How “Big Data” Went Bust | Slate

How “Big Data” Went Bust; ; In Slate; 2017-10-16.
Teaser: And what comes next.

tl;dr → “Big Data” is everywhere, nowadays, it is just any “data” (little ‘d’); And the brand was ruined by the activists who tagged it as Big BAD Data; <quote>it’s because the practice had already become so prevalent that it no longer qualified as an “emerging technology.”</quote>
and → Big Data is Facebook; Facebook is bad.
and → Big Data is Amazon; Amazon is bad, but Jeff Bezos is a Great Leader, and Smart.
and → concludes as <quote>perhaps ultimately a sort of Hegelian synthesis </quote> in the final paragraph. <snide> Mistakes will be made, only time will tell, told ya so!</snide> Yup. It’s a Freshman Seminar essay.

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Mentioned

  • “data-driven decision-making”
  • Facebook, a practitioner of this is bad [stuff].
  • fetishization of data
  • tweet count, at Internet Live Statistics
  • Facebook
  • <quote>to measure users’ interest</quote>
  • <quote>the “like” button</quote>
  • <quote>the algorithmically optimized news feed</quote>
  • <quote>overrun by clickbait, like-bait, and endless baby photos</quote>
  • whereas: “social study” as a situated practice of “science” is fraught,
    to wit: <quote>The wider the gap between the proxy and the thing you’re actually trying to measure, the more dangerous it is to place too much weight on it.</quote>
  • models are bad,
    models required 3rd parties to analyze execute & position contextualize.
  • Michelle Rhee, ex-schools chancellor, Washington D.C.
  • <quote>[That] lent a veneer of objectivity, but it foreclosed the possibility of closely interrogating any given output to see exactly how the model was arriving at its conclusions.</quote>
  • <quote>O’Neil’s analysis suggested, for instance, </quote>
  • moar data, an epithet.
    c.f. moar defined at know your meme
  • “slow food,”
    is contra “fast food.”
  • Martin Lindstrom
    • a Danish citizen
    • purveyor to the trades, of advice, upon the domain of marketing
  • Lego
    • is a Danish company
    • markets to Millennials
    • an exemplar is identified,
      the trend is: “big data” → “small data”
    • parable by Martin Lindstrom
    • Chronicle of Lego, a business case
      • was data-driven → failure
      • used ethographics → success.
    • Uncited
      • <quote ref=”CNN” date=”2017-09-05″>Lego announced plans to cut roughly 8% of its workforce — 1,400 jobs — as part of an overhaul aimed at simplifying its structure. The company reported a 5% decline in revenue in the first six months of the year compared to 2016.</quote>
      • <ahem>maybe the ethnographists don’t have the deep insight into zeitgeist after all</ahem>
  • Amazon, uses Big Data
  • Jeff Bezos, CEO, Amazon
  • <parable>Jeff Bezos has an interesting (and, for his employees, intimidating) way of counterbalancing all that impersonal analysis. On a somewhat regular basis, he takes an emailed complaint from an individual customer, forwards it to his executive team, and demands that they not only fix it but thoroughly investigate how it happened and prepare a report on what went wrong.</quote> filed under: how the great ones do it.
  • <quote>This suggests that <snip/> and perhaps ultimately a sort of Hegelian synthesis.</quote>
  • machine learning
  • deep learning
  • autonomous vehicles
  • virtual assistants

Referenced

Previously

In archaeological order, in Slate

Actualities

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