will quickly loose interest; especially if he doesn’t know if it’s worth it.

That sweet spot has been hit by many people in the past. Explorable Explanations have been doing that really, really well. And in the news graphic realm, the New York Times did an amazing job with their “You Draw It” graphic:


It’s a quizzing tool that plants a question in the reader, before showing her the information. The reader needs to build a hypothesis first. Then she can check if her belief was right or wrong. It’s science in a mini-format.

But it’s science that’s rewarded. The reader trusts the NYT that it will be worth it to work for the answer; that the question the reader asks will lead to an interesting answer. Not every data is worthy of such a treatment. In 2011, Kaiser Fung introduced the metric “return on effort” for evaluating graphics. He asked: How much will the reader be awarded for her effort to read the graphic? The reader will be annoyed to work for her insight, if the insight is disappointing. We need to adjust our work to take that into account:


When we first make a reader curious and then deliver the answer, we can have a greater impact than when we just deliver the answer. The question is: What are good ways to make people curious? What are ways to plant questions in people’s head? How can we make people curious about the world, even the ones who don’t score high on curiosity naturally?

And to think one step ahead: Can we, instead of giving people answers, or first plant questions and then answers – can we give people the questions without the answers? Questions we don’t have any answers for yet? How can we empower people, and make them interested in trying to solve the yet-to-be-solved problems?

Curiosity is like a superpower of people. When curiosity is fired up, people seek out information actively, and suck it up like a sponge. Let’s make sure we understand and use that power.

Please help me to answer these questions. Write me an email (lisacharlotterost@gmail.com) or find me on Twitter (@lisacrost).

Why Do We Visualise Data?


This is the transcript of a talk I gave at INCH in Munich at the 10th of March 2017. You can find all the slides in that insanely fast GIF up there and as PDFs on Github.

Finding a definition for data vis is ok-ish easy: Data vis represents data with visual elements to communicate information. Today I want to focus on a part of the data vis definition that is a bit overlooked: The part that tries to answer my favourite question of them all: “Why?”

So yes, why. Why the heck are we actually visualising data? I want to answer that question in five parts:

1 Data vis goals: the overview
2 Different industries have different goals.
3 Different data visualizer have different goals.
4 Goals influence each other.
5 What are your goals?

Data vis goals: the overview

In this first part, I will present my mental model of possible data vis goals; sorted into three categories. Feel free to skim that part! The first category, Attention & Beauty, includes goals that relate to the quick first impression of a data vis project. After the first initial impression, we move to goals that relate to the decoding of data: Understanding. And the last category, Implication, contains the sense-making and long-term impressions of a data vis piece.


(These three categories are nothing new. Francesco Franchi said that data visualisations can “Inspire & Entertain, Inform, Encourage” and Andy Kirk stated that the user of a data vis needs to “perceive, interpret/decode, comprehend”. That’s both awfully close to my categories of “Attention & Beauty, Understanding, Implication”.)


Attention & Beauty: 1. Get them to read: Data vis is still considered to be new and shiny and is often used to attract some eyeballs. Like the ads on Times Square, some designs out there try really, really hard to get your attention. An example is the typical colourful long-format infodesign blogpost. But every magazine will tell you that infographics work wonderfully to get a reader’s interest after pages of long text. That’s related to the 2nd goal:

Attention & Beauty: 2. Attract different learning types: Different people absorb information differently. It’s like kids who choose different toys on a playground. Infographics are amazing for visual learners. An example is Vox’ interview with Hillary Clinton. Here, the reader can get one and the same bit of information via audio (in the video), via the transcript or via the graphics.

Attention & Beauty: 3. Go with the hype: Have I mentioned that data vis is still considered to be new and shiny? And people love shiny stuff. Even if there is no rational need for data vis, many people feel that they need it for their business/article/publication. Just because everybody else has it. Sometimes, that’s not a bad thing. “Going with the hype” also includes copying successful and fancy visualisation formats. Remember NYT’s Snowfall from 2012? Every publication copied it; and made digital storytelling better in the process.

