Information is Beautiful’s David McCandless was recently invited to discuss his methods, inspirations & opinions in a free-flowing Reddit “Ask Me Anything“.

A big thank you to r/dataisbeautiful for hosting the AMA, and kudos to the Redditors who took part, for such incisive, fascinating questions.

We think it makes a terrific summary of the the Frequently Asked Questions we get at Information is Beautiful.


Rhiever asked: Can you remember a time where the use of statistics dramatically changed your opinion on something? Or disproved many of your preconceived notions about a topic?

DM: I’ve noticed that researching and visualising stats & data has substantially reduced my anxieties around air travel, infectious diseases, and sunscreen. And other such media-inflamed topics.

Working as a journalist made me a deep consumer of the media – which really does cause inflammation and increases anxiety IMHO.

Sadly, the only topic where the stats haven’t reduced my anxiety is climate change.



Zonination asked: What is the most beautiful data visualization you’ve come across?

DM: I think beauty can mean different things depending on what aspects you’re considering,

“Information is Beautiful” is a nod towards the beauty of really good visualisation – not just physical aesthetic beauty (traditional notion) but also the beauty of clarity, the beautiful structure and integrity of great data, the beauty of a great story or insight, and the beauty of using or interacting with a really great piece of design – functional beauty.

In terms of aesthetic beauty, I really like the work of Accurat, Stefanie Posavec and Nicolas Felton.


SereneScientist asked: I’d like to know what your thoughts are on the place of data visualization in academia.

DM: I think dataviz is a lens that can be turned onto any subject. The more complex, esoteric, densely academic the better, in a way, as those scientific areas often have important insights and knowledge locked away behind jargon and complexity, inaccessible to the public or even other scientists from other fields.

Done well, dataviz can act as a portal, or a looking glass, so anyone can peer in and understand or, at least start to understand, the depths of a subject.

I’d say if you’re an expert practitioner of psychology or any other complex field, then you’re the bridge. Start visualising your understanding of the field. You don’t have to be a designer. You can just sketch, create diagrams etc of what you know and want to communicate and see where it leads.


Chaosmosis asked: Do you fear that beautiful dataviz can be used for evil, as well as good? How can we balance the concern of keeping methods transparent with the desire to simplify things for popular audiences?

DM: I guess all things get used for evil eventually. You can lie with statistics and you can really lie with visualized statistics. Both the considerable power of numbers and the power of design combined. Hard to argue with.

My guard would be transparency of data and of process and of methodology. We share all our datasets for every image released. About 25% of visitors look at the sheets. There’s also a strong vocal community who, when they’re not turning their barrels on me, are quick to attack and police the “bad charts” released by political powers.




Subzero1988 asked: Where do you take inspiration from for your visual work? How long can you take working on perfecting it?

DM: I’m always revising my work – much to the annoyance of my co-workers. I don’t really see any image as “finished”, especially with so much data evolving.

I have a big collection of about 3000+ data-visualisations and designs on my hard-disk that acts as a giant moodboard. When I’m looking for inspiration, I just dip my head into that folder and pull out pieces that excite me around form, colour, typography, style. They form a moodboard that usually informs the visual I then work on. I’m always adding to the folder, looking at new work, collecting images etc.


Frostickle asked: How much time goes into looking for new interesting data sets vs. cleaning the data vs. writing code/visualising the data vs. doing the write up?

DM: It’s usually around 80% data, 20% visual & design. Typically might break down like this (% overall time taken):

  • Have an idea, shape it (2%)
  • Research data and sources (10%)
  • Clean, arrange and understand data (10%)
  • Realise data is opening up new questions, research those (20%)
  • Realise data is contradicting original assumptions, correct those and reform (20%)
  • Shape final data into story (15%)
  • Fact-check (3%)
  • Create a visual moodboard (2%)
  • Sketch 2-3 possible visual routes (3%)
  • Starting design (one route usually comes out on top) 10%
  • Style, artwork and polish (3%)
  • Fact-check and release (2%)

The design / visualisation bit goes very quickly if you’ve really, deeply understood the data, the idea, why you’re doing it and the story that emerges from the data. You really don’t design data when you visualise. You design *your understanding* of the data.


