Contrary to popular opinion, the world is not becoming more complex — the opposite is true. The fact that we create 2.5 quintillion bytes of new data per day simply means we are more capable than ever of identifying, analyzing and simplifying the complexities that come with immense amounts of information.
This capability is Big Data, and it is transforming industries worldwide. Ninety-three percent of companies worth more than $250 million rate it as either "important" or "extremely important" in their day-to-day operations. However, the obstacles are as significant as the opportunities.
Capitalizing on Big Data is a struggle. And the first stumbling block for most enterprises is trying to scale storage capacities to match Big Data's wide footprint. After all, data cannot become "big" unless it can be effectively accumulated, and it cannot become useful unless it is properly managed and analyzed. Overcoming this hurdle requires a forward-thinking approach based on the emerging concept of "big storage."
Despite the widespread reliance on Big Data, many enterprises still use legacy storage solutions designed to meet 20th-century data requirements. Up to 53% users reported that the performance of their storage solutions is no longer adequate, according to Tintri's 2015 survey of 1,000 data center professionals.
These legacy systems — such as network attached storage systems — are not only cumbersome, but also difficult and expensive to upgrade. In addition, they generally lock companies into continuing to purchase expensive, proprietary hardware that cannot evolve with the needs of the business and are simply not suited to accommodating the depth and breadth of Big Data.
The data is too, well, big, and its variety of file types surpasses the capabilities of outdated technology. As a result, something as seemingly benign as data storage often proves to be a major inhibitor of growth.
This explains why so many forward-thinking leaders are moving to a new software-defined storage paradigm in search of a scale-out solution that will not bottleneck under Big Data's weight.
Big storage is an alternative uniquely capable of meeting the requirements of Big Data. It allows the establishment of a vast "data lake" that's free of file hierarchies and restrictive load limits and that's accessible by Hypertext Transfer Protocol.
Rather than straining to meet the needs of the present, big storage must be designed to exceed the needs of the future.