Attention & Beauty: 4. Create beauty: We can’t deny it. Data vis can look beautiful, at least according to a famous info design studio and a famous data vis subreddit. Good examples can be found in the tweets from @accidental__aRt: We have no idea what data is displayed, but it’s still interesting to look at. Not because of the content, but because of the form.


Understanding: 1. Understand the data: Before we can communicate something to the outside, we need to understand it ourselves. When I sit in front of R Studio I feel like in a lab, forming hypotheses and testing them. Visualisations help data scientists to make sense of the data; and they help quantified-self enthusiasts to make sense of themselves.

Understanding: 2. Explain: Once we’ve understood the data, we can teach it. A schoolroom is a good metaphor for that process; and data journalism is a good example. If you want to find good explainers, just look at every Washington Post graphic for the US election 2016. Man, they did some great work.

Understanding: 3. Explore: But sometimes, we can’t or don’t want to go as far as explaining the data. Sometimes we want our users and readers to explore the data for themselves. In this case, we build tools for them that make it as easy as possible to get the information they’re looking for; like libraries do (you know, the ones with books; not your Javascript ones). Dashboards are the by far most prominent examples in this category.


Implication: 1. Prove: In some cases, making people understand something is not enough. You want to convince them of something (hopefully The Truth™), like a lawyer want to convince the judge of the innocence of her client. Scientists are giving proofs with data and data vis all the time; and I’m always happy when I see journalists using the scientific toolbox. An example is the FT article that shows the correlations between Brexit votes and demographics.

Implication: 2. Correct world views: We are not rational. Although it would often be more useful to act based on The Truth™, we act as a consequence of blurry feelings and beliefs. To counteract them with data could change the wrong and harmful world views of (some)(open-minded) people. A great example is Max Roser’s Our World In Data.

Implication: 3. Evoke feelings: Data vis is probably not the preferred medium to evoke feelings (video, photo, illustration; heck, even text is better at that), but you might find yourself in the position where you’re looking to get an emotional response about your data from the user/reader. Data vis projects that achieve that are “U.S. Gun Deaths” by Periscopic or “Fallen” by Neil Halloran.

Implication: 4. Evoke actions: Once you got your reader to feel something, you often don’t want to stop there. You want them to do something: donate, participate, etc. To be honest, I can’t think of any data visualisation that tries to convince me to do something specific. Let me know if you got an example!

Implication: 5. Go meta: Data defines our lives in many ways. Data vis can show that and can make us think about how data is collected, stored or analyst or used for decisions. Data art can achieve that in its extremes; but “normal” data vis can show a taste of that. Giorgia Lupi, co-founder of the studio Accurat has been an advocate for that for years. In 2015, she stated that Accurat’s visualisations want to “embrace complexity” and “maintain the nuances of the data”.

Different industries have different goals.

After going through all the goals, we will now have a closer look at the data vis goals of certain industries.

Let’s start with business slideshows: They have very little emphasis on goals of the first category, Attention & Beauty: Since the data is often crucial to the user, she will actively try to understand it and doesn’t need to get convinced to do so. image

The opposite is true for Data Art: Good data art looks appealing. But it also has a huge focus on sense-making in a deeper way (“Implication”). image

Bad” Data art often offers the same (kind of) beauty, but without the implication. It serves as decorative illustration in this role. It’s still worth existing, e.g. for cover designs of business reports. image

And then there is Data Journalism. Its biggest focus in on understanding. It needs to attract readers and as we’ve seen, wants to change people’s mind; but most often, that’s not its main priorities. image

Different data visualizer have different goals.

We all get into Data vis because of slightly different and also because of slightly similar reasons. Personally, my main motivations to do data vis are to create something beautiful, to make myself and other people understand something, to offer an overview and to change people’s beliefs. That is very close to the goals of data vis for journalism, and I do feel very home in that field.


Why is it important that personal goals align with field goals? Because goals define priorities and how much time you spend with which task. If your job is to make data beautiful, but you don’t care about beauty at all, you’re in the wrong job.

