I have been in the hi-tech arena for around 20 years and most companies that I have worked for have spent large chunks of time trying to answer this question. When I was at Nokia around Y2K, “the” app was a digital camera on the phone, quickly followed by email. We also strategized about convergence of fixed and mobile, about the mobile phone as a credit card and command prompts using voice recognition.
At a company dinner last week we were trying to predict the “future” for computing. For instance, how long will the good old “qwerty” keyboard be around in its current form factor? Kids today oscillate their digits around a tiny input screen at a furious pace. They have learned this dexterity largely by playing “rapid-touch” games on their iPods and smart-phones. Heck, kids would rather text each other across a room (20 times in 5 minutes) than dare stand up and engage in face-to-face conversations. The elegant touch typist with a straight back is a bygone era.
I am sure many people much smarter than me are already figuring out the impact that Generation Z will have on the corporate work-force. But the impact will be massive since this generation will have survived adolescence with Facebook, iPads and the rest. They will also be far more demanding of technology than my generation was and certainly not over-awed by it.
Our Catelas dinner discussion might not have uncovered the next “killer app”, but we did speak about “personalized data”. In our world of data discovery, the current buzz is Big Data, which means crunching even more disparate datasets with faster processors. But this cannot be the answer. Surely, our data searches need to become more intelligently attuned with the individual – we need to understand the individual before we “search the universe”; otherwise the search becomes impossibly big or we cut the parameters of the search so much that we exclude a lot of important stuff.
So the mathematical algorithms start getting infused with behavorial one’s – how the human thinks and behaves has a pivotal bearing on the type of data searched or retrieved. In eDiscovery the Catelas approach first identifies the relevant Custodians by uncovering “‘how they behave”, which then leads us to “what they say”. This has to be a smarter approach than simply guessing “what they said” which is the whole basis for keyword searching. It may sound complicated, but its analogous to the way law enforcement solves crimes…..
If there was a shooting in New York, the police officers would not round up thousands of people in a 5 mile radius of the scene, load them into a stadium and interview them all with the same 10 (or 1,000) questions. That is the big data approach. No, they would ask relationship-type questions of the key people at the scene – who was with the victim, did anyone see the assailant, etc. This link analysis approach identifies ‘who is involved’ BEFORE it starts looking for the ‘what’ (the smoking gun), thereby reducing the scope of data to only what is relevant, at the outset.
What is your industry’s next “killer app”?