Just a few years ago, conversations about the cloud focused on what to keep on premise, what to put in it and who had the best stack. Fast-forward and the discussions today are all about how to make sense of the massive amounts of data that are now in the cloud.
At first, computational clouds were considered the ideal solution. However, while computational systems are great at storing information and performing numeric calculations, they are limited.
Essentially, they are hindered by an inability to address the 80 percent of unstructured or “dark” data that’s accumulating at a staggering pace. They also lack intelligence in the sense that they can’t put context around the user’s intent.
For example, let’s say you’re planning a trip to the Galapagos Islands. A computational system would pull up relevant data such as flight information, hotel deals, tour prices and the like. That is helpful yet it’s still time-consuming on the part of the traveler who has to piece together all those disparate sources of information, along with their own preferences, to create an itinerary.
On the other hand, a cognitive solution is able to tap into travel information, draw on data that’s not neatly packaged and add intelligence to it. It goes beyond the basics and may include TripAdvisor reviews, tweets, deals based on the traveler’s frequent flyer miles, and even his or her preference for an aisle seat. The results are time and cost savings, personalized experiences and increased customer loyalty.
It’s clear that as cognitive clouds gain momentum, the limitations of computational clouds will become even more pronounced. Here are 10 attributes of cognitive cloud platforms (download chart, also on P.22 KMWorld, October 2014, Vol. 23, Issue 9) that differentiate it from a computational approach:
- Understand and adapt to the user, not the other way around.
- Natively accept and understand human forms of communication including natural language, text, audio, image and video.
- Have situational and temporal awareness based on ambient signals from users and data.
- Proactively provide trusted advice and offers rather than wait for commands, while respecting customer privacy and permissions.
- Understand user intent, content, context and meaning to drive dialogs and outcomes.
- Support goal-driven reasoning and concierge-like recommendations over natural language, audio, images and video.
- Augment human data processing and decision-making faster and at a scale far bigger than the human brain.
- Integrate content, context and learning across all digital, social and mobile platforms and form factors.
- Support a hybrid cloud architecture.
- Get smarter over time as they continuously interpret and learn a domain and autonomously reprogram themselves.
Now that technology has evolved to the point where apps and services can function more intelligently and intuitively, the market is ripe for cognitive platforms. In fact, Deloitte estimates cognitive computing to be a $50 billion market. This makes sense when you consider that there is an increasing need to be able to perform calculations on huge and unstructured datasets and quickly adapt to new information while also demonstrating ROI on big data initiatives.