Another way to think about goals and priorities is to ask: What do you sacrifice for what? If your main priority is beauty, you will sacrifice understanding for a fancy new visualisation form. For me, understanding is more important than beauty. I’m somebody who says: “Ok, let’s show the boring bar chart instead of the multi-dimensional slope chart, since nobody seems to understand the latter one.” But beauty AND understanding are definitely more worth than just understanding; and I’m willing to put a lot of time and effort into beauty.

Goals influence each other.

Goals/outcomes are interconnected. Some influence each other positively, some influence each other negatively. For example, if your goal is to make your data visualisation beautiful, you’re more likely to have evoked emotions as an outcome than with a boring bar chart. Evoking emotions is a side-effect of beauty.

But HOW goals influence each other is up to discussion. Different data visualizer have different opinions about how the goals of the three categories are connected. As an example, I want to talk a bit more about the relationship between beauty and understanding.

Moritz Stefaner, the “truth and beauty operator”, sees a tight connection between truth (understanding) and beauty in Alberto Cairo’s book The Functional Art: “Truth and beauty have always been two sides of the same coin. […] For Buckminster Fuller, beauty was an indicator of truth.”

Francesco Franchi argues the opposite in his book: “While creating an infographic, one moves within a spectrum: on one hand, there is aesthetic, and on the other, the information.”


Who’s right? Are understanding and beauty two extremes that couldn’t be further away; or extremely closely related?

Both are right. Yes, bringing more attention to the style can lead to more understanding. Just remember Edward Tufte’s Data-Ink-Ratio.


But that’s only true to a certain point, in my experience. If the focus on beauty is too high, the effect inverts: Understanding goes down. That’s why data art (with a high focus on Beauty) often has a low focus on understanding; and that’s why data vis with understanding as its highest priority can only be beautiful to a certain degree.


This example shows how goals can positively and negatively influence each other. But it also shows how opinions about goals can differ. I wonder what you all think about the relationship between beauty, understanding and implication. Let me know!

What are your goals?


I want to make you think about your goals when you visualise data. What are your personal priorities? What are the priorities of your industry or company you work at? Where do you stand between beauty, understanding and implication? And where do you want to stay?

Please write me an email with your thoughts, goals, new categorisations and critique (lisacharlotterost@gmail.com) or look me up me on Twitter (@lisacrost).

Data Vis in Times of Trump


This is a text in progress. I think that the topic might be on people’s mind a LOT these days, but I haven’t seen any other articles about it. I haven’t fully thought everything through. Honestly, it might be all bollocks. But it’s a start.

After the days that everybody now just calls “The Weekend Of Uproar”, Stefanie Posavec tweeted the following: “TFW all of your creative ideas & projects feel superficial when compared to the heavy f-ing shit going on in the world. Time to level UP.” I was grateful for her putting that blurry feeling into words. Lots of us feel that we need to DO something. SOMETHING. That post tries to explain WHY and HOW.


Let’s talk about Stefanie’s first point: Her ideas feel superficial. I’m sure that’s true for many of us. The question is: Do we all need to become political now? Many of us are not hardcore activists (otherwise we would have become hardcore activists and not designers or developers). You might be one of us who thinks: Do I need to feel guilty if I keep creating superficial, entertaining stuff?

I ask that question to myself. Where should I stand between political revolution and entertaining trivia? Should I present information that points to the problem, or information that distracts from it? Data that makes people think, or data that makes people smile?

The following text is not an answer, but a conversation; between me and myself. I will point out reasons why we should keep doing the same superficial things—and why we shouldn’t. Most of my arguments are as valid as my counter-arguments. The world is not black and white. I certainly won’t just create political data visualisations in the (near) future, and I won’t judge anyone who does the same.

1. “It won’t make a dent anyway.”.

