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?
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