Robin Sloan 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.

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

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 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.

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

Analytics is not about sitting behind a computer and pushing enter and having it produce an answer.

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

If we start to use social media data sets to take the pulse of a nation or understand a crisis - or actually use it to deploy resources - we are getting a skewed picture of what is happening.

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.

The big mystery of Big Data is causation versus correlation.

It turns out it's important to build a product and not just a bunch of data models.

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.

I think of 'data science' as a flag that was planted at the intersection of several different disciplines that have not always existed in the same place. Statistics, computer science, domain expertise, and what I usually call 'hacking,' though I don't mean the 'evil' kind of hacking.

As a consultant at McKinsey, I learned the value of data and the ability to shape that information into an answer.

Statistics are just a compilation of people's stories put on paper, and legislation is our attempt to address those real challenges for people.

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

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.

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

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

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.

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.

Like Netflix, Looker started as nothing more than an idea. Lloyd Tabb and Ben Porterfield were two brilliant engineers who had figured out a better way for businesses to see and analyze their data, and they asked me to join them to help out with the ABCs - that's short for Anything But Coding.

But the indeterminate future is somehow one in which probability and statistics are the dominant modality for making sense of the world. Bell curves and random walks define what the future is going to look like. The standard pedagogical argument is that high schools should get rid of calculus and replace it with statistics, which is really important and actually useful. There has been a powerful shift toward the idea that statistical ways of thinking are going to drive the future.