With every graphic we post on the NYT, the Washington Post, FiveThirtyEight etc, we’re just preaching to the choir. Have you ever seen a comment at the NYT that says “OMG, my beloved Trump is SUCH a jerk? I had no idea! Thank you for opening my eyes”?Thought so. We’re just talking to ourselves in our filter bubbles. If we sincerely think about our goals and how to reach them most efficiently, creating another graphic for liberal media won’t be the best strategy.

…BUT, Lisa, don’t be so pessimistic. We don’t know what the impact really is of all these graphics. Maybe they actually help. Baby steps.

…AND preaching to the choir is not the worst idea. Even the choir can get more motivated to act. We can help them to figure out what to do. We can support their activism with data and information. We can motivate our filter bubble to donate even more.

2. “Escapism is underrated.”

We can’t just be exposed to the BAD every single hour of every single day. We need distraction. We need the contrast, the normality. People get tired of all the serious “The world is screwed, AGAIN”-stuff, and they will turn to entertainment anyway. And we might even get people excited about data vis with our lighter projects, and then they can still use their new skills to change the world.

…BUT I’m sure we can also attract people with meaningful work, especially if we want them to create meaningful work themselves.

…AND also, wow, now you’re saying we should give people what they demand? Wow. No.

…AND who says we can’t combine meaningful information and entertainment? Vi Hart and Nicky Case with their “Parable of the Polygons” have shown that this is possible. And people seem to like politics once it comes in the form of series like “House of Cards.” It’s all about the form.

”3. There are soooo many people working on it already”

All these graphics editors in newsrooms. And all these journalists with a soft spot for data. I’m sure they’re on it.

…BUT most of them need to react to news. To what happens NOW. The more something happens NOW, the more they are busy. Most of the newsrooms don’t have enough time or staff to educate people about something that has not happened yet or is just basic political or economical knowledge (eg. “Who reports to who, and why does it matter?”).

…AND when we show the same data with two different visualisations, then that’s a feature, not a bug. Different visualisations of the same data offer different perspectives, make different statements, highlight different parts and will reach different people. Even bar charts about the same data, just differently styled and annotated, can appeal to different-minded people.

”4. A graphic won’t be good if I’m not excited about the topic.”

I’m not a political person. I’m not an activist. I’ve never been one. I’m not excited about data about politics. I won’t stay motivated to build a visualisation about politics. And then my time is better invested in some project that’s actually fun.

…BUT there’s always a way to make a topic exciting. Politics is a massive field, and massively connected to fields you care about. If you just understand it well enough, it gets exciting. So start educating yourself.


Know of more reasons? Tell me in the comments, write me an email (lisacharlotterost@gmail.com) or find me on Twitter (@lisacrost).


Many of us might not have the problem of motivation. They immediately ask: How can I use my data vis talents to do something? We have all seen the power of data vis. Many of us work in newsrooms or have successful blogs where we post side projects. So we think: If only I could come up with something that would actually help. But help who? Or what?

Here again, I don’t have a good answer. These are my (naive) thoughts so far, about what data vis can change in times of crisis:

1. Data Vis can change minds and strengthen minds. I don’t know how much data or the visualisation of data can change the minds of politicians. And it seems like most people already disapprove of Trump’s job anyway. But even if we don’t change minds: Reminding people of the dilemma (and what they can do against) might lead to more donations and to more activism.

2. Data Vis can help to investigate the truth.. Investigative journalism might uncover some truths about the government that could lead to big changes. Data Vis can help to analyse and to present that truth.

3. Data Vis can support activists. Activists might need data and information that we can research and present. I have no idea (yet) which kind of data they would need, and I’m pretty excited about that point.

4. Data Vis can educate people.. That’s what I’m most excited about: Simple education. How does the government work? Who’s supposed to sit where, what’s their job, and what is going wrong currently? How is the federal budget used? What’s the connection between the US and other countries and why does it matter?

In general, I’m a fan of the “shotgun approach”: You just throw many things on a wall. And see what sticks. We should think about what will have the most impact, but we should also just create things and put them out there. We can’t predict how they’ll get used, and by who.


