By Lawrence Ng and Dwaine Plauche

Companies need to attract new talent and retain existing talent, especially in capital-intensive and process industries, such as the oil and gas sector, where the competition for human talent is relentless. Companies that deploy innovative technologies, such as artificial intelligence and machine learning, can attract the best talent to take on the forefront of this intensifying industrial sector and for new employees, to be productive faster and better.

Driven by Industry 4.0, industrial organizations face shifting market demands, new technologies, and difficulty in managing the growing volume of data. Post-pandemic, companies are now facing an immediate labour crunch, exacerbated by the ongoing talent shortage in the capital-intensive industry due to retiring veterans and newly graduated incumbents. Combined, these factors have now created a perfect storm of generational changes in the industrial sector. These changes are challenges but also golden opportunities for our industry to make an urgent and, frankly, overdue change in our approach to people. It is time to cultivate and support a new kind of IT workforce, built around a new breed of tech-savvy domain experts and industrial data scientists.

Industrial workforce headwinds

How do companies develop a new generation of industrial IT workers? This creates a necessity to recruit and retain greater numbers of industrial data scientists—data scientists with specialized domain expertise in manufacturing backgrounds. As our industry builds this new generation of domain-expert data scientists, key challenges remain for companies to resolve. 

To be competitive, companies need to deal with the overall labour shortage situation. Industry labour shortages extend beyond the Great Resignation. For years, there has been a shortage of IT talent. According to a survey by the National Center for Science and Engineering Statistics (NECES) in the US, despite a 98% increase in the number of computer-related grad students over the past decade, supply cannot keep up with the demand. Growing data volumes and increased adoption of industrial digital-transformation strategies mean organizations are constantly trying to hire new data scientists with the skills to manage these workloads. However, there are too few data scientists and engineers to meet demand, and the competition to recruit them is fierce.  

Besides labour shortage, there is a talent shortage or skills gaps situation. Beyond recruiting and retention, veteran industrial workers are hitting retirement age, taking with them decades of historical industry knowledge and domain expertise. This is fuelling a growing knowledge gap in the industrial sector. It is important that new recruits are set up for success right out of the gate but doing so means bridging the gap between the skills and knowledge that those recruits bring to the table, and the skills needed to successfully execute their jobs.

Finally, the next generation of data scientists will have high expectations for the technologies and digital solutions deployed by their potential employers. If industrial organizations are behind the curve in their adoption of AI, automation, and other Industry 4.0 technologies that make jobs easier to perform and add value, then potential job applicants are going to look elsewhere. Indeed, the challenges outlined above can be resolved in part, by digitally transforming and modernizing technologies and processes. In particular, that means adopting new ways of managing, processing, and acting upon their enormous volumes of industrial data. However, technology alone only gets us part of the way there—it cannot single-handedly plug the talent gap and meet the expectations of a new generation of industrial workers and data scientists. 

Building the next generation workforce

More than just digitally transforming, we need to develop new ways of working aimed at fostering collaboration across organizational silos, developing novel organizational structures and meeting the demands of a more digitally driven industry. In the area of increasing collaboration and eliminating silos, new technologies – such as the next generation data historians, help to standardize the formatting and management of access to industrial data. Using digital solutions to build across-the-board access to the organization’s data, instead of silo work by an individual or team basis, helps facilitate cross-team collaboration and eliminates the barriers that separate people from each other. Strive to build up your new generation of industrial IT workers together as one team, not a fragmented group of isolated individuals. 

Companies need to maintain hybrid and remote work as the permanent status quo. The pandemic spurred a worldwide shift to remote and hybrid work, and there is simply no putting that genie back in the bottle. This is partly a recruiting and retention tactic; organizations that don’t offer the opportunity to work remotely will surely lose employees and potential recruits to competitors who do. But beyond that, remote and hybrid work are going to be an important, foundational part of the team dynamics within this new industrial workforce. Not only does the remote work option empower teams to work in a more distributed manner, doing so also creates a more agile organizational structure suited for meeting cross-functional needs. 

In volatile times, industry leaders can either get ahead of trends, or lose their footing. By building specialized roles for industrial data scientists and engineers, eliminating silos that inhibit collaboration, and making hybrid and remote work a permanent part of the work culture (not to mention effective recruiting and retention strategies), industrial organizations can put themselves on the cutting edge of these trends—closing skills gaps and cultivating the next generation industrial IT workforce of the future.

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