Maintaining service levels to 30,000+ students in 2022: University Service delivery guide

A university's reputation is built on recruiting the best lecturers, providing high quality educational material and attracting a diverse range of highly intellectual students to nurture through their system and develop into high achieving graduates.

Providing a high-quality technology infrastructure, an interactive learning environment and integrated campus experience are all things that are considered when staff and students consider which university is right for them.

Higher education institutions are underpinned by the delivery of IT infrastructure, this has become even more evident during the recent pandemic. Therefore the quality of service and capability of that infrastructure is directly related to many other factors such as student success, staff recruitment, student happiness, student enrolment, staff wellbeing...the list goes on.

Let’s begin by saying, it’s hard. Why? The IT service delivery at universities is a complex and intricate process, with huge demands on resource. The trend in demand is going up with resources increasingly lagging behind.

Quickly, let’s take a look - at what's covered:

Service delivery challenges in Higher Education 2022

-Service desk, incident management and response

-Heavily silo data and systems

-Complexity across the board

-Meeting the demands of a digital, dispersed, and diverse generation

What can be done to overcome these challenges

-System unity, data share and visibility

-Leverage AI

-Automation, auto-provisioning and data corelation

-Service management & network management: hand in hand

Service delivery challenges in Higher Education

Service desk, incident management, and response

The amount of time dedicated to processing, allocating and responding to support tickets is substantial.

With potential support queries coming from staff, students, international students and anyone relying on university IT. This pool of people is only increasing with the growth of universities, their ability to enroll more students thanks to the rise in distance learning courses, the staff to support this e-learning growth, more research entities, etc.

The process of dealing with these support queries is often fragmented and manual, with a lot of time wasted routing incidents to relevant departments, or investigations based on minimal information. In the case of genuine incidents that come to light due to issues with service delivery (rather than user error or confusion), there are long resolution times due to the complex environment the investigation needs to occur in.

This process is not only inefficient and ineffective, it doesn’t scale. The reliance on a manual process means human error will inevitably occur, resulting in things getting missed, more pressure and frustration. Without mentioning the impact on a students learning experience.

Heavily siloed data and systems

A result of rapid growth to meet demand? Disjointed procurement decisions? Reacting to world events? This could also be linked into not having a clear strategy or not having an underlaying architecture that supports integration. Regardless - having so many systems that are solely used for one purpose rather than contributing to one stitched together fabric, causes problems.

Firstly, let's state the obvious, it’s a nightmare to maintain. Having so many different systems to monitor, keep up to date, troubleshoot, come in and out of - it doesn't make anybody's job easier, that's for sure.

Secondly, it can be massively frustrating for the poor person (or people) doing the troubleshooting. When issues arise and investigation spans systems or seems to be a transactional issue between systems. Without the visibility to see transactional metrics or ‘make sense’ of what's happening, it can be a tedious process to jump between and try to arrive at a root cause.

Furthermore - how are you supposed to gain intelligence from data if it’s not being shared between systems? With correlated data, it’s extremely difficult to derive insight from events that occur in different systems, especially when they are being accessed and used from anywhere and at any time.

Finally, the user experience is poor when there is a requirement to use multiple systems. Sure, they appreciate that different systems do different things but when data is not being shared and multiple log-ins are required, there is a lot of repetition, this again doesn’t do anything for productivity, efficiency or insight.

Complexity across the board

We’ve slightly covered this above, but wanted to go into a little more detail here.

Firstly, why all the complexity?

We can probably put this down to a few things, namely:

1. Growth of universities

Impact - more locations, more remote learning

Result - Technology growth to underpin.

2. Meeting the needs of the ever-increasingly digital users

Impact - More operational technology/IoT, more advanced systems to enhance experience

Result - Technology growth to enable

3. Move to more cloud based systems

Reason -Traditional methods didn’t scale well, improves latency issues for remote workers, easier to manage

Result - Issues migrating all systems resulting in Hybrid IT

4. Skill gap in university IT teams

Impact - Point solutions are brought in to solve a problem

Result - Increases ‘things’ that need to be managed.

What is the impact of this complexity?

Putting it simply - it complicates everything, creating an uphill battle for IT teams, but let’s break that down:

Management and maintenance becomes increasingly difficult as more systems are added, there are more things to account for, understand, keep up to date and report on. Visibility can become hard to come by with more dark corners of the network being introduced, whilst requiring specialist skills to truly understand specific systems.  

This results in an inefficient process by non-specialist IT team members to ensure things are maintained correctly and things can be missed, quite easily. Creating more fires to put out, somewhere down the line.  

There are also cybersecurity impacts of this complexity - every new system introduced provides a new potential backdoor to your network, we speak more about zero trust principals here, also the impact of not scrutinising 3rd party technology is severe with the recent dark trace supply chain attack making the news. Adding new systems changes your cybersecurity posture and must be reviewed, risk assessed and actioned accordingly, but things become increasingly difficult as the architecture grows and changes over time.

Finally - the more straw you add to the pile, the more difficult it is to find the needle (Unless you have a metal detector - We’ll talk about this later). The more complex the architecture and components within it, the more of a challenge it becomes to investigate anything. It’s harder to find the root cause of performance issues, harder to maintain continuous improvement and harder to investigate incidents. It’s also difficult to judge the extent of an issue with cross platform visibility.

Meeting the demands of a distributed, digital and diverse generation

Systems can be added to for a certain period of time to increase capability and capacity but there comes a time when the underlying framework needs to change.

Analogy - You can do many things to a two-bed terrace house to improve it - a nice garden, convert the loft, a lazy spa, a conservatory, and these changes satisfy needs. When the demand out-grows fundamental structure - like adding a pool or cinema - the house needs to change to support the growth.

We are seeing this in many enterprises and higher education establishments are no different, the institution has adapted to digitalisation, remote working and e-learning but there is a point where the architecture needs to change to support the different connection types from different locations and increase in the variety of capability requirements.

If changes aren’t made then the impact is a steady decline in quality of service and increase in workload for IT teams and adding risk in the areas of cybersecurity and service outages.

Introduce the migration to the Cloud, investments in SaaS, automation, the introduction of software defined network management or SASE and AI/machine learning to help balance the load and enable innovation in ever digitalised universities.