[Wikimedia-l] [Wiki-research-l] "Big data" benefits and limitations (relevance: WMF editor engagement, fundraising, and HR practices)

[Everton Zanella Alvarenga]

Dear Pine,

thank you for sharing these links. I cannot read everything now, but one of
these warticles was also recommended by a friend, Sure, Big Data Is Great.
But So Is Intuition.<http://www.nytimes.com/2012/12/30/technology/big-data-is-great-but-dont-forget-intuition.html?_r=1&>,
by Steve Lohr, that reminded me a case in Brazil which avoided previous
mistakes, that was a collaborative process of
hiring<http://blog.wikimedia.org/2012/01/11/brazil-recruiting-and-partnership-with-the-community-moves-forward/>a
consultant for the WMF programs in Brazil, i. e., the community was
listened. (See the full discussion that resulted in a better process here <
http://comments.gmane.org/gmane.org.wikimedia.brazil/161>, although it
still can improve.)

"It’s encouraging that thoughtful data scientists like Ms. Perlich and Ms.
Schutt recognize the limits and shortcomings of the Big Data technology
that they are building. Listening to the data is important, they say, but
so is experience and intuition. After all, what is intuition at its best
but large amounts of data of all kinds filtered through a human brain
rather than a math model?"

As Alexandre Abdo pointed
out<http://permalink.gmane.org/gmane.org.wikimedia.brazil/358>in this
not so old discussion, we, the Brazilian community, were being
handled as "consummated facts", and the community experience and intuition
was not being taken into account as it could - although I must tell a lot
of efforts were done in this direction. I hope a lesson was /learned/ and
this can help to the direction the organization is taking with its
grantmaking and learnings. :)

This also reminds me that there is no mathematical model that explains now
(maybe there never will...) the kind of system Wikimedia projects deal with
and sometimes lovely graphics and data interpretations are assumed as
scientific statements, regardless of their scientifically underpinnings.

Have a good year,

Tom


On Sun, Dec 30, 2012 at 1:26 AM, ENWP Pine <deyntestiss at hotmail.com> wrote:

>  I'm sending this to Wikimedia-l, Wikitech-l, and Research-l in case
> other people in the Wikimedia movement or staff are interested in "big
> data" as it relates to Wikimedia. I hope that those who are interested in
> discussions about WMF editor engagement efforts, WMF fundraising, or WMF HR
> practices will also find that this email interests them. Feel free to skip
> straight to the links in the latter portion of this email if you're already
> familiar with "big data" and its analysis and if you just want to see what
> other people are writing about the subject.
>
> * Introductory comments / my personal opinion
>
> "Big data" refers to large quantities of information that are so large
> that they are difficult to analyze and may not be related internally in an
> obvious way. See https://en.wikipedia.org/wiki/Big_data
>
> I think that most of us would agree that moving much of an organization's
> information into "the Cloud", and/or directing people to analyze massive
> quantities of information, will not automatically result in better, or even
> good, decisions based on that information. Also, I think that most of us
> would agree that bigger and/or more accessible quantities of data does not
> necessarily imply that the data are more accurate or more relevant for a
> particular purpose. Another concern is the possibility of unwelcome
> intrusions into sensitive information, including the possibility of data
> breaches; imagine the possible consequences if a hacker broke into
> supposedly secure databases held by Facebook or the Securities and Exchange
> Commission.
>
> We have an enormous quantity of data on Wikimedia projects, and many ways
> that we can examine those data. As this  Dilbert strip points out, context
> is important, and looking at statistics devoid of their larger contexts can
> be problematic. http://dilbert.com/strips/comic/1993-02-07/
>
> Since data analysis is also something that Wikipedia does in the areas I
> mentioned previously, I'm passing along a few links for those who may be
> interested about the benefits and limitations of big data.
>
> * Links:
>
> From the Harvard Business Review
> http://hbr.org/2012/04/good-data-wont-guarantee-good-decisions/ar/1
>
>
> From the New York Times
>
> https://www.nytimes.com/2012/12/30/technology/big-data-is-great-but-dont-forget-intuition.html
> and
>
> https://www.nytimes.com/2012/02/12/sunday-review/big-datas-impact-in-the-world.html
>
>
> From the Wall Street Journal. This may be especially interesting to those
> who are participating in the discussions on Wikimedia-l regarding how
> Wikimedia selects, pays, and manages its staff.
>
> http://online.wsj.com/article/SB10000872396390443890304578006252019616768.html
>
>
> And from English Wikipedia (:
> https://en.wikipedia.org/wiki/Big_data
> and
> https://en.wikipedia.org/wiki/Data_mining
> and
> https://en.wikipedia.org/wiki/Business_intelligence
>
>
> Cheers,
>
> Pine
>
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> Wiki-research-l at lists.wikimedia.org
> https://lists.wikimedia.org/mailman/listinfo/wiki-research-l
>
>


-- 
Everton Zanella Alvarenga (also Tom)
"A life spent making mistakes is not only more honorable, but more useful
than a life spent doing nothing."