Too Few Companies Use Data Effectively

A new EMC global study has determined that about two-thirds of enterprises are ill-prepared to take advantage of the explosion of digital data created by mobile sensors, social media, surveillance, medical imaging, and smart grids.

EMC Corp. unveiled the findings of the largest-ever global survey of the data science community

Spanning the U.S., the UK, France, Germany, India, and China, the EMC 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 one-third of respondents are very confident in their company’s ability to make business decisions based on new data.
  • Looming Talent Shortage—65% 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%), budget/resources (32%), the wrong organizational structure (14%), and lack of tools/technology (10%).
  • Customer Insights—Only 38% of business intelligence analysts and data scientists strongly agree that their company uses data to learn more about customers.
  • New Technology Fueling Growth—83% of respondents believe that new tools and emerging technology will increase the need for data scientists.
  • Lack of Data Accessibility—Only 12% of business intelligence professionals and 22% 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% 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% more likely to make business decisions based on that data.
  • Love the Work—The study discovered highly favorable 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 favorably compared with 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.

Data Scientists Quotes

Andreas Weigend, Ph.D Stanford, Head of the Social Data Lab at Stanford, former Chief Scientist “We live in a data-driven world. Increasingly, the efficient operation of organizations across sectors relies on the effective use of vast amounts of data. 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. 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.

Michael Driscoll, Ph.D Boston University, Co-Founder and CTO at MetaMarkets: “Neither tools nor people alone can solve the challenges of Big Data. They must work together, and that is the promise of data science. Despite advances in software tools, the number of people with experience using these tools, and with real-life exposure to large-scale data sets, is small. Data science is a young field, and its growth will be fueled as much by technology as through the mentorship of new acolytes by leading practitioners.

EMC Executive Quote

Jeremy Burton, EVP and Chief Marketing Officer, EMC Corporation: “The Big Data era has arrived in full force, bringing with it an unprecedented opportunity to transform business and the way we work and live. Through the convergence of massive scale-out storage, next-generation analytics, and visualization capability, the technology is in place. What’s needed to fully realize its value is a vibrant, interconnected, highly skilled, and empowered data science community to reveal relevant trend patterns and uncover new insights hidden within.

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