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:

What I Wrote About In 2016


2016 was an intense year. In global news, Brussels, Brexit, Trump and Pokémon GO happened. In superduber-local news (meaning, in news regarding me), I spent nine months in Washington, DC as an OpenNews Fellow for NPR, went to more conferences than ever, gave more conference talks than ever, met amazing people and even talked with them, hiked up a 14k mountain, went to the Southern Hemisphere for the first time (I went in Summer! But there was Winter!) and thought about stuff.

Here I’ll present the questions I pondered in 2016, and my blog posts that resulted out of this pondering:

1 What is a good (data vis) tool?
2 What implications has the concept of Map vs Territory?
3 How does the US election work?
4 How to communicate feelings?
5 What is the purpose of journalism?
6 Do we all need to fight against world hunger with our skills?
7 What’s the point of visualizing self-tracked data?

Let’ start simple:

1 What is a good (data vis) tool?

I don’t have a Google Analytics Tracker installed on my blog; but I’m 68.3% sure that my very practical posts about data vis were the most visited.

After spending a few hours writing a post about tools for choosing colors in data vis (with a deceptive title that suggests that this post is a manual for choosing colors)… image

…I aimed higher. I decided to recreate the same bubble chart with a few tools that were known and unknown to me. “Can’t be that hard”, I thought. “Won’t take that long”, I thought.

I ended up spending two full weeks recording GIFs, doing tutorials, checking Stack Overflow and GitHub Issues. The result were three posts. In the first post, I tried 12 charting libraries: image

In the second post, I tried 12 data vis apps: image

And the third post, I went high-level and compared the tools with each other (half a year later): image

Do I use different tools now than I did before? No. Was that the goal? No. I’m happy to have a better overview of the field, and I was happy to share that overview with all of you. And the best thing about writing the post was having an excuse to have conversations with great tool makers like Christopher Groskopf or Ben Fry.

2 What implications has the concept of Map vs Territory?

The prize for my most-loved-concept 2016 goes to the idea of map and territory. It’s an old idea. But one I really learned to appreciate this year.

I got so excited about it that I wrote an explainer of the concept early this year: image

…and then came back to it in a talk at NACIS in October: image

It’s an idea that made me think about the way I look at the world and how I interact it. And it definitely influenced my “Mission Statement” you’ll encounter if you’ll read on.

A very practical implication for the concept of “the map is not the territory” were this years’ election (map):

3 How does the US election work?

My brain started caring about the US election when everybody else was already tired of it. But then I couldn’t stop thinking about it. Once I understood the difference between the electoral votes and the popular votes, I was hooked. I wrote a general post about why I think that we should (also) map the popular votes: image

After the election, I added a post with the actual election results in popular votes: image

I didn’t get as much feedback as I hoped I would, but that’s ok. I guess everybody was just too busy making the most amazing election maps ever. I can live with that.

4 How to communicate feelings?

Feelings are hard, man. Also blurry. And necessary. This year I bravely marched into the unknown fields of writing absolute not-criticizable posts about my feels.

I started with explaining “How I feel when I have a conversation”– which was extra tricky, because it was about privacy issues and I don’t know a thing about privacy. image

I continued with talking/gif-ing about “How I Feel When I Learn To Code”image

…and ended the year with a love letter to drawing thoughts and using pen and paper. image

All these topics have been covered by smart people long before me. But I found writing these posts incredible helpful. In the best case, these posts built a compelling narrative for me – and let people compare their experiences with mine (as I’ll explain a bit later in the part “What’s the point of visualizing self-tracked data?”). Win-Win!

5 What is the purpose of journalism?

At the beginning of the year I went to the Responsible Data Forum in NYC. Coming from my “data vis for journalism” bubble, the mostly advocacy-driven attendees opened up a new field for me. One exchange with Steve Lambert during some team work resonated especially strong with me. When I asked him “So what’s the goal of our data vis here? What should it accomplish?”, he said “Change, of course.” I was blown away. And jealous: NGOs and activists have very clear goals; journalism lacks them. What IS the goal of journalism, really? To answer that question, I read lots of mission statements of newspapers, had discussions with smart people like Brian Boyer, Gregor Aisch and Alan Smith, asked Twitter and drew diagrams like the following: image

I also gave a talk at my maybe favorite conference this year, Information+; about how I want journalism to focus on context instead of anecdotes: image

But I found a truly satisfying answer to the question why journalism matters in a slightly related question:

