Whenever a new digital tool is released, people tend to look at the time needed to reach one million users. When Twitter launched in 2006, this was two years. Dropbox needed seven months while it took only two and a half months for Instagram. ChatGPT reached this milestone in just five days.
The rise of digital tools, such as chat-interfaced LLMs, spreading across the world, not only in personal settings but also at the workplace, raises an important question:
How can anyone keep up with technological advancements as developments are happening ever more rapidly?
Especially in the European industry, where increasing costs of resources (including energy) put pressure on both incumbent players and rising startups, there is a need for companies to become more data-driven in order to stay globally competitive. However, this trend is not just limited to the industrial sector. Policy-makers and the public at large also stand to gain from becoming knowledgeable about data and algorithms. AI is based on data and in any machine learning pipeline, the majority of time is spent on preparing the data. The basis for understanding and using AI systems successfully thus lies in achieving data literacy.
But how can the aforementioned stakeholder groups become more data-driven, when talent is scarce and the willingness to adapt to new technologies low?
On a strategic level, the answer is to foster a data culture within organizations. As the term suggests, it requires a cultural change, accompanied by change management. On the other hand, people need to acquire technological skills to the extent that their current or prospective role requires them. Data culture is hereby a means to achieve data literacy.
To support the cultural shift, the AIDA model from marketing research can be a helpful framework. Originally developed to describe the phases a customer passes through before making a purchase, AIDA (Attention, Interest, Desire, Action) can also be applied to achieve buy-in for reskilling initiatives.
First, awareness must be raised about the urgency and benefits of learning data skills. This can be achieved through the communication of successful and relatable examples of data-driven applications. These examples can spark an interest in data topics, as individuals begin to see the relevance and potential impact. This interest may then lead to the desire to learn and keep up-to-date with these trends, especially if individuals are motivated to contribute to similar success stories.
Finally, with the support of organizational incentives, such as opportunities for on-the-job training, people are more likely to take action by participating in reskilling programs focused on data literacy.
When looking at concrete data skills, there are two pathways to achieve data literacy depending on the organization’s industry and the current skill set of its members. Upskilling refers to the augmentation of existing roles to improve efficiency and/or effectiveness, therefore increasing productivity. This can be achieved through modular short-cycle courses and learning formats which flexibly adapt to the needs and progress of the learners.
When traditional job roles receive major changes or even become obsolete, upskilling alone is not enough. Early exposure to new projects and a learning roadmap for transitioning into possible new roles is crucial to take these people along. In general, the larger the change in role and responsibilities, the earlier and more accompanied this reskilling effort needs to be.
Outside of organizations, the promotion of life-long learning in informal settings requires a concerted effort by governments and education providers, going beyond the traditional school and university systems. People need to be provided with offers that reach them where they are, in a format which fits their individual life designs.
The responsibility for creating a data culture lies not just within industry, with NGOs or other similar organizations, but also with national governments and EU institutions. They should take measures to drive this change, such as promoting the dialogue between organizations requiring reskilling and institutions providing education. This might happen through tailored platforms and public-private partnerships, which create accessible formats for knowledge exchange and reflection, integrated into the places where people work and live. Additionally, financial incentives for organizations to invest more in upskilling and/or reskilling their workforce (e.g. reskilling training budgets and paid-time-off for upskilling) can accelerate this change.
Instilling a data culture into every European citizen, whether they are participating in the workforce or not, is crucial for ensuring the competitiveness of Europe in a rapidly changing world. Besides keeping up with technological advancements and supporting the willingness to learn, achieving data literacy helps organizations comply with the EU AI Act (ref. Art. 4 on AI literacy), increasing the speed for introducing new tools into their organization as well as create resilience and adaptability within their populations.
Author: Felix Rank, Young Leader at United Europe e.V.
Felix Rank was part of the United Europe Mentoring Program 2024/25: “The mentoring program by United Europe gave me not only valuable insights into the learnings of a manager from my industry, but also the opportunity to reflect on topics surrounding my career decisions. The regular meetings helped me identify new angles to my questions in a trusted setting. I am grateful for this experience and can only recommend to apply for the next round of the program.”