SlySpyder13 asked: I remember asking Nate Silver about using Excel as a visualization tool and he seemed to be a big fan. What do you think? Other than that, what are your favorite visualization tools?

DM: Yeah I like Excel and have used it for years – much to my family’s chagrin (I use spreadsheets for everything). I like its swiftness and use it as a sketch pad to quickly do plots etc.

I’ve been playing with RAW lately (a nice easy web frontend for D3) and Plot.ly looks good for scientific stuff. There’s a big hunger for tools and I hope to release one myself.

[NB: Information is Beautiful is working on a browser-based visualization app called VizSweet. To be alerted when it’s released, sign up on the VizSweet web page.)



A_contact_juggler asked: How do you respond to criticisms that you create and promote chartjunk?

DM:I think this term is too broadly applied and stems from a misunderstanding of the various reasons for creating a visualisation. Firstly, using colour, usual or inventive forms, illustrative motifs, interesting type doesn’t automatically make something chartjunk – especially if the goal is to engage or get attention or simply be creative with the form. Those are legitimate aims.

But if you goal is to communicate quickly and efficiently to harried executives or business leaders or for your printer to use less ink, then of course, colour, extraneous graphics – or even style – are secondary to your intention and can be, probably should be, discarded.

In a rich and varied developing field such as dataviz, there’s plenty of room for many different intentions, many different audiences, many different goals and lots of experimentation. Some experimental or creative output will be chartjunk and criticised. Just like some canonical or classic output will be boring and ignored.


Laloeka asked: Isn’t one of the biggest risks in data visualisation incorrectly visualizing data because the nature of the data is unknown to the “artist” making the infographics? 

DM: Well, I start from a place of not fully understanding and then my journey (and challenge) is to research and inquire, assemble and verify, until I have a deeper and more comprehensive understanding of the topic.

It’s useful sometimes to come from an more “ignorant”, naive perspective as then you ask the simple, obvious questions that might be assumed by a technical audience with prior knowledge. That way, the graphic you shape hopefully covers all the bases and gives a fair, reliable and rounded view of a given topic – rounded by trying to smooth out ignorance wherever it pops up. The net result is that it may speak to as a broad an audience as possible. This process, I think, is popularly called journalism or data-journalism in this case.


Timaronan asked: A big problem in data storytelling is manipulating data purely to prove your point. Do you have any signs or patterns that you point to to say, “This is the wrong direction”, or “That conclusion is unfair”, or similar statements that help ensure fairness and strength in your conclusions?

DM: Often I start out with an idea which has an agenda, conscious or unconscious. Then as I start to examine the data and the reality, my idea start to feel the “gravity of the truth” and start to bend in a different direction. It’s my decision, at that point, as an author / creator to either go with the momentum or stick to my point. There have been several cases where I’ve had to confront my own biases to bring an image to fruition. Personally I like that kind of cognitive workout. It’s a bit of a journalistic conceit that you can be purely objective. Other people, I guess, may choose to go to the “dark side”. The team of talented people I work with keep me in check.




Unchandosoahi asked: How do you decide to be a visualization designer?

DM:I worked for twenty years as a writer & print journalist and gradually, as I worked more and more online, designed more and more websites, played more and more video games, my thinking and imagining and understanding became increasingly visual.

Then it felt more natural – and exciting – and effective to visualise the results of my research, rather than encode it into writing. So, gradually, explicably, I morphed into this new beast: a designer-writer. A visualization-designer? A graphist?

I still see myself as a journalist. I see a lot of similarities between information design and journalism. Both set about to condense and optimise information into tight, understandable, elegant form, telling stories and helping others to navigate.


Jackedsquat asked: What advice can you offer to those people who want to learn to visualize data creatively and turn it into a career?

DM: Definitely sharpen up those statistics data science skills. I’d also say work on your information skills – that is journalism: story-telling, writing & communication, asking questions, developing concepts, and learning a refined sense of what is interesting. If you can analyse data and then tell a compelling, interesting story about what you’ve found, WIN.