The third platform revolution touted by several of the big analyst houses and vendors is a myth, warns Wikibon CTO David Floyer. IT is certainly evolving quickly to take advantage of multiple disruptive technologies – Big Data, mobile, cloud, social. But the key word is “evolving”. “The implication of this thinking is this third platform nirvana is a necessity for most enterprises for most applications and that organizational IT should measure progress toward this nirvana as a sole beacon of success,” he writes in “The Myth of the 3rd Platform Revolution.”

Citing Richard Dawkins’ The Blind Watchmaker he argues that evolution is constrained by where you start from and by the necessity that each change be self-sufficient, survivable and competitive to succeed. “Evolution does NOT try and make something like an eye.”

Actually most core business systems are working well on their present platforms. Moving them to a new third platform would produce very little benefit for a great deal of effort. What is not working well is business intelligence. Today’s systems are too slow and inaccessible, forcing businesses to steer the ship by looking at the wake. It is here where systems need to evolve.

Evolving BI

Instead of defining a single “third IT platform” Floyer suggests that IT needs to evolve as individuals and organizations develop ways to create sustainable value using the new technologies. This model, he says, is codified in the “second machine age” driven by IT and increasingly able to replace human decision making, promoted by MIT’s Erik Brynjolfsson and Andrew McAfee (see video below). Examples include self-driving cars and IBM’s Watson, that can diagnose and plan treatment for cancer patients better than any human doctor.

The key to these technologies is near real-time analysis of large amounts of data that can allow a self-driving car, for instance, to react at the speed of a human driver.

The greatest potential value will be achieved by applying these new technologies to automating decision support applications as an extension of existing core business systems. The object is to increase the speed of delivering answers from days or weeks to milliseconds. These systems should evolve over time, with a team identifying an opportunity where automated decision-making would provide strong business value and then building, testing and implementing that solution before moving on to apply the technology to other specific areas of opportunity. Each implementation should be able to justify itself with its individual ROI. Over time these will grow into a more unified system on the build-out model, interconnected through standard APIs where that provides value. As long as every new piece justifies itself, the organization can know it is going in the right direction.

photo credit: Kaptain Kobold via photopin cc