EDUCAUSE QuickPoll Results: Data Modernization and Management

EDUCAUSE is helping institutional leaders, IT professionals, and other staff address their pressing challenges by sharing existing data and gathering new data from the higher education community. This report is based on an EDUCAUSE QuickPoll. QuickPolls enable us to rapidly gather, analyze, and share input from our community about specific emerging topics.^Footnote1

For the purposes of this QuickPoll, we define data modernization and management as the processes of upgrading legacy data systems and implementing modern technologies to collect, store, integrate, and analyze institutional data more effectively.

The Challenge

Higher education institutions increasingly recognize the strategic importance of data modernization as evidenced by "The Data-Empowered Institution" sitting at the top of the list of most pressing issues identified by EDUCAUSE members in the 2025 EDUCAUSE Top 10.^Footnote2 However, many institutions face persistent challenges that hinder using data, analytics, and AI to increase student success, win the enrollment race, increase research funding, and reduce inefficiencies. Legacy systems often create fragmented and siloed data environments, making it difficult to gain a unified view of institutional performance. Inconsistent data quality, limited data literacy among staff and faculty, and growing concerns around security and compliance further complicate efforts. Constrained budgets and staffing shortages are also major barriers to data modernization and management. Addressing these challenges requires a coordinated approach that combines modern infrastructure, strong governance, and a commitment to fostering a data-informed culture across the institution.

The Bottom Line

Nearly all respondents (92%) reported that their institution is in either early discussions or already has some systems modernized. However, the path to data modernization in higher education is complex, with only 1% of respondents indicating they feel their institution has fully modernized its data management and strategy. IT leaders must act as enablers and advocates, balancing innovation with governance, and user needs with budget and staffing limitations. Institutions that invest in modern, well-governed data systems will be better positioned to drive innovation, improve student outcomes, and make informed decisions in real time. While challenges persist, addressing them head-on will allow institutions to better serve students, faculty, and broader strategic missions. The capacity to harness and trust institutional data is quickly becoming a defining factor in long-term institutional resilience and success.

The Data: Modernization Is In Progress

Almost everyone is in the process of data modernization. The majority of QuickPoll respondents (68%) indicated that their institution is in the process of its data modernization and management journey (see figure 1). The next largest group of respondents (24%) indicated that they have begun discussions for modernizing but haven't implemented any systems yet. Very few respondents (7%) said their institution has not begun to address data modernization, while almost no respondents (1%) felt their institution is at the fully modernized stage in their journey.

Leaders have high expectations for modernization. Overall, 43% of respondents indicated that expectations from leadership exceed the current capabilities at their institution, while 36% indicated expectations are in line with their current capabilities. However, when responses were disaggregated by the reported stage of their modernization journey (see figure 2), a clearer picture emerges of where those expectations are coming from. Among respondents who reported they were in early discussions only, leadership expectations were much more likely to exceed current capabilities (59%) than for respondents who already had some systems modernized (39%). Those with some systems modernized were more likely to report leadership expectations being in line with their current capabilities (40%) than those in early discussions (21%). Respondents rarely indicated that expectations didn't exist or were below current capabilities. These data highlight a potential chicken-and-egg issue: Are institutions able to modernize due to the support and understanding of capabilities by leadership, or do leaders expect less once some systems have been modernized? Either way, leaders by and large expect at least as much as capabilities allow, sometimes more.

When asked to describe challenges or issues preventing their institution from meeting expectations, lack of resources, both human and financial, were very common responses. Additionally, respondents frequently mentioned data silos and lack of communication and official governance policy as barriers to meeting leaders' expectations.

The Data: Institutional and Staffing Prioritization

Strategic priorities are often aligned with data modernization efforts, but more consistency is needed. When asked whether strategic priorities are driving data modernization efforts at their institution, the majority of respondents (59%) indicated that they were in some areas but not consistently (see figure 3). About a third of respondents (34%) reported that priorities are fully driving modernization efforts, while very few respondents indicated they were not a driving force (5%) or that they didn't know (2%). Overall this is good news—strategic priorities should be the major driving force for large-scale initiatives, but the inconsistency reported by respondents indicates a need for improvement from leaders to better communicate and advocate for data initiatives.

Priorities for data initiatives vary, but operational efficiency tops the list. When asked which priorities were the main drivers for data modernization efforts, a majority of respondents (58%) selected operational efficiency (see figure 4). The next most commonly selected priorities were student success and retention (46%), data visualization and decision support (43%), and institutional research and reporting (35%). While only 30% of respondents indicated that preparation for AI and analytics was a priority, the recent 2025 EDUCAUSE AI Landscape Study found that a majority of respondents (77%) have at least some AI-related strategy in place at their institution.^Footnote3 Taken together, these findings point to potential gaps in AI-related data preparation. The least-commonly reported priorities include governance and compliance (27%), automation (17%), and advancement and fundraising (11%). Respondents who selected "Other" indicated that their priorities included finance, human resources, and reputation management systems.

Staffing support and organization are lacking. A full 20% of respondents said their institution has taken no steps to upskill staff in support of data modernization efforts (see figure 5). Only 35% of respondents indicated their institution has hired staff with up-to-date skills. Similarly, 35% of respondents said their institution has reorganized teams or roles to support data functions. Formal training programs within the institution were reported by about a quarter of respondents (26%), while only 19% said their institution will sponsor certifications. For those who selected "Other," partnering with and engaging in training from solution providers was the most common action, while a few indicated they are currently in the planning stage of how best to upskill their staff. Figuring out how to better support staff and help them improve their skill sets could be a big help for leaders planning data modernization efforts.

