Digital skills are crucial for both education and the labour market in Europe, equipping individuals to navigate in an increasingly digitalized economy. The Digital Competence Framework (DigComp) provides a clear reference for essential competencies, like information literacy, communication, content creation, cybersecurity, and problem-solving. In education, digital proficiency enhances learning outcomes, fosters innovation, and improves employability, while in the labour market it drives productivity, facilitates access to digital professions, and support adaptation to digital advancements. The EU’s digital upskilling initiatives aim to bridge skill gaps, ensuring inclusion and competitiveness in an evolving digital landscape.

The EU’s 2030 Digital Compass aims for 80% of adults to attain at least basic digital skills. However, Eurostat indicates slow progress: by 2023, 56% of EU citizens (aged 16-74) had at least basic digital skills, with only marginal growth from previous years. Despite significant investment in education, upskilling and reskilling — often guided by DigComp as reference — the risk of falling short of goals remains evident.

The review of the literature reveals numerous contributions focused on educational initiatives aimed at improving the digital literacy across various contexts — including all levels of formal education and life-long learning or adult learning. However, digitally driven analytical approaches remain scarce. Existing studies tend to overlook advanced methods such as text mining, big data analysis using open labour market datasets and semiautomated extraction of insights from reference documents. This creates a controversial scenario: experts in digital skills who are not fully leveraging them to scale-up and optimize their research and analysis.

Moreover, a significant limitation is that key standards and model references are not published in machine readable format but remain confined to rigid, traditional formats. This restriction hinders the development of expert-driven automated analysis initiatives, even when documents can be processed by LLMs (Large Language Models) to extract insights. Publishing the references in more structured, machine-readable formats would enable advanced analytical approaches, enhance practical applications and support the development of complementary resources for practitioners.

Effectively analysing digital skills requires advanced digital competencies. Data analysis and text mining extract insights from large datasets, while machine learning enables automated assessments of skills gaps and trends. Information literacy ensures accurate evaluation of digital resources, and Natural Language Processing (NLP) plays a crucial role in analysing textual data, such as policy documents and competency frameworks like DigComp. Additionally, data visualization aids in presenting findings effectively.

This paper presents an overview of digital methods enhancing the analysis and application of the DigComp framework for digital skills training and assessment. It highlights the advantages of data-driven approaches, including and big data analysis, to extract actionable insights. The study incorporates practical examples: use of open datasets accumulating over 8 million job ads from the EU tool OVATE, advanced queries in the ESCO database covering more than 3000 occupations and text analysis of 260 examples and 365 statements from the DigComp 2.2.

Keywords: DigComp framework, digital skills, digital competence, big data, digital skills analysis.