And of course, there are many ways to fix the world we shouldn’t neglect. Directly in our environment, we can comfort, change beliefs, defend values. On a higher level, we can give money and exposure to people and organisations who fix the world with their talents, knowledge and skills (like law). Even if we don’t use our data vis talents: We can still have an impact.

Please help me to make these thoughts less naive, and add to them. Again, write me an email (lisacharlotterost@gmail.com) or look me up me on Twitter (@lisacrost).

A Data Point Walks Into a Bar


Hi! That’s me giving a talk at the Chaos Communication Congress (also called CCC; also called 33c3 because it was the 33rd CCC and “CCC” consists out of three letters and hackers love abbreviations) at the 27th of December 2016.

I blink a lot. That’s what people do when they’re nervous. People are nervous when something means a lot to them – and the CCC means a lot to me. I love this festival of hacker culture, of arts and weirdness and wonderfulness. Giving a talk there was a huge honour.

This blog post won’t be a transcript of my talk, but an overview of the questions my talk answered and didn’t answer; and the sources for what I spoke about. My talk can be seen here, on the website of ccc-tv and my slides can be found here.

What I talked about

Mother Teresa said “If I look at the mass I will never act. If I look at the one, I will.” I presented ways that make us act when looking at the mass. First, I talked about our problems to see the individuals when creating data visualisations about huge numbers of people, thanks to our two different modes of thinking:


However, we still need to address both modi in fields like news or advocacy: The fast, emotional system to motivate people THAT they should do something (e.g. donating), and the slow, analytical system to help them decide HOW they should do it.


I then talked about how data vis can do both, since it’s a tool like language, that can speak to both modi. I tried to make clear that the right use of tools depends on the goals: Eg in politics or science, we might exclusively want to address the analytical side in us.


In the second half of the talk, I presented ways to talk to the fast, emotional system with data vis. Personally, I see especially much potential for the data vis scene in the category “Show what the data would mean for your experience”.


In the end, I pointed out that it can be harmful to just evoke emotions. We also need to be responsible enough to help people act upon them.

Questions I didn’t answer in my talk

  1. Do these methods I presented actually evoke emotions? Anshul Pandey, Enrico Bertini and Jeremy Boy did some first experiments in this direction, and failed to find any effect of data vis on empathy and donations. I’m looking forward to further research.

  2. Is creating empathy with data vis even necessary? Especially in newsrooms, a graphic is often accompanied by an anecdote about an individual. Maybe that combination works well enough? Maybe we want to keep data vis as as very true- and unbiased-looking overview; as a juxtaposition to the anecdotes?

  3. “How can we add re-calculation of big numbers into personal, more personal, more emotional numbers in newsroom tools?” That’s a question that Mirko Lorenz asked as a response to my talk, and it’s an excellent one. I have no answer. The easiest solution is certainly to work with colours and to show more dots than bars – but maybe there are better ways?

I’m sure there are more questions. If you know of some, write me an email (lisacharlotterost@gmail.com) or find me on Twitter (@lisacrost).


I got interested in the topic “Data Vis and empathy” thanks to a great, great Data Stories episode about Statistical Numbing. The research of Paul Slovic blew me away, so I listened to it for a second time and skimmed big parts of Paul and Scott Slovic’s book “Number and Nerves”. Their website “The Arithmetic of Compassion” gives a great overview of the key concepts. I also gained a lot of understanding into how people can deal with smaller bits of information better than with bigger bits from the paper “Small Wins” by Karl E. Weick (thanks to Jan for the tip!).

To learn more about empathy, I read and listen to Paul Bloom. E.g. his famous article “Against Empathy” and an episode of Sam Harris’ podcast on which Bloom was a guest for two very enjoyable hours (thanks to Enrico for the tip!).

To learn about the state of mind regarding the idea to evoke emotions/empathy with data vis, I found the following articles the most helpful:

Finally, and like always: Thanks to OpenNews for sending me to the CCC, and thanks to everybody who gave me hints about talks, papers, podcasts and articles about that topic.

Here’s the talk to watch right here, right now: