A Quote by Edwin Powell Hubble
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.
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To get anywhere, or even to live a long time, a man has to guess, and guess right, over and over again, without enough data for a logical answer.
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.
Companies are getting bitten by hiring a data scientist who isn't really a data scientist.
You have to imagine a world in which there's this abundance of data, with all of these connected devices generating tons and tons of data. And you're able to reason over the data with new computer science and make your product and service better. What does your business look like then? That's the question every CEO should be asking.
The problem with data is that it says a lot, but it also says nothing. 'Big data' is terrific, but it's usually thin. To understand why something is happening, we have to engage in both forensics and guess work.
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.
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.
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.
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.
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.
First you guess. Don't laugh, this is the most important step. Then you compute the consequences. Compare the consequences to experience. If it disagrees with experience, the guess is wrong. In that simple statement is the key to science. It doesn't matter how beautiful your guess is or how smart you are or what your name is. If it disagrees with experience, it's wrong. That's all there is to it.
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.
Apple knows a lot of data. Facebook knows a lot of data. Amazon knows a lot of data. Microsoft used to, and still does with some people, but in the newer world, Microsoft knows less and less about me. Xbox still knows a lot about people who play games. But those are the big five, I guess.
EMA research evidences strong and growing interest in leveraging log data across multiple infrastructure planning and operations management use cases. But to fully realize the potential complementary value of unstructured log data, it must be aligned and integrated with structured management data, and manual analysis must be replaced with automated approaches. By combining the RapidEngines capabilities with its existing solution, SevOne will be the first to truly integrate log data into an enterprise-class, carrier-grade performance management system.
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.
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