Escaping the Catch-22 of digital transformation failure

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.

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