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.
You’re reading this cultural analysis and prognostication in Slate. You going to be okay with that? They publish articles with titles such as
Why the Witch is the Pop-Culture Heronie We Need Right Now,
Watch the Uncanny Eyeball Installation That Seems to Watch You Back,
Implanted Medical Devices are Saving Lives. they’re Also Causing Exploding Corpses.
OK? … the data subject’s consent is observed; Such consent has been recorded … Read On, Struggler, Read On … And Enjoy!
<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>
purveyor to the trades, of advice, upon the domain of marketing
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.
<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>
The Age of Big Data; Staff; Sunday Review, of the The New York Times (NYT) ; 2012-02-12 (five years ago).
Michael Lewis, Moneyball, 2003, ASIN:0393057658
tl;dr → boosterism upon the use of analytics within the business operations of a baseball team.
Shopping Habits; Some Cub Reporter (SCR); In The New York Times (NYT); 2012-02-10.
tl;dr → <perhaps>that story of Charles Duhigg’s about the [Christian?] girl who is pregnant and Target’s algo finds her in her home and serves her advertisements for the happy arrival, but she isn’t married and her father is unamused.</perhaps>
Building a 300 node Raspberry Pi supercomputer; Robin Harris; In ZDNet; 2017-09-29.
Teaser: Commodity hardware makes possible massive 100,000 node clusters, because, after all, commodity hardware is “cheap” — if you’re Google. What if you want a lot of cycles but don’t have a few million dollars to spend? Think Raspberry Pi
Analyzing large graphs provides valuable insights for social networking and web companies in content ranking and recommendations. While numerous graph processing systems have been developed and evaluated on available benchmark graphs of up to 6.6B edges, they often face significant difficulties in scaling to much larger graphs. Industry graphs can be two orders of magnitude larger – hundreds of billions or up to one trillion edges. In addition to scalability challenges, real world applications often require much more complex graph processing workflows than previously evaluated. In this paper, we describe the usability, performance, and scalability improvements we made to Apache Giraph, an open-source graph processing system, in order to use it on Facebook-scale graphs of up to one trillion edges. We also describe several key extensions to the original Pregel model that make it possible to develop a broader range of production graph applications and workflows as well as improve code reuse. Finally, we report on real-world operations as well as performance characteristics of several large-scale production applications.
Sirish Chandrasekaran, Owen Cooper, Amol Deshpande, Michael J. Franklin, Joseph M. Hellerstein, Wei Hong, Sailesh Krishnamurthy, Sam Madden, Vijayshankar Raman, Fred Reiss, Mehul Shah; TelegraphCQ: Continuous Dataflow Processing for an Uncertain World; In Proceedings of the Conference on Database Research (CIDR); 2003; 12 pages.
Brian F. Cooper, Raghu Ramakrishnan, Utkarsh Srivastava, Adam Silberstein, Philip Bohannon, Hans-Arno Jacobsen, Nick Puz, Daniel Weaver Ramana Yerneni (Yahoo!); PNUTS: Yahoo’s Hosted Data Serving Platform; In Proceedings of the Conference on Very Large Data Bases (VLDB); 2008-08-24; 12 pages.