by Ted Cuzzillo
Visualization guru and data-industry skeptic Stephen Few in has a worthwhile review of Weapons of Math Destruction by Cathy O’Neil.
Data can be used in harmful ways. This fact has become magnified to an extreme in the so-called realm of Big Data, fueled by an indiscriminate trust in information technologies, a reliance on fallacious correlations, and an effort to gain efficiencies no matter the cost in human suffering.
by Ted Cuzzillo
Someone introduced himself recently as a “data scientist” to a data warehouse pro I know. “I thought he was a fool,” said Interworks consultant Tim Costello. He says it’s a meaningless term — and I believe it’s another one of those distractions thrown around in a roiling industry.
One of the most interesting of the others I’ve heard is Scott Davis, the founder of Lyzasoft. He emailed this week from somewhere on the Caribbean, “Data scientist is just a term someone applied to a set of skills and practices that have existed for a very, very long time. Like, since the invention of the abacus.”
What does he say to those who think it’s an attempt to distinguish between data analysts of any level and those at the top of the discipline? “The attempt is not by someone who does this stuff,” he writes. “It is by someone who is selling something…either a book or a training regimen or a system or a degree or a tool or….”
I looked for Stephen Few’s opinion, and of course he had one. In his 2,500-word blog post “Are you a data scientist?,” he cites interesting articles and poses good arguments, and essentially agrees with Scott and Tim. “There is indeed a science to data sensemaking,” he writes at the end, “but data science by any other name (and there are many) would smell as sweet.”
But why do we care? We do waste a lot of breath on terminology that means little one way or another. But sometimes it does matter — such as in Jill Dyché’s now-famous blog post “Why I wouldn’t have sex with a data scientist.”
Was he too busy, as she originally thought? Or was the poor guy just too full of himself? Whichever, it demonstrates natural selection the way it’s supposed to work.
by Ted Cuzzillo
A remarkable thing happened in Big Data last week. One of Big Data’s best friends poked fun at one of its icons: the Three V’s.
The well-networked and alert observer Shawn Rogers, vice president of research at Enterprise Management Associates, tweeted his eight V’s: “…Vast, Volumes of Vigorously, Verified, Vexingly Variable Verbose yet Valuable Visualized high Velocity Data.”
He was quick to explain to me that this is no comment on Gartner analyst Doug Laney’s three-V definition. Shawn’s just tired of people getting stuck on V’s.
How strange to be stuck on a definition, but we get stuck all the time trying to define Big Data. Other terms are easier. We’ve always known what visualization is. We seem to agree on “self service BI.” We also know what relational databases are, what ETL is, and all kinds of other established technology. We don’t agree on “business intelligence” or “decision support,” but somehow we don’t dwell on it. We don’t even quibble too heartily with “easy to use,” even though I could argue that we should.