COVID-19 and Higher Education: A Case Study in Data Modernization

It’s hard to find a product whose value depends more on human proximity – and is therefore more vulnerable to COVID-19 – than higher education. Insert six feet of social distance into a lecture hall, dormitory or football game, and the college experience turns upside down.

No surprise, then, that colleges and universities must rejigger their product, balancing physical and virtual instruction, while somehow maintaining an intimate and healthy community. The stakes could not be higher as enrollments fall and tuition pressures build, threatening to kill institutions with shaky financial foundations.

Some colleges and universities will keep half their students home for the fall; others will bring everyone on campus and rotate them through half-filled classrooms. All need to monitor online course participation as well as human density and infection risk. This spells a crash course in digitalization, analytics and cloud computing. 

Forward-thinking, well-funded institutions will seize the opportunity to turn weakness to strength. They will modernize their data analytics programs to both address the urgent needs and, in the medium term, open a new world of data discovery, self-service and advanced analytics.

Let’s examine a case study, based on years of Eckerson Group consulting work with colleges and universities, as well as a current engagement. This university’s IT organization struggles to meet exploding demand for data among its member colleges. They intend to enable governed and automated self-service by consolidating on a central cloud data platform. Their three-year plan calls for analysis and prediction capabilities that enhance teaching, learning and research, as well as the health and safety of their community. Their methods of achieving this offer lessons for all industries.

Use Cases

First, let’s consider this university’s target use cases, which assemble structured, semi-structured and even unstructured data from myriad sources to address online instruction quality, health & safety, and faculty research opportunities.

  • Online instruction. This university’s analytics team will integrate online students’ clicks and views, class rosters, student grades and student demographics. This helps analysts at member colleges and administrators measure online participation and outcomes by course, class size, delivery method, student group and other categories. With their findings, they can start to predict course outcomes and adjust curricula.

  • Health and safety. Health personnel and administrators will continuously assess facilities data, class rosters, COVID-19 test results and potentially contact tracing application data to monitor human density and infection risk. They will check KPIs against agreed thresholds to identify, predict, measure and respond to outbreaks.

  • Faculty research. Over the medium term, member colleges and administrators need to assess and capitalize on the landscape of faculty research opportunities. By assembling grant program data, research proposals, finance records and academic records, they can identify and respond to research opportunities in dynamic fields, such as COVID-19 population health research.

These use cases require an ambitious data modernization initiative, starting with a cloud data platform and centralized program.

Cloud data platform. Today the university relies on a patchwork of on-premises repositories and tools, including a SQL data warehouse and education-specific database and applications. Over the next three years they will consolidate on a cloud platform in order to eliminate data silos and redundant processes. Their IT organization will standardize the data integration process on an automated tool, potentially leveraging the cloud platform solution, to provide analysts with a single, commonly formatted repository of multi-sourced data.

Centralized program. To foster self-service and broader use of advanced analytics over time, the university also will standardize data preparation activities on a single automated tool. They will train ad-hoc analysts and power users throughout the university on the common solution and common practices to assemble data from the consolidated platform. This will entail a federated Center of Excellence, overseen by IT, that trains, recruits and manages self-service analysts throughout the member colleges on a dotted-line basis. The centralized program will replace today’s fiefdoms of tools and independent practices, improving operational efficiency and enabling new data-driven decisions.

Guiding Principles

So, what can data analytics leaders in other industries learn from this university? The following three guiding principles stand out.

Start with the urgent use cases. The COVID-19 disruption poses severe and even existential threats to organizations of many types. Data analytics leaders must alleviate near-term pain to get the funds they need and keep their organizations afloat. Many of these use cases will center on creating sticky digital experiences for customers, and maintaining safe shared human space indoors. Data and analytics provide the necessary insights to make this happen.

Consolidate tools and infrastructure. As in many organizations, this university has many duplicative tools, and retiring them will help fund the new initiative. They also might standardize on a single provider of the data platform as well as data integration and data preparation capabilities. Opting for a solution suite over best of breed in this fashion can reduce administrative burden and simplify training, service and support. 

Enable governed self-service. Changing customer preferences, virus risks and regulations mean that front line creativity matters more today than ever. This university’s federated Center of Excellence will improve governance by instituting common policies and practices among self-service analysts within the colleges. And it will arm them with automated data prep and analytics tools that improve their productivity. 

Schools, stores, and other face to face businesses now must balance the physical and virtual worlds. The data modernization steps and guiding principles outlined here can help maintain this balance until market forces find a new equilibrium.

Kevin Petrie

Kevin is the VP of Research at Eckerson Group, where he manages the research agenda and writes about topics such as data integration, data observability, machine learning, and cloud data...

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