I wrote a critique of the #Gartner Technology Hype Curve (it's not a cycle) back in September 2005, pointing out some of the reasons why it shouldn't be taken too seriously.
Obviously I'm not arguing with the existence of the phenomenon of hype, but the evidence that this phenomenon follows a standard curve looks extremely weak. The hype curve appears to make falsifiable predictions about the expected hype-status of a given technology on a given future date. If we are being asked to take this curve as serious empirical science, we need to see some kind of scientific proof - for example, looking at the accuracy of these predictions, and using empirical data to calibrate the shape of the curve.
But although this curve has been used by Gartner for over twenty years, I have not seen any statistical analysis, whether from Gartner or anyone else, that would help us to assess how accurate these predictions have been. The shape of the curve seems to have remained remarkably stable, despite a widespread belief that innovation has been getting faster. I made all these points in my earlier post.
I understand that some people use the hype curve as a planning tool, to decide the appropriate investment and placement of technology. I'd be very interested to know how this works, and what practical conclusions can be drawn from the curve.
Another methodological problem with tracking technology hype through time is that technological jargon isn't always stable - the meaning and identity of the hyped items shifts over time. So the same buzzword on the curve in different years doesn't necessarily refer to the identical technology - we might collectively change our minds as to what exactly a given buzzword really signifies. Technology concepts may emerge slowly through a complex social process; the sociologist Bruno Latour refers to these emerging concepts as Black Boxes. See my post on the Dynamics of Hype (Feb 2013).
If we accept that there may be a separation between the perceived progress of technology (as represented through hype and jargon) and the actual progress of technology (which we may sometimes only be able to infer indirectly), then the hype curve presumably measures the first of these. And if that's true, what value can the hype curve provide to whom?
Parts of this post were contributed to a discussion on The Enterprise Architecture Network, via Linked-In.See also Jorge Aranda: Cheap Shots at the Gartner Hype Curve (October 2006)
For more posts on Hype, see Richard Veryard on Computing
Interesting post. I suspect that the same criticisms that are usually made of the "product life cycle" concept in marketing also apply to the hype cycle: it can be self-fulfilling, not all products have the same shape curve, etc.
ReplyDeleteThings don't have to be always right, though, to provide insights to planners: the hype cycle can provide a good indication of technology areas for research and consideration for future developments. You'd never implement anything solely on the basis of it though would you?