A Quote by Stephen Cambone

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.
Our data has been harvested, collected, modeled, and monetized - sometimes sold on as raw data, and sometimes licensed just for advertisers to be able to target us.
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.
We are ... led to a somewhat vague distinction between what we may call "hard" data and "soft" data. This distinction is a matter of degree, and must not be pressed; but if not taken too seriously it may help to make the situation clear. I mean by "hard" data those which resist the solvent influence of critical reflection, and by "soft" data those which, under the operation of this process, become to our minds more or less doubtful.
Data are becoming the new raw material of business.
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.
When dealing with data, scientists have often struggled to account for the risks and harms using it might inflict. One primary concern has been privacy - the disclosure of sensitive data about individuals, either directly to the public or indirectly from anonymised data sets through computational processes of re-identification.
Part of my responsibility as an officer was to oversee a team of analysts charged with synthesizing all of the data points on the map to see how one related to another. By bringing those data points together, a broader picture could be drawn and a strategy developed to counter the existing threat.
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.
The NSA is not listening to anyone's phone calls. They're not reading any Americans' e-mails. They're collecting simply the data that your phone company already has, and which you don't have a reasonable expectation of privacy, so they can search that data quickly in the event of a terrorist plot.
My study is NOT as a climatologist, but from a completely different perspective in which I am an expert … For decades, as a professional experimental test engineer, I have analyzed experimental data and watched others massage and present data. I became a cynic; My conclusion - 'if someone is aggressively selling a technical product who's merits are dependent on complex experimental data, he is likely lying'. That is true whether the product is an airplane or a Carbon Credit.
I was interested in data mining, which means analyzing large amounts of data, discovering patterns and trends. At the same time, Larry started downloading the Web, which turns out to be the most interesting data you can possibly mine.
The goal is become the top person on 'Raw,' the example on 'Raw,' the John Cena, the ultimate workhorse of 'Raw.'
We should always be suspicious when machine-learning systems are described as free from bias if it's been trained on human-generated data. Our biases are built into that training data.
Rob Engle and I are concerned with extracting useful implications from economic data, and so the properties of the data are of particular importance.
The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.
We use nearly 5 thousand different data points about you to craft and target a message. The data points are not just a representative model of you. The data points are about you, specifically.
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