A Quote by Bruce H. Lipton
Our problems are not with the data, itself, but arise from our interpretation of the data.
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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.
All of these technologies that we are putting together... our memory technology, our CPU, our graphics architecture, our GPUs - all that is being applied to where the data is. You can almost predict where Intel will be in the future. It will be where data resides.
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.
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.
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.
Everyone knows, or should know, that everything we type on our computers or say into our cell phones is being disseminated throughout the datasphere. And most of it is recorded and parsed by big data servers. Why do you think Gmail and Facebook are free? You think they're corporate gifts? We pay with our data.
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.
As we become so visible in the digital world and leave an endless trail of data behind us, exactly who has our data and what they do with it becomes increasingly important.
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.
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.
There are a number of fascinating stories included in 'The Human Face of Big Data' that represent some of the most innovative applications of data that are shaping our future.
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.
Census data influences decisions made from Main Street to Wall Street, in Congress and with the Federal Reserve. Not to mention, the American people who look to, and trust, the data the government releases on our nation's unemployment, state of our economy, and health insurance coverage.
Data and data sets are not objective; they are creations of human design. We give numbers their voice, draw inferences from them, and define their meaning through our interpretations.
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