"According to Cassidy (2002) all speculative bubbles go through four stages: 1) displacement, when something changes people’s expectations about the future; 2) boom, when prices rise sharply and skepticism gives way to greed; 3) euphoria, when people realize the bubble can’t last but they want to cash in on it before it bursts; and 4) bust, when prices plummet and speculators incur great losses. "
So where does Gartner currently see Big Data fitting into the Hype Cycle?
According to the following, it is sliding down "Hype of Inflated Expectations":
And actually, there are a couple of facts that back up Gartner's claim. As I discussed in a previous blog post, Forrester cites data woes as continuing problem. For big data to be effective, the underlying data has to have "veracity" (i.e. as per the definition). The more market driven, perhaps, is the fact that InfoWorld reported, citing a survey from Foote Partners, that "[p]remium pay for 58 big data-related skills and certifications declined by an average of 4.7 percent during the last nine months of 2014". More to the point InfoWord notes "there are signs of a slowdown as businesses learn that jumping into big data is not a recipe for instant ROI".
These factors illustrate how the Gartner Hype Cycle is useful for organizations to identify the current state tech trends to identify when is the right time for that organization to invest in the technology in order to minimize risk of the "bleeding edge" of early adoption but at the same time avoiding the risk of joining the party too late - being taken over by competitors.