The term “Big Data” often brings notions of the more, the better. But as the volume and the number of available datasets have grown over the last two decades, the consumers of Big Data have started to feel the ramifications. When big data gets too big, it can become unwieldy; it’s too much to review, often unorganized, or doesn’t have analytical power to evaluate it.
One of the original definitions of Big Data (Gartner, 2001) is “data that contains greater variety arriving in increasing volumes with ever-higher velocity.”
Imagine being in a file room with heaps of unorganized paperwork. If your job was to find one specific application form, for example, how long would it take to track it down? Keep in mind, the application could be anywhere, and couriers continue to bring in more and more paperwork at an increasing rate. It could take years to find the one application you are looking for (if you ever find it at all). In this example, more data offers diminishing returns, if not wholly negative ones.
Now imagine if you could apply powerful analytics to this room. You can suddenly organize the files by any number of characteristics such as Type, Date, Order Date, Applicant Name. The daunting task suddenly takes a matter of seconds.
To be a useful tool, Big Data needs fast processing, affordable storage, and powerful analytical tools to Extract, Transform, and Load (ETL) into a useful form. With advanced analytical engines, like the one at the core of the NCS Platform, multiple data sources can go through this process to generate outputs such as reports and alerts.
By cataloging the millions of data points as they flow in every day, the NCS Platform arranges, aggregates, and analyzes your Big Data to deliver you the most useful and critical information.
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