For anyone who deals with the tech industry on a regular basis, overused buzzwords can quickly become the bane of daily existence. Certain terms catch on as the best way to describe a particular situation; soon enough, everyone in an industry is using them in emails, lectures, PowerPoint presentations, and drunken bar discussions; and that verbal saturation quickly reaches the point where the original term becomes a joke.
In light of that, here are three tech-centric buzzwords in danger of passing out of the “usefulness” phase, and fully entering parody territory:
Disrupt: In mid-August, the New Republic suggested that “disrupt” had become the cockroach of tech jargon: annoying, everywhere, and virtually impossible to kill. “It’s no longer the adjective you hope not to hear in parent-teacher conferences,” the magazine wrote. “It’s what you want investors to say about your new social-media app. If it’s disruptive, it’s also innovative and transformational.”
The way it’s used in tech scenarios, “disruptive” is a positive thing; it means shaking up stolid industries (like the PC-manufacturing business, or brick-and-mortar bookstores) by figuring out how to deliver their products and services in new and efficient ways. It’s such a compelling idea, in fact, that virtually everything tech-related these days seems to have “disruptive” slapped in front of it: app development, cloud services, online learning, tablets, and even philanthropy.
But however compelling the underlying idea, disruption isn’t necessarily a good thing. “What makes disruptive innovations so deadly is they’re not better than your product. They’re worse,” Matthew Yglesias wrote in Slate earlier this summer. “Anyone who needed a mainframe at the dawn of the personal computer era would find a PC to be an incredibly lame and underpowered alternative.” Overturning industries doesn’t exactly equal progress in all circumstances—and in some cases, constant iteration can kill perfectly good products that still had some life left in them.
Even that threat, though, pales in comparison to the true horror of “disrupt” as an overused term: it’s being slapped in front of so many things, it’s in danger of meaning absolutely nothing. Food-delivery services are being touted as “disruptive,” along with the latest educational app for kids; conferences are filled with “entrepreneurs” (see below) babbling “disrupt” over and over again. With all due respect to Clayton Christensen and his book “The Innovator’s Dilemma,” which first coined the term in the context of market evolution, it’s time for someone to put “disrupt” out to permanent pasture.
Entrepreneur: Everybody is a “startup entrepreneur” these days, it seems. And the term certainly applies to those who create a business plan for an innovative idea, scrape together funding to put that plan into motion, and ride the result to spectacular success (or ignoble failure). But for every genuine entrepreneur with a track record of building and selling startups to large firms such as Yahoo or Google, it seems as if a dozen people with a little bit of coding knowledge are crafting a half-baked plan, setting up a Twitter account with the trendy name of their new “company” (iCandyDelivery!), and writing a bunch of Facebook postings about the amazingness of the whole experience.
The overuse of the term “entrepreneur” does a disservice to the actual entrepreneurs slaving away at an idea; but until the current tech bubble pops, and takes with it the prospect of getting rich off selling a simple app for millions, it seems we’re all going to have to live with it. Given the legitimate uses of the word, we have little choice.
Big Data: “Big Data” is in its heyday as a buzzword. The term connotes datasets so massive they need specialized tools and frameworks (such as Apache Hadoop) to be successfully analyzed. The largest data-stores require massive investments in hardware and software—it’s a prime reason why the U.S. government keeps building and updating its supercomputers, for example. Fulfilling that demand, in turn, has become big business for IBM, SAP, Oracle, and the other giant tech vendors (as well as a host of startups that produce business-intelligence apps).
But as data-crunching hardware and software become more powerful, the term “Big Data” will increasingly lose its power. When an analytics platform can crunch pretty much any unstructured or structured dataset (or even a slurry of the two) in a matter of minutes, powered by increasingly cheap processors, everything implied by “Big Data” begins to fade away—the expense, the sense of datasets so massive they’re a challenge to solve, the effort. That’s not to say that humanity won’t always face data problems on an epic scale; but increasingly, they’re “just data” problems. “Big” simply isn’t as relevant an adjective.