Report

Data science in the new economy: a new race for talent in the Fourth Industrial Revolution

Skilled workforce Labour market Fourth Industrial Revolution Big data
Description

Through 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 information 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 information 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 platforms 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 populations—and difficult to compare and contrast—when coupled with traditional and qualitative sources of data, they can help businesses, policymakers and workers have greater analytic capacity about the present and future of work and adopt better informed and coordinated business strategies and policies.

In this report, the authors 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 talent across industries and regions based on learner skills insights from Coursera; and analysing the rising prevalence of data science skills within the core composition of selected roles through user profile analysis from LinkedIn. Finally, the authors conclude with a look towards the future demand for data science skills across industries, drawing from the insights of executives of the largest companies in the world surveyed through the World Economic Forum’s Future of Jobs Survey.

Key insights

  1. While data science roles and skills form 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 Information 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 economies: a) The Information 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 outperform 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 indemand roles across most industries by 2022.

Implications for decision-makers

Overall, 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 transformation 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 transformation to fully absorb the potential dividends of the data economy. Such transformations 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 exercise 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.

Publication Details
Publication Year:
2019