Aaron Koblin Quotes

The people who are thinking most about big data right now are corporations and governments.

Our ability to do great things with data will make a real difference in every aspect of our lives.

People are saying, 'Big Data is the new oil.'

Technologies like Ayasdi's exist now to automatically discover information from data without having someone making guesses up front.

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

Big data has been used by human beings for a long time - just in bricks-and-mortar applications. Insurance and standardized tests are both examples of big data from before the Internet.

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

The ability to collect, analyze, triangulate and visualize vast amounts of data in real time is something the human race has never had before. This new set of tools, often referred by the lofty term 'Big Data,' has begun to emerge as a new approach to addressing some of the biggest challenges facing our planet.

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.

If you have a lot of data and you want to create value from that data, one of the things you might consider is building up an AI team.

We are going to completely change what it means to do advanced analytics with our data solutions. We have machine-learning stuff that is about really bringing advanced analytics and statistical machine learning into data-science departments everywhere.

I'm going to say something rather controversial. Big data, as people understand it today, is just a bigger version of small data. Fundamentally, what we're doing with data has not changed; there's just more of it.

The premise and promise of Big Data is that there are no stories, only patterns; that the human preference for story is aligned with the human tendency for error; and that only through dislocations in scale - the scale of sample size and of time - will truth emerge.

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

The big mystery of Big Data is causation versus correlation.

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.

Data science is the combination of analytics and the development of new algorithms.

I like to say I've been working on big data for so long, it used to be small data when I started working on it.

We know now data is so powerful, and you can learn so much about yourself and creating product with data.

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.

Big Data is just that - big. But, it's a term that is largely misunderstood and difficult to explain.

There are a number of fascinating stories included in 'The Human Face of Big Data' that represent some of the most innovative applications of data that are shaping our future.