Big Data Is More Than Just a Buzz Word!

We all agree big data sounds cool (well I think it does!), but what is it?

Put simply, big data is the term used to describe massive volumes of data. We are thinking of data along the lines of “Terabytes,” “Petabytes,” and “Exabytes” in size. In my opinion, that’s as simple as it gets when thinking about the term “big data.” Despite this simplicity, big data has been one of the hottest, and most misunderstood, terms in the Business Intelligence industry recently; every manager, CEO, and Director is demanding it. However, once the realization sets in on just how difficult big data is to implement, they may be scared off!

The real reason behind the “buzz” was all the new benefits that organizations could gain from big data. Yet many overlooked the difficulties involved, such as:

1. How do you get that level of data?
2. If you do, what do you do with it?
3. Cultural change is involved, and most decisions would be driven by data. No decision would be made without it.
4. The cost and skills required to implement and benefit from big data.

The concept was misunderstood initially; organisations wanted data but failed to understand what they wanted it for and why, even though they were happy to go on the chase.

Where did the buzz start?

I truly believe Hadoop is what gave big data its fame. Initially founded by Yahoo, used in-house, and then open sourced as an Apache project, Hadoop served a true market need for large scale storage and analytics. Hadoop is so well linked to big data that it’s become natural to think of the two together.

The graphic above demonstrates the similarities in how often people searched for the two terms. There’s a visible correlation (if not causation). I would argue that “buzz words” in general (or trends) don’t take off before the technology that allows them to exist does. If we consider buzz words like “responsive web design”, they needed the correct CSS rules; “IoT” needed Arduino, and Raspberry Pi and likewise “big data” needed Hadoop.

Hadoop was on the rise before big data had taken off, which supports my theory. Platforms like Hadoop allowed businesses to collect more data than they could have conceived of a few years ago. Big data grew as a buzz word because the technology supported it.

After the data comes the analysis

However, the issue still remains on collecting data with no real purpose, which ultimately yields very little in return; in short, you need to know what you want and what your end goal is. This is something that organisations are slowly starting to realize and appreciate, represented well by Gartner’s 2014 Hype Cycle.

Big data is currently in the “Trough of Disillusionment,” which I like to describe as “the morning after the night before.” This basically means that realisation is setting in, the excitement and buzz of big data has come down to something akin to shame and regret.

The true value of big data can be categorised into three sections: Data types, Speed, and Reliance. By this we mean: the larger the data, the more difficult it becomes to manage the types of data collected, that is, it would be messy, unstructured, and complex. The speed of analytics is crucial to growth and on-demand expectations. Likewise, having a reliable infrastructure is at the core for sustainable efficiency.

Big data’s actual value lies in processing and analyzing complex data to help discover, identify, and make better informed data-driven decisions. Likewise, big data can offer a clear insight into strengths, weaknesses, and areas of improvements by discovering crucial patterns for success and growth. However, this comes at a cost, as mentioned earlier.

What does this mean for big data?

I envisage that the invisible hand of big data will be ever present. Even though devices are getting smaller, data is increasing at a rapid rate. When the true meaning of big data is appreciated, it will genuinely turn from a buzz word into one that smaller organisations might become reluctant to adopt. In order to implement big data, they will need to appreciate the need for structure change, the costs involved, the skill levels required, and an overall shift towards a data-driven culture. To gain the maximum efficiency from big data and appreciate that it’s more than a buzz word, organizations will have to be very agile and accept the risks to benefit from the levels of change.