Big Data’s 2017: Can more meta thinking free us from current malaise?

Every year, a wide array of Big Data companies send me their executives’ predictions for the upcoming year. It’s fun to compile them, then read through them, and see what everyone has to say. There’s fodder for heckling as some of the predictions are in outright contradiction of each other, and there’s ample opportunity for over-confident analysis when two or more predictions corroborate each other. Extracting insightsWhat I prefer to do, though, is look for themes that tie the predictions together and provide some taxonomy for understanding the full range of them. That way, even those items that seem contradictory at a superficial level can together shed light at a deeper level, and some consensus can emerge. And even if the consensus predictions may end up being incorrect, we nonetheless…


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