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Escaping the Catch-22 of digital transformation failure
Derived from Joseph Heller’s novel of the same name, a Catch-22 is a no-win scenario, a situation where progress seems impossible because of mutually contradictory rules. This impasse will be painfully familiar to any university leader who, intent on driving digital transformation, has found fragmented infrastructure impeding the very change that will resolve the problem of fragmented infrastructure. Surely there must be a way out of this head-spinning nightmare? There is, read on…
Key takeaways
84% of academic leaders say effective digital transformation is a priority — but only 48% say they're making good progress. (Elsevier Academic Transformation Survey, 2024)
The core barrier isn't culture or budget. It's a data Catch-22: institutions need transformation to fix their fragmented infrastructure, but can't transform because their infrastructure is fragmented.
AI can accelerate progress, but requires clean, centralized data foundations to deliver real value. Universities must address the problem of legacy data, processes and governance before this can happen.
A purpose-built RIMS breaks the Catch-22: it integrates with existing systems, centralises research data, and provides the reliable foundation every subsequent digital capability depends on.
Redefining higher education
This is a challenging time for higher education. Universities face a range of pressures, including declining government funding, falling enrollments, and the difficulty of assimilating new technologies. Behind these concerns is a growing debate around the position of academia in wider society and the relevance and impact of its teaching and research outputs. This complex situation requires a coordinated response, with many institutions looking to redefine the value proposition of higher education through measures like the diversification of income streams, collaboration with industry to redesign curricula, or efforts to highlight the real-world impact of their research outputs. Universities are also supporting these changes at the infrastructure level by embracing more flexible learning environments, nurturing links with their local communities and pursuing digital transformation programs.
Often viewed simply as the broad-based integration of digital technologies, in its classic academic definitionopens in new tab/window, digital transformation is something far more profound: “a series of deep and coordinated culture, workforce, and technology shifts that enable new educational and operating models and transform an institution’s business model, strategic directions, and value proposition.” Small wonder, then, that many academic leaders, eager to reposition their institutions in a changing world, are keen to use digital transformation as a lever for institutional change on both the technological and cultural levels. All too often, however, these ambitions are frustrated. In Elsevier’s 2024 Academic Transformation Survey, 84% of the academic leaders interviewed said that effective digital transformation was a priority, but only 48% said they were making good progress.
Barriers to digital transformation
So, what is the challenge? University-based digital transformations usually stall for a combination of reasons. One of the most frequently cited is cultural resistance to change, with faculty and staff sometimes hesitant to adopt technologies that may disrupt their traditional pedagogical or administrative autonomy. A recent studyopens in new tab/window highlights how these concerns can translate into “[staff] feeling overwhelmed, fear of technology and job security, and ideological conflicts over the nature of quality higher education.” While it is easy to see this internal reluctance as inertia, some staff may face the opposite problem, slowing the adoption of new technologies with unrealistically high expectations based on their experiences in other environments.
Related to this impediment is a failure of strategic leadership, either because rollouts are fragmented or because senior academics lack the necessary career experience to drive this kind of change. Some critics argue that the consensual management cultureopens in new tab/window of universities runs counter to the strong directive authority that digital transformation programs demand, while others suggestopens in new tab/window that the organizational complexity of many universities – their combination of matrixed, decentralized and distributed organizational models – makes any kind of decisive change difficult. After all, digital transformation is a complex business that combines strategic, technological and financial challenges with logistical hurdles, such as developing training programs or managing compliance issues. Another familiar culprit is the lack of sustained financial investment, or the difficulty of recruiting and retaining high-quality IT staff. This leads us to perhaps the most obvious obstacle, and a key part of the rationale for transformation in the first place: the problem of outdated, fragmented infrastructure. There is more than a hint of Catch-22 here: Universities need digital transformation because their existing infrastructure is a mess; however, universities cannot transform because their existing infrastructure is a mess.
Embracing the future by managing the past
How should academic institutions go about resolving this apparent paradox? Some critics dodge the question altogether by proposing that “AI transformation” has superseded digital transformationopens in new tab/window, but, as frequently diffuse university AI rollouts have shown, the latter remains an aspect of the former. Indeed, while these technologies hold enormous promise, they are not a silver bullet. Even the most sophisticated AI can be ineffective unless it is consistently trained on quality data. What this means in practice is that academic institutions urgently need to get their legacy content – research outputs, datasets, software, audio/video recordings, images, records of externally funded projects, records of internally awarded research – into a coherent form before they can apply AI to them. Put simply, it is difficult for universities to move into the future if they have not taken control of their past.
The problem is that many universities lack the digital infrastructure to do this, so they have no way to harness the combined power of their content assets and records, let alone other datasets that may be publicly or commercially available. All too often, they suffer from fragmented systems, undocumented processes and related organizational problems such as key-person dependency risk or the absence or misalignment of policies. Every day, university IT teams are grappling with disjointed legacy technology frameworks that are often held together by the operations equivalent of duct tape – ingenious workarounds, or “shadow systems” like spreadsheets, shared inboxes or offline trackers, all of which can lead to increased work, costs and risks, as well as potentially dangerous blind spots in reporting and compliance.
