Analytics Stories: A Case Study from George Washington University
As I have shared within other posts within this series, businesses are using analytics to improve their internal and external facing business processes and to strengthen their “right to win” within the markets that they operate. At first glance, you might not think of universities needing to worry much about their right to win, but universities today are facing increasing competition for students as well as the need to increase efficiency, decrease dependence upon state funding, create new and less expensive delivery models, and drive better accountability.
George Washington University Perceives The Analytic Opportunity
George Washington University (GWU) is no different. And for this reason their leadership determined that they needed to gain the business insight to compete for the best students, meet student diversity needs, and provide accountability to internal and external stakeholders. All of these issues turned out to have a direct impact upon GWU’s business processes—from student recruitment to financial management. At the same time university leadership determined the complexity of these challenges requires continual improvement in the University’s operational strategies and most importantly, accurate, timely, and consistent data.
Making It A Reality
GWU determined that getting after these issues required a flexible system that could provide analytics and key academic performance indicators and metrics on demand, whenever they needed them. They, also, determined that the analytics and underlying data needed to enable accurate, balanced decisions needed to be performed more quickly and more effectively than in the past.
Unfortunately, GWU’s data was buried in disparate data sources that were largely focused on supporting transactional, day-to-day business processes. This data was difficult to extract and even more difficult to integrate into a single format, owing to inherent system inconsistencies and the ownership issues surrounding them — a classic problem for collegial environments. Moreover, the university’s transaction applications did not store data in models that supported on-demand and ad hoc aggregations that GWU business users required.
To solve these issues, GWU created a data integration and business intelligence implementation dubbed the Student Data Mart (SDM). The SDM integrates raw structured and unstructured data into a unified data model to support key academic metrics.
“The SDM represents a life record of the students,” says Wolf, GWU’s Director of Business Intelligence. “It contains 10 years of recruitment, admissions, enrollment, registration, and grade-point average information for all students across all campuses”. It supports a wide-range of academic metrics around campus enrollment counts, admissions selectivity, course enrollment, student achievement, and program metrics.
These metrics are directly and systematically aligned with the academic goals for each department and with GWU’s overall overarching business goals. Wolf says, “The SDM system provides direct access to key measures of academic performance”. “By integrating data into a clean repository and disseminating information over their intranet, the SDM has given university executivesdirect access to key academic metrics. Based on these metrics, users are able to make decisions in a timely manner and with more precision than before.”
Their integration technology supports a student account system, which supplies more than 400 staff with a shared, unified view of the financial performance of students. It connects data from a series of diverse, fragmented internal sources and third-party data from employers, sponsors, and collection agencies. The goal is to answer business questions about whether students paid their fees or how much they paid for each university course.
Continual Quality Improvement
During its implementation, GWU’s data integration process exposed a number of data quality issues that were the natural outcome of a distributed data ownership. Without an enterprise approach to data and analytics, it would have been difficult to investigate the nature and extent of data quality issues from its historical fragmented business intelligence system. Taking an enterprise approach has, as well, enabled GWU to improve data quality standards and procedures.
Wolf explains, “Data quality is an inevitable problem in any higher education establishment, because you have so many different people—lecturers, students, and administration staff—all entering data. With our system, we can find hidden data problems, wherever they are, and analyze the anomalies across all data sources. This helps build our trust and confidence in the data. It also speeds up the design phase because it overcomes the need to hand query the data to see what the quality is like.”
Connecting The Dots
Wolf and his team have not stopped here. As data emanating from social media has grown, they have designed their system so social data can be integrated just as easily as their traditional data sources including Oracle Financials, SunGard, SAP, and flat file data. Wolf says the SDM platform doesn’t turn its back on any type of data. By allowing the university to integrate any type of data, including social media, Wolf has been able to support key measures of academic performance, improving standards, and reducing costs. Ultimately, this is helping GWU maintain its business position as well as the University’s position especially as a magnet for the best students around the world.
In sum, the GWU analytics solution has helped it achieve the following business goals:
- Attract the best students
- Provide trusted reliable data for decision makers
- Enable more timely business decisions
- Increase achievement of academic and administrative goals
- Deliver new business insight by combining social media with existing data sources
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Thomas Davenport Book “Competing On Analytics”
Solution Brief: The Intelligent Data Platform
Author Twitter: @MylesSuer