The demand for knowledge of artificial intelligence (AI), data science and business analytics is greater than ever, both at universities and in the business world. At every Dutch university, the programs on these knowledge fields have set a maximum on student enrolment , and since 2016 the number of AI-related job openings has grown by 207 percent. Therefore companies that need personnel with these skills experience problems. And yet there is hope for these companies, since there is plenty of hidden talent in the workplace.
Robert Monné, Manager at The Analytics Academy
It is a good thing that the importance of AI and data science has gained widespread recognition. It has a positive impact on our society, and it offers competitive advantages for businesses. The research and consultancy bureau Gartner predicted last year that by 2021, around 30 percent of the growth in income for market-specific digital solutions will be due to AI technology. So there is good reason for companies to keep trying to recruit from the limited pool of data scientists. Universities are wrestling with enrolment caps, and Europe will have to make sail to close the AI knowledge gap with the rest of the world.
As a company that needs data science knowledge, are you therefore left to your own devices? Fortunately, no. When we train organizations in the latest data science skills, we always encounter people in-house who have untapped talent for the field. By teaching these talented individuals the right fundamentals by offering training courses, they can develop into a role that can make life easier (and therefore more effective) for the data science (or AI) specialist. This talent can be found among analysts, business translators, or even ‘citizen data scientists’: personnel without a data science background, but who can conduct useful analyses when given the right software and access to data. These analyses generate immediate value in the operations or on the “factory or shop floor”. Gartner expects that these ‘citizen data scientists’ will produce a larger quantity of analyses than the specialists themselves by as early as 2019.
By assigning employees to these related roles internally, an organization can build data analysis capabilities efficiently, while reducing its dependence on the tight data science job market. Another side benefit of this approach is that it gives current employees new opportunities for personal development, which results in higher employee satisfaction. So the question is: how can you retrain your personnel, considering the shortage of instructors at traditional training institutes? Fortunately, there are a wide range of alternatives, including: hands-on professional/executive studies, analysis tooling courses, Massive Open Online Courses (MOOCs), self-study, or internal training courses.
Organizations that aim to become more data-driven can find the ideal solution in a mix of these forms of training. In addition, combining theory and practice is crucial in order to apply the knowledge gained into useful skills. The best way to do that is through a broad internal program, where multiple layers of the organization (from analysts up to board members) can develop the necessary skills. Doing so embeds data-related knowledge in the organization, and boosts support for and understanding of new data-driven initiatives.
In the end, however, you will still need fully-qualified data scientists for the more complex (strategic) analyses. Together with the internally trained data science enthusiasts and employees, they can often find the most valuable data-related opportunities for the organization. In addition, the employees can complement the data scientist’s technical insight with their knowledge of the specific market in which they operate. But they can also take on the simpler analytical challenges on an ad-hoc basis, freeing the data scientist to concentrate on the more challenging projects. By working together, they can make the most efficient use of both their unique knowledge and skills.
Such an organization-wide approach to addressing the need for data-related knowledge also helps to rapidly build enthusiasm for data science throughout the organization. It makes the process actually fun for employees; they develop new skills, use data to justify their decisions, and gain more satisfaction from their work. It also frequently unleashes a flood of new business questions for the data scientists to apply their advanced analyses. And that’s what organizations really need in the end: finding data-driven solutions to all of their business challenges.