Analytics Trends 2016


As we enter our third year of identifying the analytics trends that are likely to influence the trajectory of the business world in coming years, it’s clear that some trends aren’t going away. Instead, they are evolving at a rapid pace. In the world of science, such rapid evolution demands closer analysis—and the same is true with these analytics trends. They deserve a fresh look.
This year, we’re taking stock of a mix of both new and familiar topics that are shaping an “everywhere analytics” world—where analytics, science, data, and reasoning are embedded into the decision-making process, every day, everywhere in the organization.
Six significant trends are in play.
Are machines coming for us?
The newsstand rhetoric posits that smart machines will soon take over our jobs. Fear not—there’s still a place for us. Humans have always added value to machines as processes become automated, and this is likely to continue.
Still, the cognitive age is clearly upon us, as indicated by more than $1 billion in venture capital funding for cognitive technologies in 2014 and 2015. Analysts project that overall market revenue for cognitive solutions will exceed $60 billion by 2025.1 As cognitive technology evolves, it is likely to become just another tool in the toolbox—very useful for the right application but not replacing traditional analytics capabilities that also complement the human thought process. The man-machine dichotomy is not “either-or.” It is unequivocally “both-and.”
1 International Data Corporation.
Complementing one another
There are likely to be a variety of ways in which smart people and smart machines will work alongside each other. Some humans will have to build and implement cognitive technologies, of course. Others will ensure that those technologies fit into a work process and monitor their performance. And some humans will complement computers in roles machines can’t perform well, such as those involving high levels of creativity, caring, or empathy.
Paving the way to a collaborative future
Of course, these combinations of technology and people won’t happen seamlessly or automatically. Organizations will need to examine knowledge-intensive processes and determine which tasks can best be performed by machines and which by humans. Some degree of retraining may be necessary. And—let’s face it—there may be some job loss as well. Smart companies will think about these issues early in the game and help employees prepare for a collaborative future with smart machines.
Case Study: LifeLearn Sofie
In North America, most veterinarians are general practitioners, and while specialists may be available by referral, veterinarians are often required to have expertise across many disciplines, species, and breeds. That’s where cognitive computing comes in.
LifeLearn, a Canadian veterinary technology company, is developing a cognitive computing system called Sofie (running on IBM Watson) that would give veterinarians access to extensive, up-to-date knowledge on animal diseases, their specific treatments, and develop individualized patient care plans. Sofie will allow veterinarians to pose freeform questions about animal diseases, search for potential conditions based on clinical presentation and breed predisposition, and explore current diagnostic and therapeutic options. All of which are updated frequently with the latest scientific literature.

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