What Is Big Data?

what is big data

What Is Big Data?

What is big data? This question has been plaguing businesspeople and IT administrators as a seemingly unanswerable question, even as advances in technology have made it seemingly intuitively obvious. However, as big data continues to grow and develop, the confusion about what is big data grows as well. Today, we will look at some of the definitions that the “big data” terminology evokes and explore some of the applications that are being built on this new foundation.

Big Data is now a household term in many circles, including government agencies and industry. But what is big data? The term refers to both unstructured and structured data. Unstructured data is anything that is not considered to have a specific purpose. Structured data on the other hand, is very specific – usually measured in terms of time or space – and can be easily analyzed over a period of time or space to yield results that can be used to assist human decision making in various situations. It is often used in business to support sales, customer service and product management, among other areas.

As opposed to the unstructured data, which many view as a burden bearing nuisance, structured data is actually a useful tool in a number of circumstances. For example, big data mining involves mining unstructured data in order to provide information relevant to a business decision making process. Examples of structured data mining techniques include Text Mining and Information Mining. In addition, data mining can also involve building predictive databases, like that used by Yahoo! Answers.

Another application is velocity dialer technology. This is based on the idea that a phone call placed with a specific targeted description, such as product name, can yield a highly relevant response in a very short period of time. More specifically, this is done by “literally” dialing a targeted number or keyword combination at random. When more than one “result” page is returned in rapid succession, the resulting “snapshot” is used to make decisions about product launch, ad strategy, packaging and distribution, all done by using structured data mining techniques. This is unlike unstructured velocity dialer techniques, which rely on fast, semi-automatic dialing methods and call queue systems.

While there are clear benefits to using data sets constructed this way, the biggest question is: what is big data, really? And, how does it impact on business activities? The answer lies in three vs. two factors. These include:

In short structured processing is often associated with a structured data analysis technique – the goal being the construction of a predictive database. The underlying principle is that by using well-defined targeted keywords over a long period of time, a business can create a database that can be used for making decisions about marketing strategies, product launch, and deployment, customer contact, etc. In short, it means that using traditional manual methods such as phone and face-to-face interactions, a business can still extract valuable information that can be used in a strategic manner. The inherent logic behind such a system, however, goes far beyond the simple construction of a database.

Structured queries are constructed so that a business can find specific, actionable information according to a predefined set of rules – not a random, “wild” call volume. With the advent of unstructured velocity dialer technologies (which allow the production of certain kinds of calls without having to assemble or analyze any pre-existing data), marketers have been able to tap into the potential of what is called “big data.” Such techniques are now commonly used to generate and track customer experience metrics, product insight and market insight, behavioral data, and the like.

As discussed above, what is big data? In my opinion, the future holds many answers as we move toward the implementation of more advanced systems (such as predictive dialers) and more sophisticated ways of extracting, organizing and analyzing large amounts of data sets. However, for the time being, it seems to boil down to the three Vs: quality, insight and value.