It’s Here and It’s Huge: Big Data Analytics

Why is that you see advertisements concerning the same product over and over again, during a specific period of time? Or why is your number a part of the database of each and every service provider in the country (and hence their constant pestering)?

The not-so-minimal solution: Big Data Analytics.

bigstock-Big-data-concept-in-word-tag-c-49922318Source – http://olap.com/wp-content/uploads/2013/11/bigstock-Big-data-concept-in-word-tag-c-49922318.jpg

Stated using the simplest of words, it’s a collection of data, and a very, very big collection at that, of structured, semi-structured or unstructured data, which is then organized and mined for information regarding the current trends in various fields of business, consumer market etc. Basically, it’s one of the more organized and advanced tools under Predictive Analysis.

What is actually does: It notes down how, when and why of the users’ browsing, or rather, their *seeking out* information from the numerous sources available, and plot them so as to get an indication of how things are working out for the particular product or service we’re talking about. To then maximize the sales and increase consumer satisfaction, it presents the same with more a more directive approach to the same set of people.

Something close to the law of attraction, maybe?

Here are some pointers you may want to know.

  1. How BIG is Big Data?

It’s huge actually, so huge that it has become difficult to manage it using the common, everyday software we otherwise use to manage data. Imagine a set for all the people using the net at this very moment, that’s how Big we’re talking.

  1. It’s fast. Very fast.

Each time you Google something, the Data is updated. How many times do you Google in a day? Multiply that with the number of people all over the world who access Google on a daily basis.

Exactly, that’s how fast it is.

  1. It’s diverse.

Unlike databases business professionals manage on a daily basis, Big Data is not that easy to handle. It’s diverse, the reason being very simple – not everybody’s browsing patterns are the same. Also, there being multiple sources of collection, the recording patterns vary from industry to industry.

  1. Everybody contributes.

Web browsing, payments made using credit cards, uploading pictures on social networks, using 1000 hash tags to *describe* them, to the daily *essential* usage of your phone, contributes to Big data. Basically, any activity that can be taped using electronic systems acts as a contributor to Big Data, which in turn makes each of us a contributor.

PrintSource – http://www.glowmetrics.com/services/training/

5. And hence, it’s complicated.

Coming in from mixed sources, it’s not uniform in terms of structure and manner. It makes it all the more difficult to handle and analyze. Therefore, however diverse its areas of application may be, it’s not something that every other institution can employ to note down trends. That is what limits its current usage span.

Despite all the technical issues it’s facing currently, Big Data is revolutionary. Imagine what being able to provide each and every customer of yours what he/she wants specifically, without having to lure them in using a whole array of products/services could do for a company. Miracles, that’s what. Cost-cutting, better customer satisfaction, better financial input/output ratio, you name it.

However, there still are some issues companies need to take care of, before plunging into this sector of never-ending inputs and analysis.

  1. You may not get what you ask for.

Mostly, the answers we get depend on how we ask for something, rather than on what we ask for. For sake of simpler explanations – If a company wants to monitor trends in consumption of a product, the direct market inputs may work well. What they might miss out on is the co-relation between consumption and customer satisfaction, as consumers don’t talk about such things openly on the web. Hence, Big Data provides snippets which need to be put together correctly to be able to realize the big picture.

  1. After all, it predicts human behaviour, which is known to be quite unpredictable.

Big Data is not the complete solution to consumer satisfaction; it’s just an easier way to go about it. Based on one’s previous blogging records, even if we provide him/her with a pretty picture of what his/her life could be with the product/service they looked up for, and yet, they might not buy it. They might even stop *wanting* it, if pestered as much. There’s always the risk-factor.

  1. Technical Issues/Human Error

Then come the evergreen issues of analysis, an error in technique or the deliberate human error. Whilst they are easier (comparatively) to tackle, they concern us still.

As the world of technology overcomes anything that may hurdle the word development in any sense, it would certainly overcome the ones concerned with Big Data. As obvious now, there lies a huge, huge potential in Big Data Analytics, when we talk about a world centred on consumerism. The numbers are not very significant currently, as only 3 out of 10 companies have the requisite technical expertise, but the scenario is still very hopeful from the side of the corporate.

ConsumerSource – http://researchrockstar.com/customer-satisfaction-survey-results-jumping-to-conclusions/

As far as the consumer goes, it is both a boon and a bane. On one side, it redefines what we call having choices, on the other, it kind of piles them up. Where we definitely like being catered to, when it comes to tailor-made options, we don’t need to hear about them all day long. Here comes in the essential way out for the companies: being the centrist. As usage of Big Data becomes wide-spread, the efficiency would definitely increase, in terms of how and what to offer, and most importantly, how many times to offer, that is what we look forward too.

 

 

 

By Snigdha Singh

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