Understanding The New Rock Stars: data scientists


Image002

Original Post = http://chucksblog.emc.com/chucks_blog/2011/12/understanding: the: new: rock: star: the: emc: data: science: survey.html

If Big Data is Big then the race is now on to acquire and maximize the productivity of the key talent behind this wave: data scientists and their supporting data science teams.

"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" fuelling 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." Andreas Weigend, Ph.D Stanford, Head of the Social Data Lab at Stanford, former Chief Scientist Amazon.com

Key Findings from the report

  • 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% of data science professionals believe demand for data science talent will outpace the supply over the next 5 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 3 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 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.

The full study is here : http://www.emc.com/collateral/about/news/emc-data-science-study-wp.pdf