Tom Stoppard Quotes

I love data. I think it's very important to get it right, and I think it's good to question it.

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.

I'm convinced you can combine this with reporting integrity and accuracy.

Whether or not I like a piece of data has very little bearing on whether or not I am likely to accept it.

If the data do not prove that indexing wins, well, the data are wrong.

One of the myths about the Internet of Things is that companies have all the data they need, but their real challenge is making sense of it. In reality, the cost of collecting some kinds of data remains too high, the quality of the data isn't always good enough, and it remains difficult to integrate multiple data sources.

The data stream has been corrupted, return to first principles.

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.

I would only have been too pleased if someone had asked me for my data. If you really believed in your data, you wouldn't mind someone looking at it. You should be able to respond that if you don't believe me go out and do the measurements yourself.

The individual source of the statistics may easily be the weakest link.

Being flooded with information doesn't mean we have the right information or that we're in touch with the right people.

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

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

The importance of information is directly proportional to its improbability.

We think we have the best matching algorithm, we think we have the best members. So why wouldn't we want to just shine the light onto just how our processes work, what the real data are, and let people come to their own conclusions.

Now we have a whole separate supplier of data for Able Danger who's verifying that same information.

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

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.

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

Biases and blind spots exist in big data as much as they do in individual perceptions and experiences. Yet there is a problematic belief that bigger data is always better data and that correlation is as good as causation.

The bigger a data set that you have, the more polls, the more surveys that you have that people undertake, the more accurate your models are going to be. That's just a fact of data science.

Hidden biases in both the collection and analysis stages present considerable risks and are as important to the big-data equation as the numbers themselves.

Companies that rely on licensing a proprietary dataset should expect to be outpaced by competitors using modern data collection techniques and more frequent updates and greater accuracy.

We should be cautious about embracing data before it is published in the academic press, and must always avoid treating correlation as causation.

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.