Blog
Over the last decade we have seen how national and international institutions, as well as national governments and business associations themselves have been warning about the shortage of technological profiles and the threat this poses to innovation and growth. This is not an exclusively European problem - and therefore also affects Spain – but, to a greater or lesser extent, it occurs all over the world, and has been further aggravated by the recent pandemic.
Anyone who has been working for some time, and not necessarily in the technology world, has been able to observe how the demand for technology-related roles has been increasing. It's nothing more than the consequence of companies around the world investing heavily in digitization to improve their operations and innovate their products, along with the growing presence of technology in all aspects of our lives.
And within technology professionals, during the last few years there is a group that has become a kind of unicorn due to its particular scarcity, data scientists and the rest of professionals related to data and artificial intelligence: data engineers, machine learning engineers, artificial engineering specialists in all areas, from data governance to the very configuration and deployment of deep learning models, etc.
This scenario is especially problematic for Spain where salaries are less competitive than in other countries in our environment and where, for starters, the proportion of IT workers is below the EU average. Therefore, it is foreseeable that Spanish companies and public administrations, which are also implementing projects of this type, will face increasing difficulties in recruiting and retaining talent related to technology in general, and data and artificial intelligence in particular.
When there is a supply problem, the only sustainable solution in the medium and long term is to increase the production of what is in short supply. In this case, the solution would be to incorporate new professionals into the labour market as the only mechanism to ensure a better balance between supply and demand. And this is recognized in all national and European strategies and plans related to digitalization, artificial intelligence and the reform of education systems, both higher education and vocational training.
Spanish Strategies
The National Artificial Intelligence Strategy dedicates one of its axes to the promotion of the development of digital skills with the aim of putting in place all the means to ensure that workers have an adequate mastery of digital skills and capabilities to understand and develop Artificial Intelligence technologies and applications. The Spanish government has planned a wide range of education and training policies whose basis is the National Digital Skills Plan, published in January 2021 and aligned with the Digital Agenda 2025.
This plan includes data analytics and artificial intelligence as cutting-edge technological areas within specialized digital skills, that is, "necessary to meet the labor demand for specialists in digital technologies: people who work directly in the design, implementation, operation and/or maintenance of digital systems".
In general, the national strategy presents policy actions on education and digital skills for the entire population throughout their lives. Although in many cases these measures are still in the planning phase and will see a major boost with the deployment of NextGenerationEU funds, we already have some pioneering examples such as the training and employment guidance programs for unemployed and young people tendered last year and recently awarded. In the case of training for unemployed people, actions such as the Actualízate program and the training project for the acquisition of skills for the digital economy are already underway. The actions awarded that are aimed at young people are scheduled to start in the first quarter of 2022. In both cases the objective is to provide free training actions aimed at the acquisition and improvement of ICT skills, personal skills and employability, in the field of transformation and the digital economy, as well as orientation and job placement. Among these ICT skills, those related to data and artificial intelligence will undoubtedly have an important weight in the training programs.
The role of universities
On the other hand, universities around the world, and of course Spanish universities, have been adapting curricula and creating new training programs related to data and artificial intelligence for some time now. The first to adapt to the demand was postgraduate training, which, within the higher education system, is the most flexible and quickest to implement. The first batch of professionals with specific training in data and artificial intelligence came from diverse disciplines. As a result, among the veterans of corporate data teams we can find different STEM disciplines, from mathematics and physics to virtually any engineering. In general, what these pioneers had in common was to have taken Masters in Big Data, data science, data analytics, etc. complemented with non-regulated training through MOOCs.
Currently, the first professionals who have completed the first degrees in data science or data engineering, which were reformed by the pioneering universities - but which are now already implemented in many Spanish universities - are beginning to reach the labor market. These professionals have a very high degree of adaptation to the current needs of the labor market, so they are in great demand among companies.
For universities, the main pending challenge is for university curricula in any discipline to include knowledge to work with data and to understand how data supports decision making. This will be vital to support the EU target of 70% of adults having basic digital skills by 2025.
Large technology companies developing talent
An idea of the size of the problem posed by the shortage of these skills for the global economy is the involvement of technology giants such as Google, Amazon or Microsoft in its solution. In recent years we have seen how practically all of them have launched large-scale free materials and programs to certify personnel in different areas of technology, because they see it as a threat to their own growth, even though they are not exactly the ones having the greatest difficulty in recruiting the scarce existing talent. Their vision is that if the rest of the companies are not able to keep up with the pace of digitalization this will cause their own growth to suffer and that is why they invest heavily in certification programs beyond their own technologies, such as Google's IT Support Professional Certificate or AWS's Specialized Program: Modern Application Development with Python.
