Hilary Mason Quotes

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

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.

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.

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.

You and I are streaming data engines.

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?

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.

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.

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.

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.

There's a very low amount of data literacy around people.

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.

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

Data is cost. It takes money to create data, store it, clean it, and throw resources at it to learn anything from it.

Big data sets are never complete.

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.

It's amazing how much data is out there. The question is how do we put it in a form that's usable?

Big data is indeed a buzzword but it is one that is frankly under-hyped.

Everyone talks about how much data's in the world. Except, actually, 80% of it is pretty blind to computers. I mean, it can store it. But if it's a movie, a poem, a song, it doesn't know what it's actually saying or doing.

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