Kate Crawford Quotes

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

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.

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

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.

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.

Amazing things happen when you pull individual pieces of information together into larger linked datasets: meaning emerges, as you produce facts from figures.

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.

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.

Data will always bear the marks of its history. That is human history held in those data sets.

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.

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.

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.

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

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

It is easy to lie with statistics. It is hard to tell the truth without it.

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

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.

For me myself, I feel it's always interesting gathering data. I have my team who do that. I think they feed me through specific things that I might find valuable.