From Data-Overwhelmed to Data-Fluent: Supporting Educational Decision-Making in the Digital Age

In universities from Berlin to Dar es Salaam, learning stakeholders are drowning in data while thirsting for insights. Student performance metrics, learning analytics, engagement statistics, and assessment results flood educational systems. The challenge isn’t simply finding and accessing data—the bigger challenge comes from not developing the data fluency needed to transform numbers into meaningful action while also respecting diverse cultural and regulatory perspectives on student privacy.

This coming December at OEB, my colleagues Erin Czerwinski (Carnegie Mellon University), Karen Vignare, (APLU), and I will be sharing a pre-conference workshop entitled “Developing Data Fluency for Student Success.” We will feature some of the approaches Erin and her team have developed for CMU’s acclaimed DS4EDU (Data Science for Education) program; we will also share results and “lessons learned” from student success initiatives supported by APLU (the Association for Land-Grant and Public Universities) across large colleges and universities in the United States during the past several years.

Through interactive examples and real-world exercises, attendees will learn what it takes to convert data into meaningful reports for both institutional improvement and for improving student success. Special attention will be given to common challenges in educational data analysis.

Beyond Data Literacy: The Data Fluency Imperative

Data literacy, data proficiency and data fluency each represent levels of competence and understanding beyond semantic nuance: it’s the difference between passive consumption of information and active transformation of information into action. While data literacy enables participation in data conversations, data proficiency refers to the ability to actively leverage essential data in enterprise decision-making. Data fluency empowers educators to use insights naturally and purposefully, like native speakers of a language applying their skills intuitively to achieve specific goals.

This progression matters enormously in educational contexts. A data-literate educator can read a dashboard and summarise findings in reports. A data proficient manager or director can interpret statistical reports and direct strategic initiatives based on those reports and related statistics.  A data-fluent professional can interpret patterns, identify intervention opportunities; they use data to implement targeted strategies that directly impact student success. They don’t just consume data—they breathe life into it.

During the May 2025 eLearning Africa conference in Dar es Salaam, Republic of Tanzania, discussions among ministers from more than 30 African nations crystallised this understanding. As these leaders explored their national digital futures, critical themes emerged: the importance of developing skills for the digital workplace, artificial intelligence readiness, and the centrality of data. Their conclusion was unanimous: building data fluency across education systems isn’t optional—it’s essential for meaningful progress in student outcomes.

The Educational Data Revolution

Today’s educational landscape offers unprecedented opportunities for data-driven improvement, but the implementation of these opportunities must be thoughtful and contextually appropriate. Advanced analytics can identify learners at risk before they fall behind, guide targeted interventions, and shape more effective policies and curricula. Machine learning and predictive analytics reveal patterns of impact and efficacy that were previously invisible to human observation.

Consider the transformative potential: early warning systems that flag struggling students in time for intervention, personalised learning pathways adapted to individual needs, and continuous improvement cycles based on real-time feedback. These aren’t futuristic possibilities—they’re current realities for institutions with data-fluent teams operating within their chosen ethical and regulatory frameworks.

Realising this potential requires more than sophisticated technology. The most advanced analytics platform is worthless without professionals who can interpret results contextually, communicate insights effectively, and implement changes ethically and strategically. This human capacity in the decision-making loop creates the shared culture of organisational data competence that separates successful initiatives from expensive failures.

Navigating Cultural and Regulatory Perspectives

While exploring data fluency in educational contexts, it is important to acknowledge that approaches to using student data in decision-making vary significantly across regions and cultures. For example, European educational systems, operating under frameworks like GDPR, often maintain more restrictive approaches to data collection and use than their American or Chinese counterparts. This isn’t simply regulatory compliance—it reflects deeper cultural values about privacy, consent, and the appropriate boundaries of institutional data use.

In the United States, student success initiatives have been a catalyst for using predictive analytics to anticipate where students may be at risk of dropping out of college. Student success initiatives involve explicit, well-defined data practices focused on academic support and retention. These might include early warning systems that identify students at risk of dropping out, or learning analytics that help customise educational experiences. The newly launched newsletter entitled On Student Success
https://onstudentsuccess.morganedtech.com/ provides detailed information about student success initiatives in US higher educational institutions.

Our workshop will explore how data fluency can be developed within different regulatory and cultural contexts. The goal is to help educators develop the analytical capabilities to make informed decisions about what level and type of data use aligns with their institutional values and legal requirements.

