Data Science explained by the media, or: why I might just screw your wife while you’re at work
I wanted to mention two recent articles about data science. The first was in the New York Times, has a crappy title (“Big Data is Great, but Don’t Forget Intuition“), a positive outlook, and interviews skeptics like my co-author Rachel Schutt, who has the last word in the article:
“I don’t worship the machine”
The second article (hat tip Chris Wiggins) was published in Forbes, has a great title (“Data Science: Buyer Beware“), an enormously skeptical outlook, and takes quotes from data science celebrities. From the article:
Thomas Davenport and D.J. Patil’s rather hyperbolic declaration that the “data scientist is the sexiest job of the 21st century” deserves a double dose of skepticism.
These two articles are attempting to do totally different things and they both achieve them pretty well. The first brings up the need for thoughtfulness so that we don’t blindly follow algorithms:
Will Big Data usher in a digital surveillance state, mainly serving corporate interests?
Personally, my bigger concern is that the algorithms that are shaping my digital world are too simple-minded, rather than too smart.
The second article brings up the ideas that we’ve been through similar thought and management revolutions before, and trouble lies with anything that is considered the silver bullet. Here’s my favorite part:
…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.
For the record, I’d rather define data science by what data scientists get paid to do, which is how we approached the book. Even better if we talk about data scientists as people who work on data science teams, where the “extremely rare, almost implausible combinations of skills” are represented not by one person but by the team as a whole (agreed wholeheartedly that nobody is everything a typical LinkedIn data scientist job description wants).
The only weird part of the second article is the part where writer Ray Rivera draws an analogy between data scientists and “icemen”, the guys who used to bring ice to your house daily before the invention of refrigerators. The idea here is, I guess, that you shouldn’t trust a data scientist to admit when he is not necessary because there’s better technology available, not can you trust a data scientist to invent such technology, nor can you trust a data scientist with your wife.
For whatever reason I get a thrill from the fact that I pose such a sexy threat to Rivera. I’ll end with the poem he quotes:
I don’t want no iceman
I’m gonna get me a Frigidaire …
I don’t want nobody
Who’s always hangin’ around.