Insight ReportData Science in the New EconomyA new race for talent in the Fourth Industrial RevolutionJuly 2019Centre for the New Economy and SocietyThis Report is produced by the World Economic Forum’s Centre for the New Economy and Society as part of its New Metrics CoLab project. For more ination, or to get involved, please contact cnesweforum.org. This Report has been published by the World Economic Forum as a contribution to a project, insight area or interaction. The findings, interpretations and conclusions expressed herein are a result of a collaborative process facilitated and endorsed by the World Economic Forum, but whose results do not necessarily represent the views of the World Economic Forum, nor the entirety of its Members, Partners or other stakeholders.World Economic Forum 91-93 route de la Capite CH-1223 Cologny/GenevaSwitzerland Tel. 41 022 869 1212 Fax 41 022 786 2744 Email contactweforum.org www.weforum.org World Economic Forum 2019 – All rights reserved. No part of this publication may be reproduced or transmitted in any or by any means, including photocopying and recording, or by any ination storage and retri system. REF 020719Contents04 Key FindingsKey insightsImplications for decision-makers06 Emerging Demand for Data Science Skills Across IndustriesActionable Insight Source Data and Model10 Learning Achievements in Data Science Skills Across Industries and RegionsActionable Insight Source Data and Model14 Changing Composition of Data Science Skills Within RolesActionable Insight Source Data and Model19 A Look at Future Demand for Data Science Jobs21 AcknowledgementsThis Report has been published by the World Economic Forum as a contribution to a project, insight area or interaction. The findings, interpretations and conclusions expressed herein are a result of a collaborative process facilitated and endorsed by the World Economic Forum, but whose results do not necessarily represent the views of the World Economic Forum, nor the entirety of its Members, Partners or other stakeholders.04 Data Science in the New Economy Key FindingsThrough three industrial revolutions, technology has led to significant changes across economies, societies and businesses. Steam engines jump-started the transition of societies from agriculture to industrial production. The use of fossil fuels in engines and innovation in business models such as the assembly line rapidly scaled production. More recently, the digital revolution brought computing power and ination technology. Each successive industrial revolution has involved significant shifts in the way people live and work, in how value is created in the economy, and demand for the highest-value skills.As the Fourth Industrial Revolution unfolds, led by advances in technologies such as data science and artificial intelligence, the labour market is again changing in a fundamental fashion. In 2018 the Future of Jobs Survey and Report revealed that business leaders believe that by 2022, human workers and automated processes are set to share the workload of current tasks equally, while a range of new roles is expected to emerge simultaneously as digital innovation is absorbed across industries and regions. In particular, in many large advanced and emerging markets, growth is expected in sectors that will experience the bulk of these new roles, such as ination technology, renewable energy, education and the care economy, and in occupations such as data science, healthcare work and human resources.While the new labour market is changing at a rapid pace, emerging data sources are shedding light on its composition with a new depth and dynamism that has not previously existed. Online plats and specialized insight firms are now offering new and complementary ways to understand how specific skills, tasks and occupations are changing across industries and geographies. While many of these remain limited to specific populationsand difficult to compare and contrastwhen coupled with traditional and qualitative sources of data, they can help businesses, policy-makers and workers have greater analytic capacity about the present and future of work and adopt better ined and coordinated business strategies and policies.The World Economic Forum’s Centre for the New Economy and Society is a plat for insights and action on emerging socio-economic issues. As part of its agenda, the Centre offers an opportunity for companies to partner with the Forum to share insights on emerging trends in a diverse set of issues such as education, skills, jobs, gender and inclusive growth. This Report is among a series of such collaborations aimed at developing new metrics and deploying data for shedding light on public good challenges.It has become commonplace to refer to data as the ‘new oil’ of the global economy. Data scientists are the talent that provide the ability to extract, refine and deploy this new source of value in the global economy. This Report focuses on data science, among the most competitive skills of the Fourth Industrial Revolution, in collaboration with Burning Glass Technologies, LinkedIn and Coursera to shed light on how data science talent is being developed and deployed across today’s labour market.In the first wave of digitization, data could be seen as purely a by-product of the functioning of digital applications, operating systems and plats. Data is now increasingly recognized as a significant asset enabling further innovation across ancillary fields such as artificial intelligence, which can drive the improvement of services through process efficiency and deliver better results for customers. The dividends of new sources of data and s of processing are not limited to the private sector; the public sector increasingly uses data to improve government services and academia applies new s to enhance research. Yet the rapid rise in the demand for workers with skills in data science has led to a shortfall in data science skills supply and intense competition between industry, academia and the public sector for such talent. This has created a high premium on such skills and has reduced the capacity of businesses, industries and entire economies to leverage fully the dividends of innovation.What do we know about how recent demand for this talent is emerging across industries, how data science skills are becoming part of the core composition of roles and how workers are expanding their data science skillset through new learning across industries and regions What do we know about how data science demand might be expected to grow in the futureIn this paper, we look at three complementary ways in which leaders can understand the market for data science skills across the new economy monitoring the demand for data science skills through job posting analysis from Burning Glass Technologies; the distribution and quality of data science 05A new race for talent in the Fourth Industrial RevolutionKey Findingstalent across industries and regions based on learner skills insights from Coursera; and analysing the rising prence of data science skills within the core composition of selected roles through user profile analysis from LinkedIn. Finally, we conclude with a look towards the future demand for data science skills across industries, drawing from the insights of cutives of the largest companies in the world surveyed through the World Economic Forum’s Future of Jobs