5 January, 2017
How Small Data Became Bigger Than Big Data
Making big data smaller is the wave of the future. If you aren’t already doing it, you are already behind. Computers and software, at the moment, are only as good as their coders and you need human eyes to see the big picture.
Anyone in the tech world knows that there’s currently a lot of fuss surrounding big data, I hate this word. Not only because I have written about it so much but because it isn’t the right terminology. “Big” Nah, more like “infinite” or “raw.”
Despite the fact that it should be called something else, the importance of collecting it, analyzing it, and the application of your newly acquired big data knowledge is all conceived as tools. We do this to make smarter strategic decisions, reduce costs, target the right audiences, recalculate risk portfolios, optimize offerings, and overall run your business as efficiently as possible. With projected sales of data analytics tools hitting $187 billion in 2019, it’s apparent that this method of optimizing your business possibilities isn’t going away.
The breakdown of big data
To get to the bottom of all this big data hype, we should probably uncover the root of what it actually is. Big data is essentially the massive amount of structured and unstructured information that overwhelms a business daily, whether it’s from business transactions, machine-to-machine data, or social network interactions. The idea is that the available data is so intricate and vast that standard data-analyzing technologies aren’t going to be adequate enough to handle them. Because it’s such a vague term replete with possibilities, you can boil it down to a simple concept: Big data is data that is drawn from various sources and imperative to making decisions that have a positive impact on a business.