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How to Solve the Big Challenge of Big Data: Part 1

by on June 28, 2011

Managing Big Data environments is becoming a big headache for organizations of every size. And because it’s typically made up of millions, and sometimes billions, of small files, Big Data is an intimidating prospect for any self-respecting backup application. Nevertheless, CIOs are embracing these mountains of large, complex and dynamic data sets because they contain a treasure trove of valuable business insights and information.


So, exactly how big is “big?” Most environments are represented not in multiple gigabytes, but in hundreds of terabytes and, in some cases, dozens of petabytes. All of this structured and unstructured data comes from data centers, offices, desktops, laptops and handheld devices, as well as social media, to name a few sources.

And exactly why is Big Data so valuable to organizations? In a word, analytics. In addition to retaining it for compliance requirements, businesses are slicing and dicing their Big Data to spot key business, customer and market trends. Large organizations such as global pharmaceutical companies have always understood the importance of analytics. But a recent IBM study revealed that squeezing out every bit of meaningful information from their structured and unstructured data is now a key goal for CIOs of mid-sized companies. Even small companies are increasingly viewing data as a strategic differentiator that will help them grow their businesses.

Now for the $64,000 question:  How do you effectively corral and store Big Data to ensure future access and security, and facilitate compliance and analytics requirements? In my next post, I’ll take a shot at answering this one and talk about why active archiving is definitely part of the solution.

Read more with How to Solve the Big Challenge of Big Data: Part 2 

One Comment
  1. There are a lot of methods that have been tried and tested out there (like shadow system, vaulting, etc). Most differ because they utilize varying tools and capacities when used for data management. I’m wondering which method fits for what-sized companies? Or is there even such a correspondence?

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