A Quote by David McCandless
There's a tendency in graphics to allow the trimming of certain parts. But I think that if you're open about your process, your methodology, such as introducing thresholds, introducing filters, techniques people use in research and data management, it's legitimate. It's legitimate to say, "We're only going to show data above this level, or between levels."
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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.
Everything you say or allow into your eyes or ears becomes data that is stored in your heart. That data is later replayed during your prayer. If you want to know what is filling your heart, look at what you think about in your prayer. If you want to guard your heart, guard your eyes, ears, and tongue.
We get more data about people than any other data company gets about people, about anything - and it's not even close. We're looking at what you know, what you don't know, how you learn best. The big difference between us and other big data companies is that we're not ever marketing your data to a third party for any reason.
The only legitimate use for a glove is to cover an injury... A desire to prevent callus formation (possibly so as to not snag one's pantyhose) does not constitute a legitimate use. And if you do insist on wearing gloves, make sure they match your purse.
Knowledge about limitations of your data collection process affects what inferences you can draw from the data.
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.
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.
The big thing that's happened is, in the time since the Affordable Care Act has been going on, our medical science has been advancing. We have now genomic data. We have the power of big data about what your living patterns are, what's happening in your body. Even your smartphone can collect data about your walking or your pulse or other things that could be incredibly meaningful in being able to predict whether you have disease coming in the future and help avert those problems.
My central thesis is that combining increased temporal and spatial resolution in MRI techniques with increasingly powerful data correlation techniques will allow the derivation of interpreted meanings from neural signals. I observed, further, that the techniques that exist already allow some correlations.
... negative feelings are not true feelings at all; rather, they are your thoughts about something, based always on the previous experience of yourself and others. You will not find Truth in your past data, only past data that is based on other past data that is based on other past data, and so forth. Forget your "past experience" and look directly at the experience you are having. Right Here, Right Now. There is your Truth.
A lot of people seem to think that data science is just a process of adding up a bunch of data and looking at the results, but that's actually not at all what the process is.
I'm going to say something rather controversial. Big data, as people understand it today, is just a bigger version of small data. Fundamentally, what we're doing with data has not changed; there's just more of it.
Uncontrolled access to data, with no audit trail of activity and no oversight would be going too far. This applies to both commercial and government use of data about people.
Chunking is the ability of the brain to learn from data you take in, without having to go back and access or think about all that data every time. As a kid learning how to ride a bike, for instance, you have to think about everything you're doing. You're brain is taking in all that data, and constantly putting it together, seeing patterns, and chunking them together at a higher level. So eventually, when you get on a bike, your brain doesn't have to think about how to ride a bike anymore. You've chunked bike riding.
Many of us now expect our online activities to be recorded and analyzed, but we assume the physical spaces we inhabit are different. The data broker industry doesn't see it that way. To them, even the act of walking down the street is a legitimate data set to be captured, catalogued, and exploited.
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.
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