When Big Data first broke on the scene, it was all the talk. The data deluge dominated conversations from the break room to the Boardroom. We all shared jaw-dropping stats including the fact that humans create more data in two days than was created in all of history up until the year 2003.
At that time, the evolution of Big Data, the explosion of social media and the meteoric rise of mobile created a perfect storm as tremendous amounts of information were now readily available.
Big Data was going to change everything. The way companies define their business strategy. How they engage customers. Where financial institutions invest. How doctors proactively identify and treat patients at high risk for illness. Big Data has even been used to recruit major league baseball players. You name it and Big Data is going to fix it.
Except the vision for Big Data often falls short of reality.
There are lots of reasons. But the primary one is that while there’s no shortage of data, it’s nearly impossible to tame.
It’s not surprising that 55 percent of Big Data initiatives fail. It’s not from a lack of talent. It’s not from a lack of trying. It’s not solely from a lack of resources. And it’s certainly not from a lack of technology. It’s the sheer volume of data that’s tripping up businesses.
Even when they do get their arms around it, there’s still upwards of 80 percent of critical data that goes unused, uncollected, or just can’t be processed by today’s so-called “solutions.”
Many of you know this data as unstructured or “dark data.”
For example, insights from Twitter conversations, random online photos and images, geolocation data, and even customer call records are the types of dark data gems that complete the Big Data picture so many of us are trying to see.
When dark data is brought into the light, and combined with existing stores of structured data such as customer databases, financial records, and other sources of information, that’s when Big Data pays off. Except it’s not called Big Data. To make sense of all of those sources of information, you must go beyond Big Data and enter the next era in computing. Enter Cognitive Computing.
Cognitive computing addresses the major technical issues that cause Big Data projects to not live up to their expectations.
Not to give short shrift to the countless hours and enormous brains that go into Big Data initiatives, but it’s time we all admit that Big Data in and of itself is set up to fail. It fundamentally doesn’t allow you to easily extract insights and context signals from large and semi-structured datasets for reasoning and learning. Without that ability, companies aren’t able to arrive at smarter, more strategic business decisions. Isn’t that the whole point of having data?
Now if you’ve invested in Big Data, this doesn’t mean your efforts have been a waste. It means you’re ready to take them to the next level.
Cognitive Computing takes Big Data to the next level and accelerates it in three ways.
First, it uses context driven dynamic algorithms to automate pattern and knowledge discovery.
Second, it generates alternative answers that are backed by evidence parsed and inferred from disparate first and third party data.
Third, it reasons and learns instantly and incrementally improves over time.
One of the more widely known examples of Cognitive Computing is when IBM Watson was a contestant on the television show Jeopardy! There it was, standing at the podium and challenged to think on its proverbial feet. Its ability to understand the context of questions and quickly arrive at correct answers demonstrated the power of cognitive computing. But you should know that I didn’t just watch IBM Watson win. I was there. I mean really there working at IBM on a variety of business and IT initiatives that have advanced industry and society. This experience, coupled with a 15+ year career in IT and a formal education in engineering and business, led to Cognitive Scale.
What we’re doing here at Cognitive Scale is a big undertaking and a huge opportunity, especially since the cognitive computing market is expected to reach $50B by 2017.
We’re up for the challenge. We’ve got a growing team in Austin and Hyderabad and we’re actively looking for top talent. We’ve also lined up partnerships with IBM Watson, IBM SoftLayer, Amazon Cloud, Deloitte, and Avention. And it’s a good thing we’ve got the foundation in place because we’ve also just signed several top name customers – but there’s always room for more.
We’ll continue to keep you updated and look forward to sharing this journey with you. Watch the video.