Survey: Variety, Not Volume, the Biggest Challenge to Analyzing Big Data

July 9, 2014
While the huge volume of big data in healthcare continues to generate headlines, a new report finds that it’s the diverse types of data that’s a bigger challenge to data scientists and is causing them to “leave data on the table.”

While the huge volume of big data in healthcare continues to generate headlines, a new report finds that it’s the diverse types of data that’s a bigger challenge to data scientists and is causing them to “leave data on the table.”

According to a new survey by the Waltham, Mass.-based computational database company Paradigm4, nearly three-quarters of data scientists—71 percent—said big data had made their analytics more difficult, and data variety, not volume of data from electronic health records (EHRs), connected mobile devices, genomic sequencers and sensors, was to blame.

The survey also showed that 36 percent of data scientists say it takes too long to get insights because the data is too big to move to their analytics software. These issues cause data scientists to omit data from analyses and prevent them from maximizing the value of their work.

Additional results of the survey, which were generated from 111 data scientists in the U.S., found that 91 percent of data scientists said they're using complex analytics on their big data now or plan to within the next two years. Forty-nine percent of respondents said they're finding it more difficult to fit their data into relational database tables. And 40 percent said their biggest problem they face in gaining insights from their big data is managing new types and sources of data.

 “The increasing variety of data sources is forcing data scientists into shortcuts that leave data and money on the table,” Marilyn Matz, CEO of Paradigm4, said in a statement. “The focus on the volume of data hides the real challenge of data analytics today. Only by addressing the challenge of utilizing diverse types of data will we be able to unlock the enormous potential of analytics.”

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