bill vorhiesSummary: Gartner drops “Big Data” from the Hype Cycle for Advanced Analytics and Data Science? What’s going on?

It is with heavy heart that I must relay to you that Gartner has dropped “Big Data” from its 2015 Hype Cycle for Advanced Analytics and Data Science. As recently as 2012 this category was called the “The Hype Cycle for Big Data”, but alas, no more. RIP “Big Data”.

“Big Data” joins other trends dropped into obscurity this year including: decision management, autonomous vehicles, prediction markets, and in-memory analytics. Why are terms dropped? Sometimes because they are too obvious. For example in-memory analytics was dropped because no one was actually pursuing out-of-memory analytics. Autonomous vehicles because “it will not impact even a tiny fraction of the intended audience in its day-to-day jobs”. Some die and are forgotten because they are deemed to have become obsolete before they could grow to maturity. And Big Data, well, per Gartner “data is the key to all of our discussion, regardless of whether we call it “big data” or “smart data.” We know we have to care, so it is moot to make an extra point of it here.”

Hype cycle advanced analytics Gartner 2015 annotated

Is this a joke? No, not at all. Actually “Big Data” remains an item in the 2014 Hype Cycle for Emerging Technologies and even the 2105 Hype Cycle for the Internet of Things but that doesn’t really clear things up. Because right alongside of “Big Data” on those charts are at least a dozen terms drawn directly from Advanced Analytics and Data Science such as predictive analytics, real-time analytics, data science, and content analytics.

OK so there’s plenty of inconsistency among these closely related hype cycles and we’re not about to abandon “Big Data”. If we did half of us would have to change our company names. But the truth is that “Big Data” was always a little more than inconvenient.

Start by trying to create a simple definition. Oh, it’s a little like really big data and a little like really fast data and a little like a whole lot of different types of data. And that’s only if you stick with the first three Vs. No wonder our audience is sometimes confused.

When I first took a stab at making a definition I concluded that Big Data was really more about a new technology in search of a problem to solve. That technology was NoSQL DBs and it could solve problems in all three of those Vs. Maybe we should have just called it NoSQL and let it go at that.

Not to worry. I’m sure that calling things “Big Data” will stick around for a long time even if Gartner wants us not to. There’s an old saying that you die twice. Once when you pass away and once when the last person who remembers you utters your name. Based on that criterion I’m guessing “Big Data” is in for a long, long life.


August 17, 2015

Bill Vorhies, President & Chief Data Scientist – Data-Magnum – © 2015, all rights reserved.


About the author: Bill Vorhies is President & Chief Data Scientist at Data-Magnum and has practiced as a data scientist and commercial predictive modeler since 2001. Bill is also Editorial Director for Data Science Central. He can be reached at: or


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