At a time when digital technologies are transforming society, our professional and personal lives, ways of working, cooperation and communication, organizations are looking for more and more competent profiles for attracting and making digital investments. Business needs are exacerbated as the business is digitized, and these departments, in turn, need more sophisticated digital skills. Thus, the digitalization of companies is a triple problem related to skills: the inclusion of digital business in the business, the development of skills and the attractiveness for the feminization of digital technology.
For all organizations, a lack of resources is especially felt in the areas of data science, system integration, architecture, or methods. This deficit, estimated at 80,000 jobs by 2020, according to the firm’s report to the European Commission in 2017, delays projects and intensifies the “talent war” between startups, traditional companies and digital services companies. In order to better understand the evolution of digital professions and spread the usual HR practices, he updated the Nomenclature of the professions of information systems, which now refers to the European level. Enrichment and diversification of the talent pool is becoming a problem of productivity and innovation.
If the need for advanced skills is real, spreading the digital culture in companies also requires considerable effort in terms of the attendant changes. The assistance and investments that must be made in order to succeed in developing new digital tools and services for everyday life are largely underestimated, especially with regard to the digital workplace and the increasing automation of certain tasks, which leads to the transformation of transactions and interactions in business . The digital transformation of our organizations also means a change in thinking and talent-based management.
Technological innovations. The growing adoption of cloud technology in business is being implemented through SaaS solutions and growing public and private IaaS services. Cloud projects are expanding and new proposals are being developed. In full growth since 2016, the SaaS / IaaS market is concentrated in the hands of several leaders – it occupies a third of the cloud providers market – whose activity is growing at an incredible rate (up to 50% growth for the quarter). This growth brings with it an entire ecosystem around the cloud, made up of integrators and infrastructure management solution providers. The rapid growth of the cloud is a fundamental trend: a serious crisis (security breach, data leakage, service quality failure, …), restrictive regulation or model change.
With 95% of CIOs applying for cloud-based applications, the team path is clear and competition is intense. Confidence in actors and their services has become a strong point, despite the lack of reliable alternatives in France and Europe. The cloud poses several key challenges for CIOs: defining a clear strategy through business services, developing cloud technologies, ensuring security, independence and reversibility, controlling costs, optimizing resource management of software and applications, and continuing to manage interactions with an indispensable legacy.
Unlike the cloud market, the data center market has shrunk significantly by 6.5%. This is probably not a long-term trend, but rather a restructuring of the market. Operators are in a positioning crisis against, which builds and buys server capacities, but ultimately will also have to rely on existing communications players already located in data centers. Therefore, they will gradually return the business through the cloud.
The development of artificial intelligence technologies (AI) is in the context of strategic economic and political interests at the international level. Europe and France have the least ambitious goal – to catch up with the giants in this area. In addition to fantasies about a strong AI and fully automated, IT professionals perceive artificial intelligence as a way to implement three levels of strategies or projects: firstly, pure processing of probabilities and grouping of characteristics (data extraction) using the so-called controlled AI; secondly, predictive analysis and machine learning using the so-called “street” AI; and finally, data analysis with improved learning or deep learning.