Can't you just feel the sand getting sucked out from under your feet as the current tries to pull you off the beach, and into the abyss with the bad ideas of projects past?
You've heard about Hype Cycle for Emerging Technologies in the past. It charts where Gartner sees various technologies along life stages from "Innovation Trigger" up to the "Peak of Inflated Expectations" down into the depths of the "Trough of Disillusionment." Then, at some point in the future those technologies that survive cruise happily along the "Slope of Enlightenment," and off to the "Plateau of Productivity."
Of course, the Trough of Disillusionment isn't a death sentence for technologies, but it also isn't good news for the pioneers who promote or work with a technology. At minimum, the trough means a technology gets a bit tarnished and falls from the spotlight. At worst, the trough actually is where once-great ideas get swallowed up.
In the latest version of the hype cycle illustration -- showing some of the 2,000 technologies Gartner evaluated -- a number analytics concepts and technologies that rely heavily on analytics are staring down into the trough from the Peak of Inflated Expections.
The color codes on the chart indicate how many years Gartner expects each technology to take before it reaches that Plateau of Productivity, if ever.
Among the technologies heading downhill fast are wearables, which data pros still hope will feed new business applications, and the AI-related concepts of machine learning, speech-to-speech translation, and natural language queries.
While they aren't truly associated with analytics, you might want to unload those bitcoins while you can, because Gartner has cryptocurrencies heading into the trough, along with the homemade guns and cars that just everyone has been waiting to come out of consumer 3D printers.
On the brink, just waiting to take the plunge are the Internet of Things and autonomous vehicles (both 5 to 10 years from productivity). Expectations for advanced analytics with self-service delivery are at the peak now, but are estimated to be only 2 to 5 years from productivity. I guess that's the only good news for data pros.
Like any research firm, Gartner is going to be right a certain percentage of the time, and probably sort of right with the same frequency. Dead wrong? It happens. In most cases we simply forget who predicted what once four or five years pass. In that way, the researchers can work the data to show how often they were right and ignore their flaws. This hype cycle chart is an annual thing that gets people talking, complaining, and (like me) writing. It's something we can have some fun with, but not something you can bank on.
Which analytics-driven technologies this year are on the verge of settling into long-sought productivity? Nothing. So, if you take the hype cycle too seriously, the near future for analytics might look pretty dire.