A Quote by Michael Crichton

In my view, our approach to global warming exemplifies everything that is wrong with our approach to the environment. We are basing our decisions on speculation, not evidence. Proponents are pressing their views with more PR than scientific data. Indeed, we have allowed the whole issue to be politicized-red vs blue, Republican vs Democrat. This is in my view absurd. Data aren't political. Data are data. Politics leads you in the direction of a belief. Data, if you follow them, lead you to truth.
Any time scientists disagree, it's because we have insufficient data. Then we can agree on what kind of data to get; we get the data; and the data solves the problem. Either I'm right, or you're right, or we're both wrong. And we move on. That kind of conflict resolution does not exist in politics or religion.
Go out and collect data and, instead of having the answer, just look at the data and see if the data tells you anything. When we're allowed to do this with companies, it's almost magical.
I mean a global warming advocate is as wrong as anybody could be about anything, folks. It's a hoax. There is no man-made global warming. It has been thoroughly debunked. The fact that it's a hoax has been proven, by them. E-mails that were uncovered at East Anglia University in Great Britain show that they worked together to perpetuate the hoax, that they lied about data, that they eliminate data that contradicted their political belief. So you'd have to assume from that that they are political advocates disguised as scientists who are purposely engaging in misinformation.
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.
As our society tips toward one based on data, our collective decisions around how that data can be used will determine what kind of a culture we live in.
Scientific knowledge is, by its nature, provisional. This is due to the fact that as time goes on, with the invention of better instruments, more data and better data hone our understanding further. Social, cultural, economic, and political context are relevant to our understanding of how science works.
Scientific data are not taken for museum purposes; they are taken as a basis for doing something. If nothing is to be done with the data, then there is no use in collecting any. The ultimate purpose of taking data is to provide a basis for action or a recommendation for action. The step intermediate between the collection of data and the action is prediction.
TIA was being used by real users, working on real data - foreign data. Data where privacy is not an issue.
Everything is changing now that we are in the cloud in terms of sharing our data, understanding our data using new techniques like machine learning.
The biggest mistake is an over-reliance on data. Managers will say if there are no data they can take no action. However, data only exist about the past. By the time data become conclusive, it is too late to take actions based on those conclusions.
MapReduce has become the assembly language for big data processing, and SnapReduce employs sophisticated techniques to compile SnapLogic data integration pipelines into this new big data target language. Applying everything we know about the two worlds of integration and Hadoop, we built our technology to directly fit MapReduce, making the process of connectivity and large scale data integration seamless and simple.
People think 'big data' avoids the problem of discrimination because you are dealing with big data sets, but, in fact, big data is being used for more and more precise forms of discrimination - a form of data redlining.
My answer to someone who is in contrast with me - by not seeing God in the scientific data - is that you don't see God in the scientific data because you're not me. I have other experiences than you have, that bring me to look at this data as enriching my experience of God.
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.
A data scientist is that unique blend of skills that can both unlock the insights of data and tell a fantastic story via the data.
Data!data!data!" he cried impatiently. "I can't make bricks without clay.
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