Largest-ever Data Science Study cites looming talent shortage, lack of open data access as key opportunity inhibitors for big data.
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 analysing 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.
* 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 supply over the next five years – with most feeling that this supply will most effectively be 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 organisational structure (14%), and lack of tools/technology (10%).
* Customer insights – only 38% of business intelligence analysts and data scientists strongly agree that their companies use data to learn more about customers.
* New technology fuelling 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 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 visualisation abilities very favourably compared to the views of business intelligence professionals.
* Involved across the data life cycle – data scientists are more likely than business intelligence professionals to be involved across the data life cycle - from acquiring new data sets to making business decisions based on the data. This includes filtering and organising 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.
“We live in a data-driven world. Increasingly, the efficient operation of organisations across sectors relies on the effective use of vast amounts of data. Making sense of big data is a combination of organisations 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 organisations across sectors must adapt to this new reality or perish,” said Andreas Weigend, PhD Stanford, Head of the Social Data Lab at Stanford, former Chief Scientist Amazon.com.
“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 fuelled as much by technology as through the mentorship of new acolytes by leading practitioners,” said Michael Driscoll, PhD Boston University, Co-Founder and CTO at MetaMarkets.
EMC executive quote:
“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 visualisation capability, the technology is in place. What's needed to fully realise its value is a vibrant, interconnected, highly-skilled and empowered data science community to reveal relevant trend patterns and uncover new insights hidden within,” said Inana Nkanza, Country Manager of EMC Southern Africa.
* Read the EMC Data Science Study in full
* Read Chuck Hollis's blog: 'Understanding The New Rock Star: The EMC Data Science Survey'
* Read the EMC Education Services release announcing new training and certification programme focused entirely on Data Science & Big Data Analytics
* Connect with EMC via Twitter, Facebook, YouTube, LinkedIn and ECN.
EMC Data Science Study
EMC conducted an independent research study to measure attitudinal and behavioural profiles of data scientists and business intelligence professionals globally and across industries. A total of 462 IT decision-makers who self-identified as either a data scientist or business intelligence professional participated in this market research study through participation with research partner Toluna. An additional 35 participated by invitation through their previous attendance at the EMC Data Scientist Summit and the online data scientist community Kaggle. The survey was designed to provide a first-ever look into the behavioural patterns, thoughts, and needs of the data science community.
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