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What about the data that should never, ever get in your attention? According to Wharton's Peter Fader, the least valuable data is the noisiest in the Big Data space: social and mobile.
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Why is it we can predict a consumer's propensity to read Hunger Games, upgrade their iPad or download music featured on the Voice, yet we fail miserably at predicting life-threatening events, such as developing breast cancer?
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"At this very moment, there’s an odds-on chance that someone in your organization is making a poor decision on the basis of information that was enormously expensive to collect." -- Corporate Executive Board
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Andreas Weigend's 8 Rules for Big Data are spot-on. If your organization's Big Data conversations center on the processing of big data, you are missing the point.
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Are we in IT so busy managing everyone else's data, that we forget to use data for own decisions?
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Using self-tracking, an eclectic mix of early adopters, fitness freaks, technology evangelists, personal-development junkies, hackers and patients are converting everyday activities into data points in order to improve their lives. Could it also improve their bank accounts?
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If we are to classify contextless data dirt, this first case illustrates superfluous, distracting data. I posit that an equally insidious type of data dirt forms when data is analyzed without regard to origin, business-system and business goal contexts.
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For the vast majority, Lin's breakthrough is a complete surprise. However, for numbers hobbyist Ed Weiland, Lin's breakthrough was merely a matter of time.
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In a way, you could say data science leans towards innovation, while business intelligence leans towards optimization. Each are critical for business, government and societal progress.
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When you think about the human resistance in adopting data-driven decision-making, or really any change, at the root is the me question. What is the impact on my job, my span of control, my future opportunities?
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