Solving the Big Data Problem?

Solving the Big Data Problem?

With a shortage of data scientists, what are the alternatives for making sense of Big Data? We examine Cognitive Computing, its strengths, and how it can fit into the current Big Data landscape.

There’s a problem within big data. The problem is that there’s too much information and not enough talent to manage it. The supply of analysts and data scientists can’t keep up with the ever growing demand for this type of talent. This shortage presents a problem, because even the most advanced data platforms are useless without experienced professionals to operate and manage them.

How do we solve this? More training and better academic programs? Possibly, but what if there was another solution. What if instead we trained computers to do the work for us, or at least make it easier to manage data tools? Improvements in cognitive computing are making that an approaching reality.

In the simplest sense possible, cognitive computing is the creation of self-learning systems that use data mining, pattern recognition and natural language processing (NLP) to mirror the way the human brain works. The purpose of cognitive computing is to create computing systems that can solve complicated problems without constant human oversight.

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With that definition in mind, what would those capabilities provide for big data analytics? Well, as cliché as it sounds, the possibilities are really endless, but let’s discuss how cognitive computing could help less-experienced staff handle the complexities of data.

Cognitive computing will bring a high level of fluidity to analytics. Thanks to improvements in NLP, it’s becoming easier and easier to communicate with our machines. Staff who aren’t as familiar with data language or data processing, which are normally essential for proper analytical functions, could still interact with programs and platforms the way humans interact with each other.

Meaning, by providing simple commands and using normal language, platforms built with AI technology could translate regular speech and requests into data queries, and then provide responses in the same manner they were received. With this kind of functionality, it would be much easier for anyone to work in the data field.

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While that sounds all well and good, many might question when we’ll actually reach that point. After all, we’re in a pinch with talent shortages now, so a solution designed for years in the future won’t do us much good. Well, AI and machine learning aren’t some abstract ideas reserved for future generations.


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