Thanks to our increasingly-wired society, nearly everything we do produces a data trail. By examining and analyzing these data trails; by seeking the common patterns in customer and employee behavior, organizations can gain access to new and previously unknown insights. It would not be much of an exaggeration to call these insights the Holy Grail of analytics.
This, ladies and gentlemen, is big data – and it’s currently one of the most powerful motivators on the market for cloud adoption.
Part of the reason for this is the sheer amount of information that needs to be sorted through in order to gain anything of value. Big data is just that – big; too voluminous for traditional database software and impossible to sift through by hand. This issue is compounded by the fact that, for every nugget of insight, there exists potentially thousands (or millions, or billions) of useless lumps.
Not surprisingly, this means that most big data analytics solutions require a massive amount of processing power and create a significant computing overhead. That means these solutions are impractical for many enterprise clients. Except in the most extreme cases, most organizations aren’t really going to be capable of constructing and managing their own server clusters to address big data – and even those businesses that can afford to do so might find it prohibitively expensive, at best.
That’s where the cloud comes in. As a technology, cloud computing is uniquely-suited for delivering big data resources. It’s elastic and scalable, meaning it can quickly and easily adapt to the massive processing spikes generally required to analyze big data. On top of that, it’s also incredibly easy to set up – and sync – a database on the cloud, much more so than it would be with a traditional server.
What this all amounts to is that an organization can more quickly and efficiently analyze the data it collects by using a cloud service. Given how fast the value of data tends to drop as time goes by, this is an extremely potent advantage. Of course, even this pales in comparison to the cloud’s greatest strength: cost.
Analyzing big data on the cloud is a minimal investment compared to what it would cost to purchase the necessary resources on-demand, to say nothing of the savings it offers when pitted against the expenses involved in setting up one’s own server clusters. Since the cloud generally operates on a pay-per-use model, this means that big data resources can be provisioned as-needed. One’s organization will gain all the same insights, at a significantly-reduced cost, paying only for the resources they actually use.
It doesn’t seem like much of an exaggeration to call big data the Holy Grail of analytics. The insights it offers can be the difference between an organization’s success and failure. All that power comes with a price, however – an extremely hefty one.
That’s where cloud computing comes in. With its easy provisioning, rapid scaling, and pay-per-use model, it cuts the price of big data analytics to a degree that even small businesses can afford it. That, more than anything, is why big data and the cloud go hand-in-hand.