While some view data science as a logical extension of traditional business intelligence (BI), the professionals that populate the two fields are startlingly different, according to the results of a new survey.

The survey, which was conducted by EMC and polled nearly 500 data scientists and BI professionals, found that computer science and engineering were the two most popular undergraduate majors for data scientists, while business dominates as the top field of study among BI pros. Data scientists are also 2.5 times more likely to have earned a master’s degree and nine times more likely to have a doctoral degree than their BI counterparts.

The tools the two groups use to do their jobs also diverge, according to the survey. While BI pros largely rely on Excel, Data Scientists more often use advanced statistical packages like R, emerging platforms like Hadoop and NoSQL databases, and visualization tools such as Tableau.

The survey also found that most people that consider themselves Data Scientists don’t actually hold that title. Common titles associated with the Data Scientist role are business analyst, analytic manager, and research scientists. One respondent was actually a fisheries biologist.

The upshot of these survey results, according to an accompanying report, is that a successful Data Scientist requires “rigorous scientific training” in order to apply “advanced analytical tools and algorithms to generate predictive insights and new product innovations” from large and varied data sets.

The only problem is that there aren’t enough educational and training resources to prepare the next generation of Data Scientists, according to Data Scientists themselves. Close to two-thirds of respondents said they expect a significant shortfall in the number of Data Scientists over the next five years due to a lack of training and education opportunities.

Courtesy EMC, Copyright 2011

“Making sense of Big Data is a combination of organizations having the tools, skills and more importantly, the mindset to see data as the new ‘oil’ fueling a company,” said respondent Dr. Andreas Weigend, Head of the Social Data Lab at Stanford University and former Chief Scientist at Amazon.com. “Unfortunately, the technology has evolved faster than the workforce skills to make sense of it and organizations across sectors must adapt to this new reality or perish.”

The survey also asked about the conditions needed for Data Scientists to be successful in their jobs. Among the top responses was a willingness on the part of enterprises to allow Data Scientists free reign to explore data as they chose. Data Science, the survey found, is a highly collaborative discipline, which enterprises must embrace in order to make the most of Big Data Analytics:

That means building high-performing, cross-functional teams that include a variety of roles, including programmers, statisticians, and graphic designers, and aligning them to directly support interested business decision makers. They should also loosen restrictions on data in the enterprise, allowing employees to more freely run data-driven experiments. Finally, data scientists should be given free access to run experiments on data, without bureaucratic obstacles, so that they can rapidly translate their own intellectual curiosity into business results.

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The EMC Data Scientist survey highlights the need for more Big Data Analytics training and education opportunities for both new entrants to the job market and to help existing BI pros transition to the role of Data Scientist. It also makes clear that enterprises, particularly non-Web companies with strict corporate cultures like legal and financial services firms, must give Data Scientists the freedom they need to do their jobs.