Though it has become ubiquitous in our lives over the past few years, big data is still vastly misunderstood. Most of us know what it is, the numerous places it comes from, and how it can be utilised – but what we’re less familiar with is how to separate the generalised myths from the nitty-gritty facts.
Let’s dig into five of the most prevalent myths around big data, and the grains of truth that fuel them.
Big data can predict the future
Wouldn’t it be amazing if this was true? Obviously, it isn’t. As with any form of data analysis, big data can identify trends and extrapolate to predict potential future scenarios, but – given that not all data is reliable, and not all reliable data is accessible – it is never going to be 100% accurate. Take weather predictions for example: sometimes they’re reasonably accurate, and other times your app might tell you there are clear skies outside when you can very clearly see it’s pouring with rain.
Big data is everywhere
Big data is certainly far more widespread than it used to be – but everywhere? Hardly. According to a report from Forbes, only a little over half of major companies surveyed were utilising big data in 2017. At present, big data analysis is still very much in the growth stage, and more companies are seeking to use it than already have analysts in place.
That being said, the data itself is always growing, and has been doing so since the dawn of the internet. It might not be everywhere yet, but it’s well on its way.
Utilising Big data requires a big budget
At present, the front-runners in big data analysis are corporations with a big budget to match. However, that doesn’t mean that smaller companies can’t jump on the big data hype on a smaller scale. Plus, given the rate at which the need for big data is increasing, the benefits of taking on specialists will likely outweigh the costs in the imminent future.
Big data is always good data
As we mentioned before with big data not being able to predict the future, not all material harvested is going to be useful for specific requirements. The trick is knowing exactly how to analyse the data you have – but even then there will be some ambiguity.
Big data is… big
As far as the term goes, big data is something of a misnomer. A better name might be “Diverse Data” (but that’s not exactly as catchy, is it?). Yes, there’s a lot of it, but the singular datum within a set might be something as small and specific as the time of day a certain product was bought from a particular retailer. With enough of it, though, this data is incredibly useful – so there’s no doubt that its impact is ‘big’.