A Quote by Walter A. Shewhart

Rule 1. Original data should be presented in a way that will preserve the evidence in the original data for all the predictions assumed to be useful. — © Walter A. Shewhart
Rule 1. Original data should be presented in a way that will preserve the evidence in the original data for all the predictions assumed to be useful.
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.
Data by itself is not useful. Data is only useful if it can be applied for public benefit.
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.
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.
Our original plan when we founded Waze was to sell the data, and we could keep it ad-free that way.
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.
Data isn't information. ... Information, unlike data, is useful. While there's a gulf between data and information, there's a wide ocean between information and knowledge. What turns the gears in our brains isn't information, but ideas, inventions, and inspiration. Knowledge-not information-implies understanding. And beyond knowledge lies what we should be seeking: wisdom.
The data are what matter in economics, and the more ruthlessness that an economist can summon to make sense of the data, the more useful his findings will be.
Machine learning and artificial intelligence applications are proving to be especially useful in the ocean, where there is both so much data - big surfaces, deep depths - and not enough data - it is too expensive and not necessarily useful to collect samples of any kind from all over.
Rob Engle and I are concerned with extracting useful implications from economic data, and so the properties of the data are of particular importance.
A scientist naturally and inevitably ... mulls over the data and guesses at a solution. He proceeds to testing of the guess by new data-predicting the consequences of the guess and then dispassionately inquiring whether or not the predictions are verified.
I don't believe in data-driven anything, it's the most stupid phrase. Data should always serve people, people should never serve data.
With too little data, you won't be able to make any conclusions that you trust. With loads of data you will find relationships that aren't real... Big data isn't about bits, it's about talent.
Data-driven predictions can succeed-and they can fail. It is when we deny our role in the process that the odds of failure rise. Before we demand more of our data, we need to demand more of ourselves.
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.
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