Capitalizing on Big Data with Data Science

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It’s easy to get overwhelmed by the marketing hype these days around “big data.” But what does it actually mean in practice? What’s the difference between big data, data science and the kinds of analytics that organizations have been using for years? How do big data systems and modern data science work together to answer new kinds of questions? Scientific Computing spoke with Roy Wilds, Ph.D., of PHEMI, a Vancouver-based big data company, to find out.

When people talk about the data revolution, they’re usually focusing on big data systems. But that’s really only half the story. “Big data” implies this new generation of distributed computing frameworks, like Apache Spark, that can collect huge amounts of information from all manner of sources and aggregate it in one place. That’s a big deal. But the other half of the story is about what you do with that data—and data science is a big part here.

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Roy Wilds leads the data science team at PHEMI Systems, and is responsible for designing and building data analytics and data science software to extract insights from data. Roy has served as data mining team lead for multiple research teams, and has helped build protection tools for various data mining operations. Prior to becoming the Chief Data Scientist at PHEMI, Roy was the Director of Product Management responsible for PHEMI Central™ product development and strategy.

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