Clive Granger Quotes

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 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, 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.

The statistics might have a Eurosceptic cast, but they are not exactly a fun read. Few of us want to wade through ONS graphs or European Commission tables.

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.

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

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.

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.

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.

The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.

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.

The big mystery of Big Data is causation versus correlation.

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

A lot of people seem to think that data science is just a process of adding up a bunch of data and looking at the results, but that's actually not at all what the process is.

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

Networks love data. They love to be able to look at numbers and try to predict what they think will work.

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 next darwin is more likely to be a data wonk than a naturalist wandering through an exotic landscape.

Every day we have teams looking for new data sets.

I learned analytics. It's a point of reference, of information. It has to be a factor in decision making.

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

Every day we go over data and use science and data to drive policy and decision-making.