What Is Big Data – 6 Things You Really Need To Know
What is ‘big data’? If you’ve read countless descriptions and still can’t get your head around the definition of this mythical form of technology, or where the line between ‘little’ data and its larger cousin is drawn, we’d like to let you into a little secret.
Big data is rarely, actually, big data.
Let’s step back a bit. We’ve set ourselves the challenge of answering the question posed at the start of this post as succinctly as possible.
What Is Big Data?
Think of a huge database whose primary purpose is to house information on human behaviour and habitual actions. That’s big data.
What Is Big Data Used For?
Corporations can use big data to analyse customer behaviour and spot trends or patterns relating to the way they shop, purchase and browse the web. The knowledge gained from big data can then be used to target consumers with more relevant products and services.
So… Why Isn’t Big Data Big?
The answer is a rather technical one, but fascinating nonetheless. There was a time when large organisations wanted the largest and most powerful datasets imaginable in order to conduct their own big brother-style investigations into human behaviour. In reality, the majority of the problems they were looking to solve simply didn’t require large scale data.
Far smaller snapshots of data can now be used to pinpoint those all important touch points that provide answers to the questions posed by marketing departments. So powerful is the computing hardware of today that these datasets can actually be streamed from super-fast SSD hard drives and reside within the memory of a relatively basic PC.
So, leave any thoughts of humongous data centres and all-seeing eyes at the door, because big data isn’t as big as its name suggests.
Here are 6 more common misconceptions about big data:
1. Big Data Is Complex
Big data is simply a larger volume of the kind of stuff we hold in smaller databases. Think DOBs, last purchase dates, products browsed, favourite brands – that kind of thing. A well-design big dataset should, in fact, be simple in its construction if it is to be used effectively.
2. The More Big Data The Better
As noted earlier in this post, big data doesn’t mean hundreds of millions of rows of database entries – far from it. The old adage of ‘quality rather than quantity’ absolutely applies to big data.
3. Everything Needs Capturing To Make It Worthwhile
Capturing every single datapoint may sound like exactly what big data is all about, but the reality is far different. Again, quality is key, therefore recording countless details that will never be analysed is a waste of everyone’s time and, at worst, slows down the process of analysing big data.
4. Big Data Only Benefits Big Companies
Not true. Just as enterprise-level software is now available to businesses with budgets of all sizes, the same goes for big data. We’ve already established that big data runs on moderate hardware, therefore the tools required to get the most from it are accessible to any organisation and should absolutely be used if the industry demands it.
5. Big Data Is A Mess
In point 1, we noted that big data shouldn’t be complex, and if that rule is followed, the resulting datasets won’t be a mess. Far from it, in fact – big data, in order for it to be accessible and of value to those analysing it, has to be well-structured and tidy.
6. Big Data Knows Everything
Perhaps the biggest misconception about big data is that it is some kind of all-seeing eye which is able to provide answers on anything an organisation desires. Not true, I’m afraid. Big data can assist marketing plans and provide a window into the world and habits of customers but it should always be combined with more traditional forms of market research. Relying solely on big data can actually be rather dangerous.
The world of big data is, clearly, full of myths and half truths, but one thing remains true – it is an invaluable tool for businesses of all sizes. The trick is to not get overwhelmed by big data, because it simply isn’t the colossus you may think.