In figure 1, I've taken part of the lifecycle curve and modeled onto it a differential benefit curve (differential value - cost of implementation). This latter curve shows how the benefit of an activity changes as it evolves from its early innovation (where it is a strain on company resources) to a late product stage where the activity is ubiquitous and of insignificant differential value between competitors.
By the time an activity is implemented, the actual differential benefit may be vastly different. This creates a delta for expectation i.e. a difference from what we thought we would get and what we got. Figure 2 provides a graphical notation of this.
The evolution of an activity from products to utility services invokes its own expectation curve not through differential value (the creation of a new activity) but operational efficiency (a more efficient means of providing an existing activity).
In figure 5, I've provided the later stages of lifecycle including the transition from products to utility services and modeled an operational benefit curve (operational efficiencies over competitors - cost) of a transition to utility services.
The reason why I mention this, is that whilst Cloud Computing is all about volume operations for ubiquitous and well defined activities (i.e. use of computer resources in business) and is hence all about commodities, this transition will create a similar expectation curve around operational efficiency in much the same way that a genuine innovation creates an expectation curve around differential value. This is shown in figure 6, and the result is the same delta in expectation curve shown beforehand.
Hence, in the following hype cycle I've highlighted several activities, including :-
- cloud computing: more efficient provision of the existing activity of "using computer resources in business"
- social network analysis: a relatively new activity and a potential differential
The lesson of this story has been known in military circles for a long time. An imperfect plan executed today is better than a perfect plan executed tomorrow i.e. if you wait until the activity can be easily and effectively implemented (the plateau of productivity), it'll provide little competitive benefit to you.
Fortune favours the brave.
[A final few comments]
To generate the expectation curve I had to create a model over time. This required lots of assumptions because the evolution (lifecycle) curve does not have a time axis (i.e. you can't predict when something will evolve). There are hence a couple of points I'd like to make clear.
- You can't simply overlay the expectation curves of different activities on top of each other - i.e. the axis of time is different (some are stretched, some are shortened). Gartner's curve doesn't define its time axis and we can therefore assume they're referring to a general shape which appears over an undetermined length of time.
- The Gartner curve specifically refers to the technology trigger. We can assume this is when the technology starts to spread and ignores any early stage effects (invention etc).
- If the Gartner curve was based upon the measurement of some physical property, it would be possible to reverse the process i.e. from Gartner curve to expectation curve to evolution lifecyle and accurately state where an activity was along the uncertainty axis. By very definition this is impossible. I can currently only state where something was in the past once it has become a commodity. Hence I have to conclude that Gartner's curve is not based upon some external measurement of physical property but instead it is more likely by a process of expert review (i.e. averaging where forecasters think something is on the curve).
- The expectation curve matches the Gartner curve in certain circumstances. I cannot conclude the Gartner curve is valid in all circumstance but I can approximate the value zones where the curve does match.