Informed Decision-Making

Only one-third of respondents are very confident in their company’s ability to make business decisions based on new data.

About 65 percent of data science professionals believe demand for data science talent will outpace the supply over the next five years, with a majority feeling that this supply will be most effectively sourced from new college graduates.

The most commonly cited barriers to data science adoption include the lack of skills or training (32 percent), budget and resources (32 percent), the wrong organizational structure (14 percent) and the lack of tools and technology (10 percent).

Only 38 percent of business intelligence (BI) analysts and data scientists strongly agree that their company uses data to learn more about customers.

About 83 percent of respondents believe that new tools and emerging technology will increase the need for data scientists.

Only 12 percent of BI professionals and 22 percent of data scientists strongly believe employees have the access to run experiments on data, which undermines a company’s ability to rapidly test and validate ideas and thus its approach to innovation.

Data scientists are three times as likely as business intelligence professionals to have a Master’s or Doctoral degree.

Although respondents found an increasing need for data scientists in their firms, only 12 percent saw today’s BI professionals as the most likely source to meet that demand.

Data scientists require significantly greater business and technical skills than today’s BI professionals. According to EMCs survey, data scientists are twice as likely to apply advanced algorithms to data, but also 37 percent more likely to make business decisions based on that data.

The study discovered highly favorable attitudes among respondents toward the companies where they work. In fact, data scientists believe their IT functions are better aligned and better able to attract talent, are ahead in key technology areas such as cloud computing and-not surprisingly-rate their company’s data analysis and visualization abilities very favorably compared to the views of BI professionals.

Data scientists are more likely than BI professionals to be involved across the data lifecycle-from acquiring new data sets to making business decisions based on the data. This includes filtering and organizing data as well as representing data visually and telling a story with data.

Data scientists are more likely than BI professionals to use scripting languages, including Python, Perl, BASH and AWK. Yet Excel remains the tool of choice for both data scientists and BI executives, followed closely by SQL.