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Universities have rich and diverse operations: they equip students to be effective global citizens, their researchers provide solutions that are relevant nationally and internationally, and they are strong partners in business, industry, and local communities. Technology is driving rapid change in each of these dimensions. This poses a number of challenges and many of the lessons apply across all industries.
Increasing research complexity
There has been phenomenal growth in the demand for compute and storage for research. However, the impact of technology on research runs much deeper than these mechanics. Technology, and the availability of previously unimaginable volumes of data, is enabling fundamentally different approaches to research. For example, data visualization is an increasingly important aspect of many research projects.
The IT challenge is to ensure researchers are aware of and equipped to use the best tools to support their particular research. However, it is not feasible for IT teams to retain expertise across the range of tools that can be used across the different disciplines. One approach which has been successful is to use the power of the crowd and to facilitate putting researchers in touch with peers across the organization who have the skills and experience to assist. This can take many forms, from online communities to more traditional approaches. For example, a weekly “hacky hour” at a campus café enables researchers to bring their technology challenges to discuss and problem to solve with peers, and from there use existing research networks to identify still others who might assist.
While university research might take this to an extreme, it is not uncommon for IT teams in many other industries to lack the scale to have deep expertise in all the applications used across their organization. Indeed, as we head into the future, deeper IT knowledge will increasingly become part of everyone’s role - rather than remaining the domain of the IT team. The hacky hour approach could be adapted for those situations.
"Technology, and the availability of previously unimaginable volumes of data, is enabling fundamentally different approaches to research"
One very interesting development in higher education is the formation of shared compute facilities by multiple universities in partnership with government. Examples are NCI and Intersect. Rather than each institution developing high performance computing facilities to manage peak demand, the shared facilities can be used when demand rises. While this is a very effective model in the sector, it is hard to imagine other sectors adopting it!
Cornucopia of online tools to support effective learning
CIOs everywhere are plagued by requests from their organization’s staff to use this or that app to support different aspects of the organization’s work. In higher education this is taken to a whole new level. There has been a boom in new online tools to support learning - polling tools, captioning tools, tools to support self-paced learning, tools to support group work and so on. Students bring their own technology on campus and have strong views about the applications they want to use. They are savvy and demanding consumers of technology.
In response, IT teams in universities have needed to develop the capability to prototype rapidly, while at the same time thinking about potential for scaling. One of the key lessons in this is the importance of spending adequate time at the start of a project in understanding the nub of the problem that is being solved. Design thinking approaches are proving useful here. The lessons are similar to those in other industries using agile approaches - don’t underestimate the amount of time required from both IT and colleagues across the institution when using agile approaches and maintain a strong focus on reviews along the way.
A right-sized approach to technical architecture is also essential for avoiding road-blocks, excessive complexity, and technology debt. There is frequently more than one tool that can be used to address a particular problem. Ensuring that the selected tool complies to the extent necessary with well articulated architectural principles and agreed application platforms is critical for future agility.
In addition to learning technology, our universities also leverage specific technology for each discipline. For example, robotic “patients” are used in nursing education, VI/AI/MR are used in social sciences, design and engineering related disciplines, and 3D imaging machines of all varieties are used across disciplines.
Data, AI and IT security
Some IT trends are universal. Like our peers, higher education CIOs are grappling with how best to leverage and protect data. This is an area of increasing investment and CIO attention.
One interesting use of data and artificial intelligence in higher education is to support student success. A number of universities use a range of approaches to analytics to identify students at risk of performing poorly and to tailor learning to individual requirements.
Universities have also been quick to embrace the concept the use of chat bots to enhance the student experience.
Leveraging technology investments
Universities have a strong focus on ensuring that the funds entrusted to them are used effectively. When it comes to technology that involves ensuring that all staff and students have the digital literacy skills required to enable them to leverage the technology available. As complexity continues to increase, digital literacy is becoming an increasingly important focus for many institutions. To maximize the competitive advantage from our technology investments, it is the collective digital capability of the entire organization that will make the difference.
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