Building Comprehensive Data Fluency

Effective data fluency in education encompasses five interconnected capabilities that transform how professionals interact with information across diverse institutional contexts:

Critical Thinking with Data involves moving beyond surface-level interpretation to understand context, question assumptions, and recognise limitations. In educational settings, this means understanding that correlation doesn’t imply causation and that demographic patterns require careful, ethical interpretation that respects student privacy and cultural sensitivities.

Contextual Application enables professionals to adapt insights across different educational environments and regulatory frameworks. A data pattern meaningful in one institutional context may require a completely different interpretation and response in another setting, particularly when crossing cultural or national boundaries.

Technical Confidence empowers educators to use data tools and techniques without intimidation. This doesn’t require becoming a statistician, but rather developing comfortable familiarity with common analytical approaches and platforms while understanding their appropriate limits and applications.

Ethical Communication ensures insights are shared responsibly, protecting student privacy while enabling informed decision-making. This skill becomes increasingly critical as data use expands across educational systems with varying regulatory requirements and cultural expectations.

Sound Judgment guides when and how to act on data insights within appropriate boundaries. Not every statistical significance warrants immediate action, and data-fluent professionals understand how to balance analytical findings with practical, ethical, and regulatory considerations specific to their context.

From Individual Skills to Organisational Transformation

The journey from data literacy through proficiency to fluency represents a fundamental transformation in organisational culture. Data literacy enables participation in conversations about educational improvement. Data proficiency supports informed leadership decisions about resource allocation and strategic direction. Data fluency drives innovation in practices themselves, creating new approaches to persistent educational challenges while respecting appropriate boundaries.

This progression requires intentional development across all organisational levels. When everyone possesses the necessary literacy, leaders demonstrate consistent proficiency, and specialists achieve the fluency needed to advance institutional capabilities within ethical and regulatory bounds, the entire educational ecosystem benefits. Students receive more targeted support, teachers make more informed instructional decisions, and administrators allocate resources more effectively.

The OEB Opportunity

The December 3rd workshop at OEB provides an opportunity to learn from experienced practitioners who have successfully implemented data fluency initiatives across diverse educational contexts and regulatory environments. Attendees will leave with concrete tools, proven strategies, and the confidence to begin implementing data fluency initiatives appropriate to their own institutional contexts and cultural frameworks.

The timing couldn’t be more crucial. As educational institutions worldwide grapple with increasingly complex challenges—from personalised learning demands to accountability pressures to resource constraints—data fluency provides the competitive advantage needed to thrive rather than merely survive, regardless of where institutions set their boundaries around data use.

Ellen D. Wagner, North Coast EduVisory Services, LLC

Written for OEB 2025 by Ellen Wagner.


About the author: Ellen D. Wagner, Ph.D.

Ellen Wagner is an ed tech innovator, analyst and advisor. Her experiences range from tenured research professor, department chair and administrator in Academic Affairs and Continuing and Professional Education, to successful tech entrepreneur, with three exits to her credit. She has served as a senior executive in five commercial software companies, including Macromedia and Adobe Systems. These experiences helped inform her role as Vice President of Technology and Innovation for the Western Interstate Commission for Higher Education, as she led their community of practice membership association, WCET. At WCET, she co-founded the Predictive Analytics Reporting (PAR) Framework, a predictive analytics research effort funded by the Bill & Melinda Gates Foundation. PAR launched as an independent non-profit service provider; Shortly thereafter PAR was acquired by Hobsons where it became part of Starfish Retention Solutions. Proceeds from this sale established and funded the Foundation for Student Success, now managed by NCHEMS.

Ellen is currently managing partner of North Coast EduVisory Services, LLC, where she advises learning tech companies and higher educational stakeholders on digital transformation and emerging technologies for learning, human performance and organisational capacity development.

She is affiliated with the Mixed Emerging Technology Integration Laboratory, Institute for Simulation and Training, University of Central Florida. She serves on the editorial board of eLearn Magazine, and is a reviewer for several juried journals in the fields of learning tech, elearning and online learning. She is a member of the Advisory Board of the Kirwan Innovation Center of the University System of Maryland, as well as the Global Advisory Board for OEB (Online Educa Berlin).

Join Ellen for her Pre-Conference Workshop “Developing Data Fluency for Improving Student Success” at OEB25.