Meanwhile, at the level of the data itself, there are often issues with incompatible formats, partly due to information being siloed across multiple locations. This can lead to unhelpful duplication, with data from different systems providing divergent answers to the same question, or the opposite problem: data is missing altogether or simply inaccurate. Without at least some degree of centralization, it may not even be possible to discover these conflicts, errors or omissions, let alone address them. This prohibits successful reporting, or in the case of a key area like research, provides a gravely distorted view of institutional outputs and impact – usually one that makes research teams appear less productive and effective than they really are. For a university keen to bolster its reputation and showcase its positive impact on society – a solid return on all those public funding dollars – this is a serious problem.
Research workflows in the university’s IT infrastructure
Accommodating research within a university IT infrastructure is challenging because of the sheer range of use cases and data types involved, along with the need to balance external showcasing and collaboration with internal management, security and support. Ambitious institutions might work to implement what IT professionals call a “hybrid infrastructure model,” combining on-site data centers or private clouds with public cloud services (e.g., Azure, Amazon Web Services) within a single ecosystem, often with a user-facing platform or interface. Depending on the resources available, institutions can supplement this arrangement with institutional repositories and external tools such as reference managers and project management tools to help manage, showcase and track research data. The whole process is extraordinarily demanding, requiring significant long-term investments in both IT capabilities and adjacent areas such as training, customer service and change management.
Given the scale of the commitment required to self-build an institutional research system – along with the increasing complexity of the research enterprise, the pressing need to ensure global compliance and the growing cybersecurity threat – many universities opt instead to bring in a purpose-built Research Information Management System, or RIMS. A RIMS – sometimes also referred to as a Current Research Information System (CRIS) – is a software solution used by research institutions to centralize, manage and showcase the research lifecycle. By consolidating and streamlining data on publications, grants and scholarly activities, a good RIMS can directly facilitate the transition from manual processes to an integrated digital ecosystem. There is also evidence that such systems can support some of the cultural aspects of digital transformation, for example, by bridging the gapopens in new tab/window between what some regard as traditional library values (open access, transparency, neutrality, privacy) and the management focus on performance and evaluation.
Of course, not all RIMS are created equal. The capabilities of different offerings can vary widely, as can the cost and complexity of implementation. From the perspective of digital transformation, however, the biggest challenge may be ensuring a RIMS can integrate successfully with a university’s existing IT infrastructure. Systems-level compatibility will reduce cultural resistance, support ease of adoption and reduce the number of openings for potential cyberattacks. It will enable the RIMS to fulfil its role as a central hub that unites and aligns disparate data sources, improving administrative efficiency, informing strategic decision making and promoting research reporting and visibility. This virtuous circle of connectedness and enhanced performance is, in the end, what digital transformation is all about.
Frequently Asked Questions
Digital transformation in research means moving from fragmented, manually-maintained systems toward connected, data-driven infrastructure that supports decision-making at every level. For research institutions, this goes beyond adopting new technology — it requires establishing authoritative data sources, automating compliance and reporting workflows, and creating the conditions for strategic use of research intelligence. Institutions that have undergone this shift report faster response to funding opportunities, stronger national assessment performance, and a reduced administrative burden on researchers.
The most common barriers are data fragmentation, legacy systems that cannot communicate with each other, and a lack of clear data ownership across institutional departments. Research data frequently sits in siloed HR, finance, and publication systems with no single authoritative source. Without a centralized research information layer, institutions struggle to produce consistent reporting, respond to evolving compliance requirements, or demonstrate impact at scale. Cultural resistance and unclear ROI cases are also common — successful transformation programs address governance and change management alongside technology.
Research infrastructure refers to the systems, data, and processes that enable an institution to manage, track, and act on its research activity. It underpins everything from compliance reporting and funding management to researcher profiling and impact assessment. As global competition for research funding intensifies and reporting requirements grow more complex, institutions without robust research infrastructure face a compounding disadvantage — slower reporting cycles, inconsistent data and limited ability to make evidence-based strategic decisions. Investing in research infrastructure is increasingly recognized as a prerequisite for institutional research competitiveness, not an administrative overhead.
When research data lives across disconnected systems — HR, finance, publication repositories, grant management — every report requires manual aggregation, every compliance cycle carries risk of error, and institutional leaders lack the real-time intelligence to act strategically. Fragmentation creates invisible costs: staff time spent reconciling data, duplicated records, missed funding deadlines, and incomplete national assessment submissions. A unified research information layer eliminates these inefficiencies and creates the data foundation that meaningful digital transformation depends on.
Common warning signs include: researchers spending significant time correcting or reconciling data across systems; inconsistencies between what HR, finance, and research office systems report about the same activity; difficulty producing reliable outputs for national assessment exercises or funder reporting; and an inability to answer basic strategic questions — such as which departments are most active in external collaboration, or where grant pipeline gaps exist — without manual data collection. If transformation initiatives are repeatedly stalled by data quality issues or integration failures, infrastructure is almost certainly a root cause rather than a secondary concern.