Sendhil Mullainathan Quotes

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.

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.

There's been the emergence of a philosophy that big data is all you need. We would suggest that, actually, numbers don't speak for themselves.

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.

Big data is mostly about taking numbers and using those numbers to make predictions about the future. The bigger the data set you have, the more accurate the predictions about the future will be.

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.

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.

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

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.

The power of Ayasdi is its unique ability to automatically discover insights - regardless of complexity - without asking questions.

My job is to analyze our data set to understand it and build products on it. I look at raw data, do the math to clean it up, and build systems to make it easy to understand.

In disciplines as disparate as baseball, financial services, trucking and retail, people are realizing the power of data to help make better decisions.

You need to use data science and machine learning to get the ground truth of what's happening inside of a company.

While data can only tell you what has happened in the past, it can in some ways give you a sense of what might be of interest to an audience in the future.

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

Data is the fabric of the modern world: just like we walk down pavements, so we trace routes through data, and build knowledge and products out of it.

It's difficult to imagine the power that you're going to have when so many different sorts of data are available.

There's definitely a huge opportunity for businesses to transform their operations and decision making by using data.

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

There is a reasonable concern that posting raw data can be misleading for those who are not trained in its use and who do not have the broader perspective within which to place a particular piece of data that is raw.

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.

Most of 'big data' is a fraud because it is really 'dumb data.'

We're rapidly entering a world where everything can be monitored and measured. But the big problem is going to be the ability of humans to use, analyze and make sense of the data.

When human judgment and big data intersect there are some funny things that happen.

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