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

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

    If most of your data is in a data warehouse somewhere sitting in nice rows and columns without a lot of interaction with the outside world, then, almost regardless of size, you are not dealing with "Big Data." There are a few caveats here, however. If that data needs to be ready at a moment's notice to help in, say, fraud detection at a credit card provider, that moves you into the area of Big Data. It becomes Big Data because the information is being used in a real-time process – one that takes its cues from any number of sources to determine if someone is getting ripped off or if they are on vacation. Luckily, today's emerging crop of high-speed data bases is well equipped to handle this issue.

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

  • 1 | 2 | 3 | 4 | 5 | 6 | 7
  • Five Ways to Know if Your Challenge Is Big Data or Lots of Data-5

    If most of your data is in a data warehouse somewhere sitting in nice rows and columns without a lot of interaction with the outside world, then, almost regardless of size, you are not dealing with "Big Data." There are a few caveats here, however. If that data needs to be ready at a moment's notice to help in, say, fraud detection at a credit card provider, that moves you into the area of Big Data. It becomes Big Data because the information is being used in a real-time process – one that takes its cues from any number of sources to determine if someone is getting ripped off or if they are on vacation. Luckily, today's emerging crop of high-speed data bases is well equipped to handle this issue.

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?