6 Do we all need to fight against world hunger with our skills?

A highlight of this year was discovering Polygraph. Its creator, Matt Daniels is obviously very talented. But something felt wrong about it. In his articles, he drew attention to words that rappers use in their lyrics; instead of to the main issues on this planet (as Hans Rosling or Max Roser would do). I couldn’t understand why Matt didn’t use his amazing talents for the greater good. In an email conversation, he replied to my discomfort with the following words:

I’d argue that every project that I’ve done has had impact – it just depends on how you qualify “impact.” Take the rapper vocab project for example: Teachers have used it as a way to engage students in math/literature, grounded in something kids care about. Numerous people have emailed me that they were inspired to pursue coding, design, and data viz via that project. It’s been used to legitimize hip hop, which is widely marginalized in the realm of intellectual culture.

For me, that was mind blowing. He doesn’t just state that “impact” is a very, very blurry concept (lots of concepts are). He also implies that you can’t always foresee what the positive consequences of your work will be – but you can trust people to use your work for their goals. These goals might not be your top priorities (“engage students in math”), but they are THEIR first priority. It means that people can use and interpret you work in different ways. I can see something in a different way than the artist intended, and it can still be extremely valuable for me. And such an artwork could have a huge impact on me.

This epiphany made me more comfortable about my own (not-big-problems-like-hunger-solving) work. And I saw it as a good-enough purpose for journalism: Journalists try to educate people who are in different positions and will use the same information differently.

It also resulted in the first draft of a “Mission Statement” for my work. In my daily blog, I wrote down what kind of things I want to create:

  • Something that resonates with me and makes me feel something, like music.
  • Something that’s beautiful and sophisticated.
  • Something that makes me wonder.
  • Something that makes me feel astonished.
  • Something that shows the map of the world I have.
  • Something that sets my map in context to other maps out there.
  • Something that sets my map in context to the territory out there.
  • Something that shows me that maps are maps and not the territory.
  • Something that shows me the world from a different perspective.
  • Something that challenges what I know about the world.

These goals don’t want to be fully achieved, but always aimed for. Doing this was my main intentions for my two election map posts, and for a little visual experiment that asked: Which Cities Are On Similar Latitudes? image

Turns out, challenging beliefs is one of my favorites hobbies. I’ll try to come up with more ideas that will do so in the future.

7 What’s the point of visualizing self-tracked data?

People who have read my blog exactly a year ago might remember my issues with Self Tracking and its visualisation. “Self-tracking results in irrelevant visualisations which give a fake comfort of meaning, but don’t help neither the tracked person nor the viewer”, I wrote a bit bitter. As somebody who has done some quantified selfing myself, I wanted to see a point in it, but just couldn’t.

Until I met Nicolas Felton at Resonate in Belgrade. Finally I found the perfect guy to ask. His most appealing argument in our conversation was that readers can compare their lives with the life of the self-tracker. That mirror-effect works similar to a fiction piece – but instead of asking yourself “Would I have been brave enough to destroy that spaceship from within?”, you ask “Do I have more or less social contact than this guy? Am I exploring my city less? Do I exercise more?”. I was so happy with that answer that I drew another little diagram: image

And it made me feel better about so many projects I encountered in 2016.

Last, and also least?

I don’t think that every post I wrote is the best post I’ve ever written, but I’m still glad I wrote them. Here are the posts that truly deserve to be mentioned at the very bottom of this Year In Review article:

An explanation of Information Theory and its definition of “information”, applied to American vs. German small talk: image

“How I organize my files”. A friend asked me to write about that. I’m looking forward to look back at it in 20 years: image

At the end of October, I gave an experimental session I titled “Let’s Ask All Our Embarrassing Data Questions”. A few days later, I wrote about what went well and what didn’t: image


And that was it! 2016. Thanks to all the amazing people I met at all the conferences I’ve visited (Responsible Data Forum, Tapestry, NICAR, International Journalism Festival, Resonate, Data Journalism Unconference, Information+, Netzwerk Recherche, SRCCON, Hacks/Hackers Media Party Buenos Aires, NACIS, MozFest). Thanks to everyone who gave me an opportunity to give a talk or to talk through ideas. Thanks to everyone who retweeted my work and/or told me what they got out of it; it made my day every single time. Thanks to everyone who pushed me and/or the data vis field forward in 2016. And thanks to everyone who read an article or two on this blog.

Let’s do everything even better in 2017! If you want me to cover anything specific on this blog, write me an email (lisacharlotterost@gmail.com) or find me on Twitter (@lisacrost).