Finding pi: Enterprises must dump their legacy ideas and search for radical innovation by Nirav Shah.
From the post:
Radical innovation has historically overcome barriers to scientific progress. For example, the discovery of pi as a numerical concept found application in mathematics, physics, signal and image processing, genomics and across domains. Similarly, the internet unleashed innovation across industries. Today, the computing world stands at a point where “pi”-like innovations can unlock quantum value.
The disproportionate dichotomy
Enterprises spend $2.7 trillion on technology related products. More than 95 percent of that spend is driven by desktop or laptop related applications, services, networking and data center infrastructure for employees, partners and customers.
Amongst enterprises, there is an installed base of 700 million personal computers, while smartphones and tablets form an installed base of 400 million mobile computing units. While mobile computing units constitute 36 percent of devices, less than 5 percent of enterprise dollars are focused on the mobile device base highlighting a disproportionate dichotomy.
Nirav’s article is an interesting read but I’m not sure we should be seeking a “pi” moment.
There is evidence of π being known since approximately 1900 to 1600 BCE. Which means it has taken 3,600+ years for π to embed itself in society. I suspect investors would like a somewhat faster return on their investment.
But we don’t need a π moment to make that happen. Consider this observation from Nirav’s post:
A survey of CIOs indicate that more than two thirds of North American and European insurers will increase investment in mobile applications, however Gartner predicts that lack of alignment with customer interests and poor technical execution will lead to low adoption rates. In fact, Gartner expects that by 2016 more than 50 percent of the mobile insurance customer apps will be discontinued.
Does the Gartner stat surprise you?
How often have you sat at a basketball game wishing you could check on your automobile insurance policy?
Software apps that are born of or sustained by management echo chambers are going to fail.
There is nothing surprising or alarming about their fate.
What is alarming, at least to a degree, is that successful apps are identified after the fact of their success. Having a better model for what successful apps share in common, might increase the odds of having future successful apps.
Pointers anyone?
PS: Of course I am thinking of this in terms of topic map apps.
Assuming that a topic map can semantically integrate across languages to return 300 papers for a search instead of 200, where is the bang for me in that result? The original result was too high to be useful to me. How does having more results help?