Whether you’re hiring or applying for new roles in emerging technologies, it’s important to know where tech is headed and how companies are adapting hiring and skilling strategies.

We’ve culled a few insights from the World Economic Forum’s The Future of Jobs Report to get you up-to-speed about the tech jobs of tomorrow. This article on big data is the final in a four-part series, with previous editions on blockchain, machine learning and cloud computing.

The WEF’s The Future of Jobs Report pinpoints the widespread adoption of big data analytics as one of four specific technological advances “set to dominate the 2018-2022 period as drivers positively affecting business growth,” along with high-speed mobile internet, artificial intelligence and cloud technology.

According to the report, 85% of all companies are likely or very likely to expand their adoption of user and entity big data analytics by 2022, making this technology category the leader in terms of “emerging” technology adoption.

It’s also important to note that the intersection of machine learning, artificial intelligence and big data will drive a significant shift in how work is accomplished, with more tasks automated than ever before as the efficacy and availability of data increases.

For example, it’s estimated that 62% of information and data processing and information search and transmission tasks will be performed by machines by 2022, compared to 46% today.

This shift in the nature of work will drive key skills gaps across industries. According to The Future of Jobs report, between 2018-2022, “all industries expect sizable skills gaps, and more than 50% of the workforce will need at least some level of reskilling.”

While the data scientist role topped LinkedIn’s list of most promising jobs, the company also estimated that more than 151,717 data science jobs are going unfilled in the United States alone.

The impact of this shortage already keeps CIOs up at night — and according to a recent KPMG report, big data and analytics lead the list of roles suffering from a skills shortage. 

A majority of companies in nearly every industry have adopted or are expected to adopt big data analytic technology by 2022, with information and communication technologies (93%), aviation, travel and tourism (89%) and global health and healthcare (87%) expecting to adopt it at the highest rates.

Given big data’s level of popularity and the demand for data roles, companies in these industries and others can expect fierce competition for qualified individuals — and should be planning well into the future to help mitigate against the damaging effect of skills shortages.

But there is plenty of reason for optimism. As the workforce undergoes major reskilling efforts to account for key skills gaps caused by the Fourth Industrial Revolution, the WEF’s The Future of Jobs Report states that up to 75 million jobs may be displaced in the move to machine-based tasks. However, 133 million new roles are predicted to emerge as the division of labor between humans, machines and algorithms evolves.

As big data analytics adoption continues to grow, data analysts and data scientists are needed to support the practical applications of machine learning and artificial intelligence. And by doing so, turning large data sets into actionable insights that solve new business challenges. 

From a jobs standpoint, these roles will be highly in-demand across most industries, as will a new role called “big data specialists.” While not fully defined by the WEF, this role is defined as “related to understanding and leveraging the latest emerging technologies,” and also includes AI and machine learning specialists, process automation experts, user experience, human-machine interaction design and others.  

As should be expected, database and network specialists will also continue to be in demand to support the adoption of big data analytics and manage the computing power needed to do so.

Ensuring that workforce gaps don’t get in the way of big data initiatives requires immediate and ongoing attention, since competition for trained big data specialists is fierce. When defining your big data initiatives, here are three industry-specific considerations to keep in mind:

For information and communications technology companies, skills gaps in local labor markets is one of the main barriers to adoption of emerging technologies like big data analytics. To help overcome this issue, 57% of companies in this industry will look to expand task-specialized contractors and other tactics to meet the immediate needs for talent.

Machines will also play a role in filling these gaps, allowing humans to do mission-critical strategic work. 

The share of task hours machines spend on administrative activities versus human activities will grow from 39% to 57% as the need for data entry clerks, administrative support, user support, accounting, bookkeeping and similar roles declines. Even with complex and technical activities, machines take on an increased share of the workload, from 25% in 2018 to 46% in 2022.

Among the three industries mentioned in this article, the aviation, travel and tourism industries face the largest reskilling challenges, with 68% of companies’ workforces needing some form of training to adapt to big data and other emerging technologies, with 18% needing more than a year of training and reskilling.

With global tourism forecast to exceed $1.8 billion by 2030, organizations able to sort through the promise of big data for these markets, and develop strong training and hiring programs, stand to gain a major competitive advantage over those who take a more traditional or cautious approach.

Nearly 80% of global health and healthcare organizations report “lack of understanding of the opportunities” as the biggest barrier to adoption of new technology, more than any other sector in the WEF report. This signals a need to modernize and effectively compete with promising startups and technology-first health companies.

According to Health IT Analytics, as health organizations achieve greater data maturity, big data and predictive analytics can be used to develop risk-based scoring to determine when individuals might benefit from additional treatment or intervention, improve data-driven supply chain management, advance precision medicine and augment data security, among other initiatives. 

Whether a traditional healthcare company can take advantage of opportunities like this relies entirely on its ability to adopt and deploy foundational data technology effectively.

As a key input to machine and AI-driven tasks, big data analytics is obviously expected to play a large role in evolving traditional job roles and sparking new business growth. When considering how ubiquitous big data and data science are already as business functions, it’s fair to wonder whether big data should even be classified as an “emerging” technology — or whether it’s a must for any business hoping to compete now or tomorrow.