How to make universities even smarter


Universities using data is a topic close to SAS’ heart, and in a recent blog we talked about the specific challenges that UK universities face in attracting and keeping students, and how data can help universities better understand the student lifecycle.

The first Universities and College Admissions Services (UCAS) deadline for undergraduate course choices has just passed, so student recruitment is front-of-mind for the higher education sector. I recently hosted a webinar to delve further into how universities can use data to make smarter choices. Effective data analytics can help education institutions to:

University lecture

  • Improve recruiting and admissions
  • Tackle student retention
  • Understand the profitability of courses and research
  • Forecast intake and finances
  • Increase donations and philanthropic activities
  • Track and improve REF research funding (REF being the framework that calculates funding for UK higher education)

Achieving these goals requires informed decision making, effective resource planning, and a commitment to optimising student success across a wide range of university departments.

Best practices for analytics across the student lifecycle
From working with universities all over the country, I've gleaned three best practices that are fundamental to successfully applying analytics in the student lifecycle:

  • Integrate data across the institution
  • Equip all decision makers with self-service analytics and data visualisation capability
  • Use advanced analytics to identify current and future trends for informed decision making

Of course, these must go hand-in-hand with policies and processes to implement, support and drive these strategies.

The challenge is that there's often too much data in too many places. In some cases, data volumes may exceed storage systems, making it difficult to run analyses. Some universities suffer from poor data quality, which means it cannot be trusted. Others struggle with inconsistent data across multiple sources, making the data redundant.

In overcoming these data challenges, it's crucial that you not place IT under undue pressure, running requests on demand, or tackling data consistency issues for each request. The solution has to be easily repeatable in order to be cost effective.

Introducing data analytics to your organization
Data analytics offers efficiencies in IT, along with key improvements in how outcomes of analysis are presented and used. Not everyone in an organisation will be accustomed to working with data, yet many will recognise the potential benefits of using it. It could be a dean looking at a specific department, a professor looking at a course, or faculty heads looking at trends within the faculty. For this reason, it’s not just the process of gathering data that requires attention, it’s also how you present the outcomes.

Visualisation gets an organisation much closer to achieving those efficiencies. It provides an at-a-glance view for people to delve deeper into the specific statistics they need, even if they’re not experts in handling and analysing data -- and it's self-service instead of reliant on IT.

To learn more about making the most of data in higher education, watch the webinar. You can also read how higher education organisations harness big data beyond business intelligence (BI) to make faster decisions.


About Author

Peter Snelling

Principal Systems Engineer

Peter Snelling is Principal Technical Account Manager at SAS Public Security. Based in the UK, Peter's responsibilities include raising the awareness of SAS software within the policing and intelligence community.

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