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Posted December 5 2011

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Global Study

Only One-Third of Companies Making Effective Use of Data

Largest-Ever Data Science Study Cites Looming Talent Shortage, Lack of Open Data Access as Key Opportunity Inhibitors for Big Data

HOPKINTON, MA – EMC Corporation unveiled the findings of the largest-ever global survey of the data science community. Spanning the United States, the United Kingdom, France, Germany, India and China, the Data Science Study reveals and quantifies a rampant scarcity across the globe for the prerequisite skills necessary for a company to capitalize on the opportunities found at the intersection of Big Data and data analytics. Only one-third of companies are able to effectively use new data to assist their business decision-making, gain competitive advantage, drive productivity growth, yield innovation and reveal customer insights.

The survey revealed that the explosion of digital data created by mobile sensors, social media, surveillance, medical imaging, smart grids and the like — combined with new tools for analyzing it all – has created a corresponding explosion in the opportunity to generate value and insights from the data. As such, the business demand for data scientists has quickly outpaced the supply of talent.

The EMC Data Science Study respondents included nearly 500 members of the data science community globally including: data scientists and professionals from related disciplines such as data analysts, data specialists, business intelligence analysts, information analysts, and data engineers globally, all of whom have IT decision-making authority.

Key Findings

* Informed Decision-making – Only 1/3 of respondents are very confident in their company’s ability to make business decisions based on new data.

* Looming Talent Shortage – 65 per cent of data science professionals believe demand for data science talent will outpace the supply over the next five years – with most feeling that this supply will be most effectively sourced from new college graduates.

* Barriers to Data Science Adoption – Most commonly cited barriers to data science adoption include: Lack of skills or training (32 per cent) budget/resources (32 per cent), the wrong organizational structure (14 per cent) and lack of tools/technology (10 per cent).

* Customer Insights – Only 38 per cent of business intelligence analysts and data scientists strongly agree that their company uses data to learn more about customers.New Technology Fueling Growth – 83 per cent of respondents believe that new tools and emerging technology will increase the need for data scientists.

* Lack of Data Accessibility – Only 12 per cent of business intelligence professionals and 22 per cent of data scientists strongly believe employees have the access to run experiments on data – undermining a company’s ability to rapidly test and validate ideas and thus its approach to innovation.

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

* Augmenting Business Intelligence – Although respondents found an increasing need for data scientists in their firm, only 12 per cent saw today’s business intelligence professionals as the most likely source to meet that demand.Higher-Level Skills– Data scientists require significantly greater business and technical skills than today’s business intelligence professional. According to the Data Science Study, they are twice as likely to apply advanced algorithms to data, but also 37 per cent more likely to make business decisions based on that data.

* Love the Work – The study discovered highly favourable attitudes 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 like cloud computing and not surprisingly rate their company's data analysis and visualization abilities very favourably compared to the views of business intelligence professionals.

* Involved Across the Data Lifecycle – Data scientists are more likely than business intelligence 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.

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

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