How AWS helps higher education institutions navigate data

Data is key to a higher education institution’s ability to expose insights and improve student performance and outcomes. Helping institutions understand how data can be used and how it can propel institutions toward a brighter future is a priority for Amazon Web Services (AWS). With many data solutions available, AWS can help education institutions map out an individualized data journey by starting simple, unlocking existing data, and moving toward a fully data-driven institution.

Higher education institutions are at different stages of their data journey and may have different objectives, including:

• They may be using data already, but would benefit from a single solution, like contact center intelligence, to improve access to student enrollment information.
• They may have migrated a core system, but have yet to build a data lake or leverage analytics platforms to gain wisdom from the data, requiring more than one data solution.
• They may be experienced with data systems, but have the goal to integrate generative artificial intelligence (AI) data solutions to predict academic challenges and offer intervention.

“Data products and solutions can be complex and difficult to navigate for higher education institutions,” said Valerie Singer, general manager, AWS Global Education. “Our goal is to simplify these solutions to best meet our customers’ needs by outlining a strategic journey for each customer, showing how data solutions can build upon current data implementations, and how data can help address higher education needs.”

What does a data journey entail?

The first step in creating an AWS data journey is to meet with the higher education institution to define a clear business objective and goal, also called the ideation step. The meeting(s) will include the institution’s relevant administrative stakeholders and technology leaders and help identify current business priorities, share examples and case studies, and establish a baseline for data outcomes and metrics for success.

After defining the institution’s objectives and goals, AWS works with the institution to evaluate the current potential in terms of people, processes, and technologies within the institution. If needed, this may include upskilling institution staff and administration in cloud technologies. It may also include application assessments to determine if the current applications have the capacity to take on additional workloads.

Once the institution leaders and AWS determine the gaps between their current data models and the proposed objectives and goals desired by the institution, they work together to build a recommended custom data journey, including milestone projects and AWS solutions, that build towards the desired business outcome. Depending on the institution’s current solutions and their desired goals, the journey may range from one single-scoped solution to multiple solutions to accomplish their needs.

How customers use data solutions to meet business objectives  

AWS showcased the data journey during this week’s Bett UK in London to 40 attendees from global higher education institutions. The session presented examples of why data matters in higher education as well as barriers that often inhibit institutions to align people, processes, and technology. During Bett UK, attendees also had the opportunity to hear and learn from other higher education institutions using data to improve retention and enhance reporting mechanisms. Read more in the following paragraphs about how two higher education institutions implemented data solutions to solve their challenges. 

The Federal Institute of Rio de Janeiro (IFRJ): IFRJ sought a solution to organize reliable information, improve retention and student success, and automate collecting and extracting data for decision making. Working backwards from these goals with 4strategies (an AWS Partner) and FICAR, decision makers chose to build a searchable data lake that connected various data sources. The data lake was used to align information across the institution by generating reports on student retention and prompting educators to collaborate and identify predominate factors to student dropout rates.

“Going beyond the data lake, and using it as a source of organized information and conceptual model, a tool was created that could provide autonomy for managers in building dashboards, including using generative AI, which is a very important feature,” said Fabio Carlos Macedo, technology director at IFRJ. “The organized data lake allowed FICAR to be connected, delivering insights about students and issues related to retention and success – allowing education leaders to develop a set of actions to improve these rates.”

University of East London (UEL): Working with the end in mind, UEL had a goal to increase student engagement and retention and reduce student dropout patterns by better leveraging data. To meet this goal, they needed to integrate and analyze data streams – including student achievement and attendance data. UEL aimed to simplify engagement reporting and spot patterns of disengagement earlier. This would allow them to deliver targeted interventions, extra support, and resources to at-risk students to improve retention. By participating in data workshops with AWS, UEL has been able to further scope the use of AI and machine learning (ML) tools to enhance approaches to the delivery of teaching.

“Working with AWS has provided opportunities to expand our ML work into other areas of the university because we now have the skills and knowledge to do so,” said Gary Tindell, head of insights for UEL. “With this increasingly rich and valuable dataset, we would like to refine our student engagement reporting and gain a greater understanding of our data by making use of the generative AI and business intelligence tools available in AWS.”

If you represent a higher education institution and are interested in learning how AWS can help guide your institution through your data journey, please contact us.