Data Complexity Quotes

The more data we have, the more likely we are to drown in it.

Collecting intelligence information is like trying to drink water out of a fire hydrant. You know, in hindsight It's great. The problem is there's a million dots at the time.

Confusion and clutter are failures of design, not attributes of information. And so the point is to find design strategies that reveal detail and complexity - rather than to fault the data for an excess of complication. Or, worse, to fault viewers for a lack of understanding.

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

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.

The Pentagon suffers from too much Intelligence. They can't use what they get because there is too much collected. It would almost be better if they didn't have it because it's difficult to find out what's important.

No observational problem will not be solved by more data.

As long as the centuries continue to unfold, the number of books will grow continually, and one can predict that a time will come when it will be almost as difficult to learn anything from books as from the direct study of the whole universe. It will be almost as convenient to search for some bit of truth concealed in nature as it will be to find it hidden away in an immense multitude of bound volumes.

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

The trouble with facts is that there are so many of them.

The human genome contains so much data that, it has been calculated, it would fill 43 volumes of Webster's International Dictionary.

Dropbox looks really simple to the end user and is extremely magical and just works. But under the hood, the complexity of the technology is huge. The amount of work it requires to store, scale and move this data is pretty intense.

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.

Real data is messy. ...It's all very noisy out there. Very hard to spot the tune. Like a piano in the next room, it's playing your song, but unfortunately it's out of whack, some of the strings are missing, and the pianist is tone deaf and drunk- I mean, the noise! Impossible!

I think you can have a ridiculously enormous and complex data set, but if you have the right tools and methodology then it's not a problem.