Five Ways to Know if Your Challenge Is Big Data or Lots of Data

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The power of Big Data does not originate from some singularity somewhere in cyberspace. Back in the days when a terabyte (TB) of enterprise-class storage cost millions, not $79.99, and processors were clocked in kilobytes per second, everything was Big Data. Today, when a typical smartphone has access to almost unlimited amounts of storage and processing in the cloud, defining Big Data vs. lots of data comes down to what you want to do with that data.

If you are looking for new business insights by combining your data with data from the outside world and then throwing a bunch of advanced analytics at it to answer pressing business questions — like when to jump into a new market or what color shoes to recommend to your customer based on their preference and the preferences of people just like them — then you are looking at a Big Data challenge.

But, if you just want to move massive amounts of data around your network faster and faster, for example, or take an overnight batch process and turn it into a few hours, then, really, you are dealing with a lots of data problem. What you want to do with your data makes it Big Data, not how much of there is.

With Big Data making headlines daily, it’s easy to mistake “lots of data” for “Big Data.” As most IT folks agree, organizations of all stripes, from government agencies to academia, have been dealing with massive data sets for years. “But just because you have a lot of data, that doesn’t mean it should be considered ’Big Data,’” says Jim Gallo, national director of business analytics at ICC, a leader in business technology solutions focusing on big data and application development.

“If an organization has large volumes of structured data – point-of-sale data, inventory data, sensor data -– that doesn’t translate directly to a Big Data problem or opportunity,” says Gallo.

Today, most organizations use data warehouses and business intelligence (BI) suites to meet their analytics needs. But BI suites are limited to analyzing structured data in relational databases. When you combine the three “Vs” of Big Data – volume, variety and velocity – with unstructured data such as YouTube videos or medical images with the desire to learn something new from those mashups, you enter Big Data territory, according to Gallo.

“When you want to do something other than store and fetch images; when you begin to look inside the images and draw correlations to other data types like electronic health records (EHRs) or a Twitter feed or weather data, that’s when you have a Big Data challenge,” says Gallo.

So how can an organization know if the challenge it is facing is Big Data or just lots of data?


Related Topics : Vulnerabilities and Patches, Resellers, Broadcom, Broadband Services, Supercomputing

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