Big Data and real time analytics

In a broader sense, Big Data and everything that constitutes it, is really built on the premise of working toward being able to provide real-time analytics for extremely large pools of information.  In other words, Big Data IS just another form or attempt to bring real-time analytics into greater use in a larger, more refined capacity.

As far as the telecommunications industry (which has also experienced dynamic exponential growth over the last several years) is concerned, Big Data is basically its own form of real-time analytics.  They do have a good point though.  Given that it is telecommunications companies that are providing the catalyst for data aggregation, not to mention the means and the drive for millions, if not billions of users to add their content (and voice) to the ever-growing stream of information, it’s clear that they have a major stake in Big Data.   

Companies like Sprint for example, have already made strides into what might be called “information brokering”.  Basically, this is centered on either marketing one’s own analytical conclusions and analysis and/or selling the accumulated data outright.  In either case, the value is clearly attached to the ability of a business to collect, organize and perhaps even analyze large amounts of data.  Telecommunications companies are positioned well in this regard because they not only facilitate the flow of information across many sources; they also tend to become heavily invested in all aspects of collecting data (eventually) on the IT side of the equation.

In a way, the activities and data that human beings are constantly feeding into social media platforms and institutions like Google constitute what one might consider an algorithm unto itself.  In other words, the value comes from the natural tendencies of human beings interacting with each other individually and in groups; this “human algorithm” that seems to be dictating an exponential increase in data proliferation is what corporations are really trying to tap into.  Perhaps this is why Big Data is referred to as an upcoming major resource market?

Anyway, getting back on point; once it becomes possible for big data brokers to quickly and effectively differentiate large amounts of information from structured and unstructured data sets, we’ll see a dramatic shift take place.  Sure, there are certain technologies that exist right now which might allow one to achieve such a thing, but they’re severely limited, buggy, inaccurate, or simply not fast enough to provide real-time analytics capabilities as of yet.   Once the time between accumulation and processing is significantly reduced and newer processes which allow parsing to become much more intuitive and accurate emerge, it’s highly unlikely that any type of major shift in the aims or abilities of Big Data will change.

However, rest assured, this is on the way.  Every single day scientists and researchers are developing new methods for breaking down big data, whether its through largely conventional means, with the assistance of A.I., or perhaps even through the increased use of cloud computing.

Speaking of cloud computing and its relationship with/to big data, it’s very likely that clouds will be used to provide major assistance with regards to processing power that’s needed for breaking down large pools of data.  In all reality, cloud computing represents what might very well be one of the only ways that we might possibly be able to achieve real-time analytics services in the big data market.  Sure, more powerful stand-alone supercomputers could be developed to handle this task, but that would be infinitely more expensive that cloud computing and still might not be ultimately as powerful.

Where is Big Data headed, you ask?  Well, in short, all signs seem to point toward Big Data becoming something of a conventional “resource” which additional wealth can be extracted from.  Since it is data itself that is largely determining how effective and popular something can be (just look at Google and the relationships between its following, database and algorithms, for example), it only makes sense to monetize it.  Regardless, we’ve only scratched the surface with regards to where big data might be headed and what it might teach us about ourselves.  Right now, we just need to focus on finding new ways to make use of this emerging resource.

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