RTFB

Aunt Judy of the Attic has found a blog she likes, Mathbabe, aka Cathy O'Neill, and has quoted therefrom extensively, or at least one article someone sent to Atlanta

Distrust the experts.
Why? Because you don’t know their incentives, and they can make the models (including Bayesian models) say whatever is politically useful to them. This is a manipulation of the public’s trust of mathematics, but it is the norm rather than the exception. And modelers rarely if ever consider the feedback loop and the ramifications of their predatory models on our culture.

Well you can go over there and have a peek,  but Eli thought he would RTFB a bit, and it was interesting.  O'Neill is a data scientist, someone whose skill set involves structure and extracting useful information from the mass of glop called today's INTERNET or the data base of the week.

The blog was interesting, a bit off the area where Eli has much to say, but can learn a little by reading,
However, however,

There was another post with the Rabett Run class title,Data Science explained by the media, or: why I might just screw your wife while you are at work points to an article in Forbes (why this is so delicious the bunnies will see in a nonce) by Ray Rivera, Data Science:  Buyer beware, which is a slightly more skeptical brew and one O'Neill is unhappy, but a bit uncomfortable with

Rivera starts with an easy shot at everything else

Any field of study followed by the word “science”, so goes the old wheeze, is not really a science, including computer science, climate science, police science, and investment science.

so he obviously has little use for natural, physical or social science. but what the hell, data science for his is perilously close to management fad of the week and an outgrowth of the devil itself Business Process Reengineering (BPR), go read if you want details. but

Data science is the spry third generation of BPR, responding to vastly increasing IT capacity, unprecedented ability of businesses to create data, widespread realization that data is a valuable resource, and the burdensome need to extract data from storage in order to realize business value. Yet, data science belongs to a family tree of business practices that for over a century have been governed by technocrats who view organizations as machines, desiring to automate everything and eliminate people wherever possible. Data science is shaping up to be a redux of its grandfather BPR, with the same structural features (BPR was never really engineering, nor as we shall see is data science really science), and its propensity for sin and indulgence.

Patience, this is Rabett Run and we get there, bunnies enjoy the trip, but Eli does get there and hopefully everybunny learns a bit along the way.

Oh yes, here it is, Rivera points out that

As BPR morphed into knowledge management, the virtue of simplicity was reversed, and complexity came to indicate merit. Data science promises to deliver value by unpacking some of that complexity. Yet like the two generations of fads that preceded it, data science tries to create value through an economy of counterfeits:

• False expertise, arising as persons recognized as experts are conversant in methods and tools, and not the underlying business phenomena, thereby relegating subject matter knowledge below methodological knowledge,
• False elites, arising as persons are summarily promoted to high status (viz., “scientist”) without duly earning it or having prerequisite experiences or knowledge: functionaries become elevated to experts, and experts are regarded as gurus,
• False roles, arising as gatekeepers and bureaucrats emerge in order to manage numerous newly created administrative processes associated with data science activities, yet whose contributions to core value, efficiency, or effectiveness are questionable,
• False scarcity, arising as leaders and influencers define the data scientist role so narrowly as to consist of extremely rare, almost implausible combinations of skills, thereby assuring permanent scarcity and consequent overpricing of skills.

Great, let's look at the those

• False expertise, arising as persons recognized as experts are conversant in methods and tools, and not the underlying business phenomena, thereby relegating subject matter knowledge below methodological knowledge,

Seen that have we.  Demands for the laying on of statistical high priests in climate studies who don't have a clue about why carbon dioxide doesn't sink in the atmosphere, freaky econometricians, mining investors, and more.  Eli could go on and on and on, but you, dear readers can fill in the blanks

• False elites, arising as persons are summarily promoted to high status (viz., “scientist”) without duly earning it or having prerequisite experiences or knowledge: functionaries become elevated to experts, and experts are regarded as gurus,

 IPCC Expert Reviewers:) (not all of them, of course, but certainly those who proudly proclaim the title)

• False roles, arising as gatekeepers and bureaucrats emerge in order to manage numerous newly created administrative processes associated with data science activities, yet whose contributions to core value, efficiency, or effectiveness are questionable,

Auditors, FOIA demanders, rent seekers the ones, who in the words of the idiot tracker

uses the coercive power of the state to force other people to give him, gratis, the fruits of their labor. He does not produce himself — he uses the data of others, repackaged and sensationalized,

and finally

• False scarcity, arising as leaders and influencers define the data scientist role so narrowly as to consist of extremely rare, almost implausible combinations of skills, thereby assuring permanent scarcity and consequent overpricing of skills.

Anybunny seen Honest Broker hereabouts?