Aaron Koblin Quotes

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.

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.

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.

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

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

Every day we have teams looking for new data sets.

If we just focus on stats, we're gonna eventually not have them.

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.

The impulse to collect data preceded the ability to make sense of it. People facing a complicated problem measure whatever they can easily measure. But the measurements by themselves don’t lead to understanding.

By visualizing information, we turn it into a landscape that you can explore with your eyes: a sort of information map. And when you're lost in information, an information map is kind of useful.

There is a rampant tendency in any industry where someone is trying to sell something with a bunch of data, where they cherry pick a little bit... bias a little bit. This becomes quite easy when there is an enormous amount of data to cherry pick from.

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.

Here's the thing: If you're monitoring every single thing that goes on in a given culture, if you have all the information that is there to be had, then that is the equivalent of having none of it. How are you going to process that amount of information?

Social media has given companies access to unprecedented amounts of information on client behavior and preferences - so-called Big Data. But making sense of it all and turning it into actionable policy has been elusive.

The Government are very keen on amassing statistics - they collect them, add them, raise them to the nth power, take the cube root and prepare wonderful diagrams.

We're not in an information age anymore. We're in the information management age.

There's a lot of power in executing data - generating data and executing data.

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

Rob Engle and I are concerned with extracting useful implications from economic data, and so the properties of the data are of particular importance.

We have entered an era where the kinds of data and analyses that people run have gotten incredibly complicated. So you have researchers using sophisticated software programs that they probably don't understand but are generally accepted and everyone [in the field] uses.

Good data is hard to find. Hard data is good to find.

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