To see the need for cloud Big Data analytics, consider the fact that 90% of the world’s data has been created in the last two years. And that the volume of business data worldwide, across all companies, doubles every 1.2 years. In short, the information explosion is very real.
The problem for ordinary mid-sized businesses is that while their own on-premise servers might be able to handle ERP-sized data, the volumes heralded by the arrival of Big Data just choke these on-premises resources to a standstill. To the rescue: cloud Big Data analytics.
Leveraging fast servers and powerful software in the Cloud, cloud Big Data analytics handily tames these Big Data data sets, enabling even quite ordinary businesses to benefit from the detailed insights that cloud Big Data analytics can bring. Answers that were never before economically attainable — drawn from data sets that were never before economically achievable.
In short, the cloud Big Data analytics revolution is very real. And it’s happening now.
Cloud Big Data analytics: Volume, Variety, and Velocity
Even so, it’s caught some businesses by surprise. They read the hype about Big Data, and imagine that their own data volumes can never reach those levels.
Not so. Because apart from anything else, Big Data is more of a state of mind than an absolute number. In fact, all sorts of businesses — including ordinary businesses like yours — can find themselves facing a Big Data challenge, and consequently need to look to cloud Big Data analytics to shoulder the burden of the number-crunching.
How so? Simply consider the definition of Big Data — or at least, the definition of Big Data that is most commonly used: the three ‘V’s.
Namely: Volume, Variety, and Velocity. Put another way, if you’ve got a very large data set, you clearly have a Big Data problem. But the same thing goes for high-variety data, and especially so with a data set that builds at a high velocity.
Let’s take a look — and see how quite ordinary businesses can find themselves needing cloud Big Data analytics.
Cloud Big Data analytics: granular data is everywhere
One Matillion customer, for instance, is in the fast food business, owning a number of franchised outlet chains. Nothing unusual there, you might think.
Start to explore the wealth of insight locked away within its Point of Sale (POS) systems, though, and the highly granular level of detail that such systems provide quickly turns into a Big Data problem, even when attempting to analyse just four or five years’ data.
Not convinced? Check out the numbers: 600 million rows of sales history, and growing at a further million rows a week, covering items ordered, the date and time of day of the transaction, which brand or franchise was involved, which restaurant, the person serving, and the details of any applicable promotion.
Cloud Big Data analytics: data by unit, not by batch
And increasingly, businesses are wanting to get into that level of detail. Not just in customer-facing scenarios, either.
In the world of manufacturing industry, Manufacturing Execution Systems, ‘plant historian’ systems, and quality systems are all capturing huge volumes of data, and often at a high velocity.
Consider the vast amount of data it’s possible to capture on pharmaceutical filling lines, or bottling lines. Once, such data would have been captured on a shift basis, or a batch basis.
But now — spurred by regulatory pressure — manufacturers are moving to capture the details of every individual item produced, along with details such as date of production, time of production, filling or packaging line, and any applicable operating parameters.
Cloud Big Data analytics: processing power on tap
Faced with this tidal wave of highly granular data, ordinary on-premise resources and analytics engines can’t cope. They just don’t have the computing horsepower — especially when data from different systems must first be combined into one data set.
But the flexible resources in the Cloud, of course, can cope. Far more powerful that what’s generally available to ordinary businesses on-premise, they quickly make light work of the task.
Meaning that data sets that a company might once have seen as just too big to analyse can now be mined for insights.
To find out how cloud Big Data analytics can revolutionize the way your business handles Big Data, download our free guide below