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Challenging The Big Data Narrative: “Most data isn’t ‘big’…”

the nonsense between earth and sky (NYC)Bandwagons make for a smooth ride, but not normally to useful insight.  Their opposite, however, in the form of scattered voices of the “anti-bandwagon” often don’t help much either when reflex contrarians don’t offer much more than complaints. While looking for a solid counter to a few of the popular big data narratives, I found this piece, “Most data isn’t big… and businesses are wasting money pretending it is” by Christopher Mims on Quartz, a “digitally native news outlet for the new global economy.” Mims offers 4 warnings – and examples to support – sourced from data scientists.

For our purposes, making sense of it all (perhaps, untangling the image above), the piece signals where we’re situated in this phenomenon more than it is a rebuttal of big data. As is typical, a heavy weight of  comment on tech topics starts with “how to apply it,” but to start there jumps ahead of a few basic questions that are outlined in the article: Does it apply to my work? What will it cost for what I get out of it? Am I ready to dive into all of what big data implies – “a number of tough disciplines—statistics, data quality, and everything else that comprises ‘data science.’”

Of course, we could find counters to some of Mims’ scenarios. For example, on the point of cost, there are relevant and low-cost big data tools that make it a reasonable proposition even for small budgets. (We’ll post one next week.) But his overall point makes a bigger statement on the need to gain a viable working perspective on big data in a practical context. We can use that.

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