It had to be spawned in marketing: The term big-data, like the term cloud computing, is simple and punchy. It evokes a mental picture of a situation that enterprises find tough to talk about, let alone control.
The difficulty is that big-data encompasses a range of unstructured information in which enterprises are literally drowning. Getting a handle on it calls for sophisticated technology that isn't so easily explained.
The crux is this: IBM is releasing "new predictive analytics software that automatically correlates and analyzes big data to help clients embed hyper-intelligence into every business decision." The term IBM uses to describe this new software is "analytical decision management."
The new software is part of IBM's Smarter Analytics initiative, which, simply put, is focused on helping enterprises make sense of big-data in real-time.
There's new (and complicated) technology involved. Here's how Jeff Jonas, IBM Fellow and chief scientist of entity analytics, describes the new analytical decision management applications released this week: "The technique that we're using -- very new -- is called incremental context accumulation. [It] works the same way that puzzle pieces find their home in the puzzle, and it does this in real-time." The more data you use, Jonas says, the more accurate the predictions you can make.
No, I don't understand it. Do you? That said, I do understand the problems that IBM is addressing -- namely, the need to parse social networking data to find buying patterns and brand recognition among customers, as well as more negative trends such as fraud, the need to see what the heck is going on in real-time and take action about it.
And maybe that's the point. IBM isn't describing one product or technology in all this, but a series that matches up with other products, kind of like the puzzle pieces referenced by Jonas. Does it matter what language you apply to that? Big-data solutions is as good a term as any. As they say, actions speak louder than words.
As I restated to Mary, I full agree that we are still the architects of computers and the human mind is superior. My original thinking was that we have not "recomputed" our own thinking to account for the affects of the greater computing power and the impact of more and smarter data. So the human mind has not kept pace with the power of computing that we designed.
Restated, those comments should be directed to the impact of the data itself, the product of computing, rather than the computing itself.
DHagar, I would politely disagree with you. Even with the significant advancement in machine learning and AI I believe that it has not surpassed the limit of the human brain. Firstly, because these programs are developed by humans and perform in a manner which is written in form of a code.
Yes, to the common man the computation power of the computer might not be comparable to humans as it takes us a bit longer to work on structured problems than computers. However, unstructured problems are totally a different ball game and humans definitely have an edge over machines there.
Mary, I should clarify my remark. We understand how the technology works, what I meant to imply is that we have not yet caught up with the impact and full understanding of its impact. It has created such a ripple affect, along with the ability to have more comprehensive information available upon demand, that I do not think we have begun to digest what that impact is.
So the human brain has not caught up with the results of the use of the technology, we are still limiting it too our past concepts.
While I don't agree that tech has moved beyond human understanding, I do think analytics have exceeded the powers of many who don't skills in this area. That's where the specialists and vendors come in.
I believe that the technology now, and with the compounded machine learning, has moved beyond the limits of our human brain to understand how it works. The specialists, as you point out, are increasingly "applying" that computing power in new and different ways in response to our computing, or now adding the business demands.
It truly opens the door to entire new dimensions of thinking, analyzing, and problem solving that we haven't begun to tap into. So your point, BIG DATA, is a good human-oriented term to define this phenomenon.
Yes, it makes sense that data scientists would be best for working on analytics for big data. I also wonder whether many in IT aren't simply going to rely on their internal resources to ask the right questions, while leaving the byzantine mechanics of the actual software to IBM and other big vendors.
It's enormously complex--and I agree with IBM in that the more modeling ("learning about") this data that the underlying analytics algorithms do, the more that certain patterns begin to repeat themselves so that the puzzle pieces begin to fall in place.
Big data processing is a difficult concept for IT (or almost anyone) to grasp because the process for sifting through this data is nonlinear and takes many paths at once. The hope is that an "end result" picture (confirmed by repeating patterns of data) begins to emerge that people in the business can act upon. By the way, most enterprises are finding that they need to hire quantitative analysts and researchers to work with this stuff.
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