Other multinational companies are addressing the talent shortage by retraining their employees in analytics and artificial intelligence skills. They are following different strategies to do this, such as incentivizing their employees to take MOOCs or creating tailored training plans with specialized providers in the education sector. In some cases, employees in non-data related roles are also encouraged to participate in data science training, such as data visualization or data analytics.
Although it will take time to see their effects due to the high inertia of all these measures, they are certainly going in the right direction to improve the competitiveness of companies that need to keep up with the high global pace of innovation surrounding artificial intelligence and everything related to data. For their part, professionals who know how to adapt to this demand will experience a sweet moment in the coming years and will be able to choose which projects to commit to without worrying about the difficulties that, unfortunately, affect employment in other areas of knowledge and sectors of activity.
Content prepared by Jose Luis Marín, Senior Consultant in Data, Strategy, Innovation & Digitalization.
The contents and views reflected in this publication are the sole responsibility of the author.
Blog
The increasing concern of European authorities for the management of digital skills among the working population, especially young people, is a growing reality whose management does not seem to have an easy solution.
“The internet and digital technologies are transforming our world. But existing barriers online mean citizens miss out on goods and services, internet companies and start-ups have their horizons limited, and businesses and governments cannot fully benefit from digital tools.” This strong affirmation opens the European Commission (EC) web section dedicated to digital single market. The digital single market is one of the strategic priorities of the EC and, within it, one of the action lines is the development of digital skills among the employed population of Europe - especially young people.
When classifying digital skills, we have the same problem as classifying emerging technologies. Most efforts in this area focus on the establishment of a hierarchical classification of skills / technologies, which rarely go deeper than two or three levels.
The EC establishes a classification of digital competences in 5 categories (always in the Internet domain):
- Information processing
- Content creation
- Communication
- Problem resolution
- Security
In each of these categories, the Commission proposes a framework whit three levels of user competence (basic, independent, expert). For each level of competence, standard statements are proposed to help users to perform a self-assessment to establish their digital competence level.
Now, the question that arises in various forums is how to take a step beyond the simple self-assessment of competencies. Questions as “how to perform a search for terms related to digital skills?” are not trivial to answer.
When the skills classification has one or two levels of depth, it is enough to perform a "literal search" of the term to be searched. But what happens if the term tree related to digital skills has thousands of terms hierarchically organized?
For example, imagine that a company needs a very specific profile for a new position in an R&D project. The required profile is a professional with advanced knowledge in MLib Apache Spark library and more than 2 years of experience in streaming Big Data. In addition to these skills - called hard skills - the professional needs to have a series of social skills or soft skills such as public communication ability and teamwork.
How can we find such a profile in a database of 400,000 employees around the world?
A possible solution to these and other issues may be the creation of a digital skills ontology in Europe.
An ontology provides a hierarchical organization of terms (taxonomy) and a set of relationships between them, which facilitates the search - both literal and inferred - of complex terms and expressions. This is already very useful by itself, but if you also combine the formal structure of an ontology with its technical implementation, using a technological tool, you get a powerful technological product. A technical implementation of this ontology would allow, among others, perform the following complex search in an efficient and unambiguous way:
Find a person WITH MLib technical skills that also HAS more than 2 years of experience in streaming Big Data and also HAS the soft skills of teamwork and communication skills at the intermediate level.
With an underlying ontology, in the previous example, all the underlined terms would have a unique identifier, as well as their relations (uppercase). The semantic search engine would be able to identify the previous query, extract the key terms, understand the relationships (WITH, HAS, more, etc.) and execute a search against the employees database, extracting those results that fit with the search.
A good example of using an ontology to perform complex searches in immense databases is SNOMED-CT. It is a standard vocabulary to search clinical terms in patient databases. The clinical domain is especially indicated for the development of ontologies due to the complex structure inherent to the clinical terms and their relationships.
Although there are classic tools and methods of organizing information based on traditional databases and relational models, ontologies and their technological implementations offer higher flexibility, scalability and level of personalization to different subfields.
Precisely the characteristics of flexibility and high scalability become fundamental as open data repositories become increasingly bigger and diverse. The European Open Data portal contains more than 12,000 datasets classified by topics. For its part, the website specializing in data science, Kaggle, hosts 9000 datasets and, annually, they organize competitions to reward those professionals who best analyse and extract useful information from these data. In short, the volume of data available to society is increasing year after year and ontologies are importance as a powerful tool for managing information hidden under that blanket of raw data.
Content prepared by Alejandro Alija, expert in Digital Transformation and innovation.
Contents and points of view expressed in this publication are the exclusive responsibility of its author.