David McCandless Quotes

Information is expanding daily. How to get it out visually is important.

I think of myself as a translator. I just change the dry, unfeeling language of data into a visual language that allows for feeling.

Data, I think, is one of the most powerful mechanisms for telling stories. I take a huge pile of data and I try to get it to tell stories.

There's something that happens with the collection of a large amount of data when it's dumped into an Excel spreadsheet or put into a pie chart. You run the risk of completely missing what it's about.

The problem with data is that it says a lot, but it also says nothing. 'Big data' is terrific, but it's usually thin. To understand why something is happening, we have to engage in both forensics and guess work.

The greatest moments are those when you see the result pop up in a graph or in your statistics analysis - that moment you realise you know something no one else does and you get the pleasure of thinking about how to tell them.

Personally, I find visualisations great for helping me understand the world and for sifting the huge amounts of information that deluge me every day.

Some of the pictures in 'The Human Face of Big Data' will bring tears to your eyes; others are so surprising or memorable that you just have to show them to your friends and family.

I love data. I think it's very important to get it right, and I think it's good to question it.

A journalist and an information architect face exactly the same problem - how to give shape to the pile of information in front of you in a way that will make it easy and natural for people to comprehend. I can't imagine any better preparation for the work I do now.

On average, people should be more skeptical when they see numbers. They should be more willing to play around with the data themselves.

Go out and collect data and, instead of having the answer, just look at the data and see if the data tells you anything. When we're allowed to do this with companies, it's almost magical.

Computers can't find the unexpected, but people can when they eyeball the data.

The job of the data scientist is to ask the right questions.

People believe the best way to learn from the data is to have a hypothesis and then go check it, but the data is so complex that someone who is working with a data set will not know the most significant things to ask. That's a huge problem.

Numbers can't speak for themselves, and data sets - no matter their scale - are still objects of human design.

The biggest challenge in big data today is asking the right questions of data. There are so many questions to ask that you don't have the time to ask them all, so it doesn't even make sense to think about where to start your analysis.

Data and data sets are not objective; they are creations of human design. We give numbers their voice, draw inferences from them, and define their meaning through our interpretations.

To learn something from your data, the forming of a hypothesis lies with the human being, which turns into a query, which becomes a result. The problem is that there are too many queries to make, too many questions to ask.

Visualizing information is a form of knowledge compression.

Above all else show the data.

Allowing artist-illustrators to control the design and content of statistical graphics is almost like allowing typographers to control the content, style, and editing of prose.

Some of the commercial work I do is helping people to improve their presentations and add some design thinking. There are so many amazing things in science, and such great data, which can often be locked away. It's in the minds of these amazing practitioners, who can't necessarily express what they want in a visual way.

Without the hard little bits of marble which are called 'facts' or 'data' one cannot compose a mosaic; what matters, however, are not so much the individual bits, but the successive patterns into which you arrange them, then break them up and rearrange them.

The very best [infographics] engender and facilitate an insight by visual means - allow us to grasp some relationship quickly and easily that otherwise would take many pages and illustrations and tables to convey. Insight seems to happen most often when data sets are crossed in the design of the piece - when we can quickly see the effects on something over time, for example, or view how factors like income, race, geography, or diet might affect other data. When that happens, there's an instant "Aha!".