By Apostolos Kostas | October 2025
Across Europe, the call for data-literate schools has grown louder. Policymakers, researchers, and educators are recognizing that the ability to understand, interpret, and act upon data is not just a digital skill, it is a civic and professional necessity. Two major EU-funded projects released in 2025, DATA-READY (D2.1) and EVIDALI (D3.1), shed light on how data literacy is being conceptualized, implemented, and measured within compulsory education. Though both reports share a common goal (to strengthen data-informed practice in schools) they approach the challenge from distinct angles. DATA-READY maps strategies and policy frameworks for integrating data literacy into curricula, while EVIDALI dives into the research evidence and measurement tools that underpin teacher data literacy. Comparing the two offers a panoramic view of Europe’s evolving data-literacy landscape.
From Policy Visions to Classroom Practice: Different Starting Points
The DATA-READY D2.1 report takes a systemic and policy-oriented perspective. It surveys European curricula, national strategies, and teacher-training initiatives, identifying how (and whether) data literacy appears in compulsory education. The findings are sobering: while many systems embed elements of digital and statistical literacy, few treat data literacy as a coherent, cross-curricular competence. The report calls for a shared conceptual framework, stronger policy coordination, and robust teacher professional development.
In contrast, EVIDALI D3.1 begins inside the classroom and inside the research literature. It systematically reviews 76 quantitative studies on Data Literacy for Teaching (DLFT) and maps the theoretical and empirical tools used to measure teachers’ competence in data use. The focus is on how teachers engage with data to improve learning, what kinds of data they use, and how those practices are studied and assessed. The report thus shifts attention from the policy architecture to the practical and measurable realities of data use in schools.
DATA-READY asks “What do education systems say about data literacy?” while EVIDALI asks “What do teachers actually do with data, and how do we know?”
Conceptual Convergence: Defining ‘Data Literacy’ (Still a Work in Progress)
Both reports confirm a fundamental problem: no shared, operational definition of “data literacy” currently exists in compulsory education.
DATA-READY reveals conceptual fragmentation across Europe, data literacy is often subsumed under “digital competence” or “information literacy,” without clear progression models or assessment methods. EVIDALI, through its literature synthesis, shows that even in empirical studies, the term “data” is inconsistently defined, only 28.9% of reviewed papers provided an explicit definition.
Where definitions do appear, both projects align around a multidimensional understanding of data literacy that includes knowledge, skills, attitudes, and contextual awareness. Both also highlight the emerging role of AI literacy, expanding the notion of data competence to include the ethical and pedagogical implications of machine learning and algorithmic systems in schools.
Complementary Perspectives: Systemic Strategies vs. Empirical Foundations
Together, the two reports form two halves of the same puzzle: one defines the strategic architecture of data literacy, and the other provides the empirical building blocks to measure and operationalize it.
A critical point of intersection lies in their treatment of teachers. DATA-READY identifies teachers as both beneficiaries and enablers of data literacy reform but notes that professional development is inconsistent and often peripheral. EVIDALI, conversely, foregrounds teachers as research subjects and agents of change, examining their knowledge, motivation, and the conditions under which they use data effectively. EVIDALI’s identification of seven empirical dimensions, ranging from data skills and decision-making to professional development and accountability, adds granularity to the human side of data literacy. Yet, as DATA-READY warns, these individual capacities can only thrive within supportive institutional ecosystems: policies, leadership, and resources.
Emerging Priorities: AI Literacy, Ethics, and Systemic Coherence
Both reports converge on one clear future direction, AI literacy as a new pillar of data literacy.
EVIDALI explicitly integrates AI literacy frameworks (e.g., Long & Magerko 2020; Chiu et al. 2024) into its analysis, arguing that the ability to interpret and responsibly use AI-generated data is now a core professional skill for educators.
DATA-READY, meanwhile, frames AI as a contextual challenge for curriculum design and educational equity.
Yet, both warn of a widening competence gap: schools are collecting more digital data than teachers are prepared to interpret. Bridging this gap requires policy coherence (DATA-READY) and empirical grounding in practice (EVIDALI).
Critical Reflections: Where Europe Stands
Taken together, the two reports reveal both progress and fragmentation in Europe’s data-literacy ecosystem.
Strengths:
- Growing recognition of data literacy as essential to 21st-century education.
- Emerging integration of AI and digital dimensions.
- Increasing alignment between policy initiatives and research evidence.
Weaknesses:
- Persistent conceptual ambiguity and lack of standardized frameworks.
- Uneven teacher professional development and systemic support.
- Limited instruments for assessing real data-literacy competence rather than self-perceptions.
In essence, Europe has made strides toward data-aware education, but not yet toward data-ready schools.
From Parallel Tracks to Integrated Action
The most productive way forward is not to treat DATA-READY and EVIDALI as separate initiatives, but as complementary phases of a common strategy:
1. DATA-READY defines the ‘why’, the strategic need and policy vision for embedding data literacy across education systems.
2. EVIDALI defines the ‘how’, the empirical evidence and measurement instruments that can turn those policies into practice.
The synergy between the two offers a roadmap for a European Data Literacy Framework for Schools—one that connects policy, practice, and measurement through a shared understanding of what it means to be data-literate in the age of AI.
Conclusion: Building a Data-Ready, Evidence-Informed Future
Both DATA-READY and EVIDALI demonstrate that data literacy is no longer a niche competence, it is central to educational improvement, equity, and democratic participation. Yet Europe’s challenge lies not only in defining data literacy but in operationalizing it across every classroom, teacher-training program, and education policy.
If DATA-READY shows where systems should go, and EVIDALI shows how teachers might get there, then the next step is to build the bridge between them: evidence-informed policy for evidence-informed teaching.