You hear the phrase big data a lot. But what does it really mean?
Big data is defined as “any voluminous amount of structured, semi-structured and unstructured data that has the potential to be mined for information.” That’s great, that means all businesses need to do, in a nutshell, is drive these large collections of data in an actionable way.
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Businesses today are looking to extract value from this overwhelming amount of information. At the end of the day, the data is meaningless if it doesn’t say something, right? But part of the big data challenge includes knowing what to use but it’s equally important to know what not to use. Companies need to be selective about what they analyze so they don’t drown.
In a survey conducted by Capgemini Consulting in November 2014, 79% of participants reported they have yet to fully integrate all of their data sources. Other implementation issues included data silos, disconnection between groups and ineffective data governance. With that said, to every challenge, there is a solution like investing in tools to tackle big data problems.
Big data applications are of use too but it’s just as easy to get lost in the hype around them. So before you go shopping for them, it’s important to be able to identify the most common use cases. According to Cloudera CEO Tom Reilly, who spoke at the Structure Data conference earlier this year, big data apps fall into three distinct categories: customer insight, product insight and business risk.
Algorithms can be equally helpful in cleaning up the big data mess, but the challenge is identifying which algorithms. Thankfully, a new class of deep learning algorithms can help overcome this challenge.
“In essence, this approach makes it possible to identify hidden patterns buried in large troves of data,” Lawton said. “Although the basic deep learning techniques have been around for decades, they were constrained to work on a single computer. Promising new architectures are now making it possible to scale these deep learning systems to work in the cloud.”
One interesting thing big data can bring is improving business processes – a benefit that probably isn’t as obvious. According to expert George Lawton, McLaren Applied Technologies explored just that and is “somewhat akin to bringing a sim-city like view to the enterprise.” It allows analysts to “tinker with different approaches to optimize important metrics.”
Big data is growing and the rise of mobile, IoT and Web applications are all driving that growth. What will your enterprise do to capitalize on this trend?