Common Challenges

The process of data modernization often encounters significant hurdles. The most commonly reported challenge for advancing data strategy (see figure 6) was siloed data across departments (51%), followed very closely by budget or funding limitations (49%). Culture and change resistance, as well as skill gaps among staff—unsurprising, based on the lack of upskilling reported in figure 5—were both selected by 41% of respondents. About a third of respondents (33%) indicated challenges with data governance and compliance, while a quarter indicated a lack of leadership or executive sponsorship. The least reported challenge was inadequate technology infrastructure (18%). These responses highlight the importance of people and communication when planning and implementing data strategy, perhaps suggesting the need for a more unified and centralized approach to data organization and governance. To help address these, explore the Strategic Planning and Governance and Recommendations and Resources sections of the 2024 EDUCAUSE Analytics Landscape Study.

Respondents reinforced many of the above challenges in open-ended responses. When asked to describe challenges they are facing in their data modernization initiatives, they highlighted several key struggles in their own words:

Resources are in short supply.

• "Time and skill resources. We do not have the funding to support the staffing these projects require, so we hire external support who do not fulfill the needs nor understand the culture or the mission."
• "The biggest rate limiter for us is staff time to attend to this work, even where there is executive buy-in and agreement on how best to proceed."
• "Chasing down shiny things (AI and other novelties) are distractions that eat up valuable time and resources. It would have been better spent on establishing the foundations like master data management and mapping or classifying what lives where, who is responsible for maintaining it, and how it impacts our core activities."

Siloed data and older technology are difficult barriers to overcome.

• "Ancient core-historic systems, upskilling, and silos on top of the different datasets and their utilization."
• "Resourcing to modernize existing applications and a lack of clear guidance on the institution's needs."
• "Large amounts of 'shadow' data services and work that are not centrally known."

There are gaps in organizational support and leadership.

• "It is not an organizational priority. Each team is making its own decisions, but we strive to collaborate and share knowledge."
• "The chief data officer role in higher education institutions needs to be thought through and well defined. Often, executive leadership doesn't know how to define these roles and quantify the skill sets needed. This cannot be completely IR or completely IT. The [person] leading a data unit should have experience and/or understanding of both."
• "There is not a strong commitment to data analytics, data-informed decision making, or data modernization at the executive level."

Promising Practices

Institutions are adopting a range of practices that align technology, people, and process. Centralizing data through modern platforms such as cloud-based data warehouses or lakes allows for better integration and access across departments. Also, establishing clear data governance frameworks with defined roles, standards, and accountability can help maintain data quality and trust. Respondents identified the following as promising practices, with some caveats, in their own words:

Data organization and governance pave the way for modernization.

• "Implementing basic taxonomies and getting consistency of definitions across the institution (even if the definitions differ across domains) is fundamental but often neglected in favour of shiny new technology things. It's so basic it should go without saying but I see it neglected time and time again in favour of new, more interesting shiny things."
• "Good governance will make everything easier, but it's not a quick win."
• "We first need to provide clear policies and include use cases/examples."
• "Clearly assigned responsibilities helps you to maintain your data quality."

Easy wins can help you get started, but there must be long-term commitment.

• "I think focusing on 'easy wins' is the wrong approach. Data modernization needs sustained commitment over time to succeed."
• "Many of the easy wins have been achieved. The most value from future projects could come in the form of reducing tech debt and restructuring roles, which aren't very easy."
• "Focus on high-value, easier-to-achieve dashboards using platforms you already own. These may not be where you end up long term, but you can gain support through bite-sized successes."

Look for opportunities to grow data modernization efforts during large projects.

• "Taking advantage of a new ERP implementation to force reconsideration of how data management and reporting happen."
• "We have recently begun moving to cloud-native solutions that can support data lakehousing, IoT, and AI. This has been a grassroots effort but is starting to make progress at the institutional level."

Use communication and collaboration to reduce siloing and grow support.

• "Create shared dashboards or reports—make important data (like enrollment or student progress) easy to find and access in one place. Offer quick training sessions—teach staff how to use tools like Argos, Excel, or Brightspace in short, focused trainings. Pick a data helper in each office—choose someone in each department to help with data and work with IR or IT when needed."
• "Collaboration across silos, unifying tools, and establishing best practices."
• "Collaborative efforts internally and with other institutions."

Critically, institutions need to pair these strategies with strong change management. Building stakeholder engagement early, demonstrating quick wins, and aligning data initiatives with broader institutional goals can help leaders manage the complexities of data modernization.

Notes

1. QuickPolls are less formal than EDUCAUSE survey research. They gather data in a few days instead of over several weeks and allow timely reporting of current issues. This poll was conducted between June 2 and June 4, 2025, consisted of 12 questions, and resulted in 165 complete responses. The poll was distributed by EDUCAUSE staff to relevant EDUCAUSE Community Groups rather than via our enterprise survey infrastructure, and we are not able to associate responses with specific institutions. Our sample represents a range of institution types and FTE sizes. Jump back to footnote 1 in the text.↩
2. Susan Grajek and the 2024–2025 EDUCAUSE Top 10 Panel, "2025 EDUCAUSE Top 10: Restoring Trust," EDUCAUSE Review, October 23, 2024. Jump back to footnote 2 in the text.↩
3. Jenay Robert and Mark McCormack, 2025 EDUCAUSE AI Landscape Study: Into the Digital AI Divide, research report (Boulder, CO: EDUCAUSE, February 2025). Jump back to footnote 3 in the text.↩

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Sean Burns is Researcher at EDUCAUSE.