Survey.Key insights1. While data science roles and skills a relatively small part of the workforce, recent trends indicate that these are currently among the highest in-demand roles in the labour market.2. The demand for data science skills is not limited to the Ination Technology sector as data’s importance grows across multiple sectors, including Media and Entertainment, Financial Services and Professional Services.3. Data science skills are particularly critical to a distinctive set of growing roles. For example, in the United States those roles are Machine Learning Engineers and Data Science Specialists. These skills are only nominally in demand across more traditional roles such as Relationship Consultants, but those roles are also facing major churn in skills.4. The data science skillset is not fixed and is rapidly evolving as new opportunities in data analysis and further technological advances redefine the specific skills composition of data scientist roles.5. The disparities in achievement of data science learners point to varying levels of data science talent across industries and economiesa. The Ination Communication and Technology ICT, Media and Entertainment, Financial Services and Professional Services industries are currently taking the lead both in hiring data science talent and in the achievements of online learners who are actively updating their skillsets across industries.b. Across most industries, online learners based in Europe demonstrate higher proficiency in data science skills than in North America, followed by emerging regions. Exceptions to such trends exist in sectors such as Telecommunications and Technology, where learners in the Asia Pacific region and the Middle East and Africa outper regional averages across industries.6. Jobs such as Artificial Intelligence and Machine Learning Specialists or Data Scientists, in which data science skills are perhaps most profoundly applicable, are forecasted to be among the most in-demand roles across most industries by 2022.Implications for decision-makersOverall, the rapid growth and evolution of data science roles and skills stresses the need for appropriate business strategies and education and training policies that can match this demand, in quantity and quality, so that skills shortages do not hinder the transation potential unveiled by vast sources of data and improved data analysis techniques. Industries and countries that fail to understand and address these dynamics risk slower growth and dynamism.More precisely, the insights included in this Report point to the following implications In the Fourth Industrial Revolution all sectors will need to undergo a fundamental transation to fully absorb the potential dividends of the data economy. Such transations will need to be accompanied by appropriate talent investments in data science skills. Industries that have been able to capture a large share of high-skilled talent in more traditional data science skills such as statistics or data management cannot be complacent, and they need to make fresh investments in newer skills, such as data visualization or statistical programming, if they wish to meet their innovation potential. A one-shot investment in reskilling will not be sufficient. Given the rapid pace of change within data science professions, maintaining skillset relevance will require responsive and dynamic upskilling systems that respond to fast-changing technologies and associated skills demand. Differences in achievements of online learners across industries and regions showcase potential data science skills capacity gaps which, if left unaddressed, may reduce the innovation and competitiveness potential of specific businesses, industries and regions. Public and private sector stakeholders in these regions will need to consider greater investment in data science. Given the rising demand for such skills, this investment is likely to generate significant returns for individuals and companies and contribute to generating new pathways for socio-economic mobility.The sections that follow present three new metric scorecards that individually and collectively shed new light on data science roles and skills in the labour market of the Fourth Industrial Revolution. The collection provides one starting point to what could be further efforts aimed at tracking skills demand and capacity across emerging sectors such as renewable energy and the care economy. This rcise can set the foundations to analyse skills dynamics in other sectors, building on the potential of multi-source data collaboration to create coherent frames of analysis and common taxonomies that can provide business and policy leaders with a common frame of reference. Finally, this Report presents a forecast on the importance of data analysis jobs across multiple industries.06 Data Science in the New Economy Scorecard 1 Emerging Demand for Data Science Skills Across Industries07A new race for talent in the Fourth Industrial RevolutionOnline Markets and Data skillsets, five-year trend, by industryDemand for Data skills, percent*Aviation, Travel linear regression within Statistics; programming languages such as R within Statistical Programming; neural networks within Machine learning; Hadoop within Data Management; and charting within Data Visualization.Among these six clusters, three have introduced significant innovation in recent years Statistical Programming, Data Visualization and Machine learning.Overall, the Technology industry, which is at the forefront of the Fourth Industrial Revolution, shows the most competitive talent base in Data Science skills, both at the aggregate level and across skills clusters. Two industries follow Professional Services and the Telecommunications industries.Industries that have pered well in more traditional data skills, such as statistics or data management, cannot be complacent and need to make fresh investments in more innovative data skills, such as data visualization or statistical programming, if they wish to fulfil their innovation potential. The Finance industry, for example, falls under this category. Historically, it has maintained strong talent bases in quantitative skills and, especially statistics. Learner achievements indicate that the industry continues to be competitive in these traditional areas of expertise despite increasing competition for talent from the Technology industry. However, the achievements of Finance industry learners showcase lower skills proficiency across other skills such as Statistical Programming and Machine Learning where the industry ranks 5th and 6th, respectively.The skills proficiency of learners varies by region. For example, in the Finance industry, learners across Europe are assessed as being at the cutting-edge of talent, while in Latin America they are considered emerging.On average, online learners based in Europe demonstrate higher proficiency in Data Science skills than in North America, followed by emerging regions. For example, the Telecommunications and Technology sectors learners exhibit comparatively strong skills proficiency in the Asia Pacific and Middle East and Africa regions. Similarly, learners in the Middle East and Africa in the Automotive industries demonstrate cutting-edge data science skills.When comparing skills achievements across industries and regions, some results are highly polarized. For example, in the Europea