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.
The job of the data scientist is to ask the right questions.
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.
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.
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.
I'm very lucky to work at bitly, with a data set that allows us to explore human social behavior at the scale of human social behavior.
A search is a search, even if it happens to disclose nothing but the bottom of a turntable.
A library could show you everything if you knew where to look.
On average, people should be more skeptical when they see numbers. They should be more willing to play around with the data themselves.
Personally, I find visualisations great for helping me understand the world and for sifting the huge amounts of information that deluge me every day.
The power of Ayasdi is its unique ability to automatically discover insights - regardless of complexity - without asking questions.
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.
Information is expanding daily. How to get it out visually is important.
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.
I was interested in data mining, which means analyzing large amounts of data, discovering patterns and trends. At the same time, Larry started downloading the Web, which turns out to be the most interesting data you can possibly mine.
In the fast-changing, information-filled world of the Internet, you never know what you might find. Maybe you'll discover a great price on an airline ticket, or maybe you'll come across that quote you've been racking your brain for.
I think of myself as a translator. I just change the dry, unfeeling language of data into a visual language that allows for feeling.
Computers can't find the unexpected, but people can when they eyeball the data.
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.
Every day we have teams looking for new data sets.
Ayasdi's customers can finally learn the answers to questions that they didn't know to ask in the first place.
I'm, like, an Internet fiend. I will research anything and everything that pops into my head.
I love data. I think it's very important to get it right, and I think it's good to question it.
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.
