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Contextualizing Big Data for the Everyday Business

Words like terabyte, petabyte, exabyte and zettabyte among many others have entered the business lexicon holding the hands of big daddy -- yes, we're talking about big data here.
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Words like terabyte, petabyte, exabyte and zettabyte among many others have entered the business lexicon holding the hands of big daddy -- yes, we're talking about big data here.

As data is growing at a monstrous speed, businesses are left grappling on how to curate data or how to even make sense of it. According to a Gartner report, through 2015, 85 percent of the Fortune 500 companies will fail to exploit big data for competitive advantage. As extraction of data becomes difficult, organizations will have to develop resources and strategies to tap into the unlimited potential of big data or else they can never really gain from the info-thick big data that holds the golden key to future success.

Harness the power of data to maximize relevance

When pulling out information from the huge data pile, our main focus should be on data that's relevant to our business. Big data holds a sea of precious information which includes customer behavior, market demands, changing needs, shifting preferences and more such valuable insights that could help businesses grow and adapt to the changes quickly. To build this capability, businesses will need to improve their network infrastructure, reinforce their analytical abilities and enhance the intelligence of their business operations. To fully understand the scope of integrating big data analytics in their framework, businesses have to look deep down within their operations, processes and databases. Contextualization comes as a second step to this.

Build contextualization through three data types

Demographic data

This type of data explains who the customer is in big, bold words. Customer preferences, purchase patterns, behaviors, concerns and their interaction with digital channels give businesses a clarity on the nature of their customers.

Historical data

Past records of customer interaction, whether they made purchases, whether or not they were satisfied -- all of these fall under the category of historical data. Such information can be gathered from the data trail that customers leave when they interact with any company while visiting the website, spending time on various product pages or making purchases. This data acts as a way to predict the behaviour and future actions of customers.

Situational data

The current geographic location of the customers, devices used by them, their current online activities etc. help organizations gain an idea of what are the current preferences of the customers or what are they looking for at a particular time.

These data types help business gain full context of every customer interaction. Moreover, categorizing data helps to systemize the process of data mining and comprehension so that the information can be used in the most effective manner.

Today when big data has become an essential for companies, they should be able to make sense out of it in order to utilize its benefits. Contextualization not only helps to comprehend the data in the best possible manner but it also helps in gathering and storing data in ordered groups and sequences. Once this process is in place, organizations can use big data to unlock various key insights that will help them prepare their businesses for the future.