January 31, 2016
The Big Implications of Big Data
Data generation isn’t slowing down. As noted by CloudTweaks, just 6 percent of data is now stored in physical format; the remaining 94 percent is digital. What’s more, 90 percent of all information created by humans has come into existence over the last two years. These are broad strokes but demand a specific response: Big data. By collecting, analyzing and then acting on this emerging stream of data, it’s possible for companies to capitalize on consumer or manufacturing trends before they become common knowledge. However, big data comes with big implications for business processes.
While adopting a big data solution can help manage the influx of data across network endpoints, it can’t stem the tide of this data or predict the speed or volume of informational “traffic.” As a result, companies must be prepared for changing traffic patterns across wide area networks (WANs) as they dig deeper into actionable data. The big implication here? That big data isn’t a “break” for local IT. Instead, it requires a new kind of oversight that focuses on network elasticity and agility to handle the changing demands of data streams.
According to Forbes, another implication of big data adoption is the problem of unknown questions. Data now multiplies at such a rate that it’s possible for companies to glean answers about almost anything — but if they aren’t asking relevant questions, these answers are meaningless. Running catch-all queries could produce a myriad of results detailing the behavior and preferences of customers; in isolation, however, these results often leave companies more confused than confident. The answer? Some organizations are turning to data scientists and other professionals to help design an ideal series of questions, while others curate their queries using line-of-business (LOB) outcomes to ensure results are actionable and timely.
As noted by an IBM Redbook Whitepaper, dealing with big data can have serious implications for network performance, especially when it comes to managing workloads. For example, many businesses rely on multiple Hadoop nodes to effectively distribute processing tasks across their infrastructure, but if one of these nodes fails, the results can range from restarted analysis to massive network slowdown — in turn crippling other critical systems. Here, the solution lies with performance on demand — companies must partner with providers that allow resource increases or reductions on the fly.
Not all big data implications come with warnings and worry. Properly managed, big data volumes offer the potential for high-speed insight that not only draws from existing data sources, but also leverages data in transit to produce real-time, actionable results. Put simply, with the right infrastructure in place, companies can meet big data on their own terms and ensure that relevant relationships are never ignored. Achieving this goal means meeting the challenges of access latencies, rapid interpretation and response times. Though with the right vendor partnerships, companies can offload much of this work to responsible third parties and instead focus on end results.
Considering the adoption of a big data solution? Be prepared; going big means dealing with traffic patterns, question volumes and performance permeability to achieve insight at speed.
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