Boosting institutional research with data governance and self-service reporting
SAS provides fast and easy data access for a wide array of users.
to make better decisions
The University of Idaho invests in SAS® to give administrators access to trusted, insight-rich data
A department chair is curious about enrollment trends for a particular course. She opens her laptop, applies a few filters to an interactive dashboard, and within seconds is drilling down into 10 years of enrollment data to shape the future of the course.
Compare this to the old method. Before implementing SAS Visual Analytics, administrators at the University of Idaho (UI) had to submit their data requests to an analyst, who would attempt to extract the data from disparate sources, code a report by hand and return the requested info – often weeks later.
This illustrates a dramatic change in institutional research. By making data available via self-service reporting, the UI’s Institutional Effectiveness and Accreditation (IEA) team is giving decision makers a new lens into university data.
Our whole mission is helping students be successful in the university environment. SAS allows administrators to use their time more efficiently and focus more purposefully on what drives student success. Dale Pietrzak Director of Institutional Effectiveness and Accreditation University of Idaho
A new era begins
Idaho’s top research university has used SAS for more than 30 years. For decades, the IEA team relied on manual programming in Base SAS – and later, SAS® Enterprise Guide® – to fulfill data requests and compile mandatory reports.
But the need has grown for greater insight and self-service reporting. Today, university officials face greater accountability and fiercer competition for students, increasing their reliance on data and analytics to anticipate the future and respond proactively to changing trends.
Recruited to bring UI’s institutional research program into the modern era was Dale Pietrzak. Starting with a team of two, the IEA Director has overseen the expansion of his office, as well as the transformation of analytics at the university.
“Our whole mission is helping students be successful in the university environment,” Pietrzak says. “SAS allows administrators to use their time more efficiently and focus more purposefully on what drives student success.”
Data governance is critical
At the outset, data governance was a major issue for Pietrzak and his team. He describes a past where “data was a second thought and driven more by conceptual and theoretical kinds of decision making.” With little rigor around data usage, administrators were coming up with different conclusions using different data sets – data governance became critical.
Flash forward to the present, where data is now tightly managed by the IEA office. The team standardized the way it uses data across the institution and ensured uniform definitions across the university. Now the data is consistent across all kind of public dialogs and internal decision-making processes.
With executive support from the UI president and provost, the IEA office is now “the one truth.”
Getting the data right
As more administrators rely on data-based decision making, the University of Idaho is implementing a strong data governance strategy.
“Otherwise you have people coming to very different conclusions from very different datasets,” Pietrzak says.
Watch the video to the right, and hear more from Pietrzak in this full series of videos.
With a sound data governance program in place, the IEA team provides the institution a set of standardized data sets and visualizations of those data sets for a wide array of users, from those who were relatively unsophisticated with statistical processes to those who are somewhat advanced.
“We have staff members who have never been trained in multivariate analysis and don’t understand the difference between decision trees and logistic regression,” Pietrzak explains. “We needed to give them the insight without the analytical knowledge.”
The university invested in SAS Visual Analytics and SAS Visual Statistics to meet this need. Just three months after implementation, the IEA team was publishing a range of self-service dashboards, giving staff members access to insight-rich trend data on enrollment, admissions and student performance – information to do their jobs better.
“The majority of people really like not having to contact somebody for information,” Pietrzak says. “The trick has been giving them unique slice-and-dice capabilities without providing too much detail. Overall, it’s been a really positive growing experience for all.”
University of Idaho – Facts & Figures
Improvements to retention and recruitment
With fewer information requests rolling in, Pietrzak and his team have ramped up the complexity of their analytics projects – with great success. He offers two examples.
“It’s one thing to tell the advising group that academic ability has a great impact on student performance. They already know this. But when we can present that graphically to show them exactly how things like student retention and success rates are affected by high school GPA. For instance, it’s allowed them to focus their remediation efforts on students who are most likely to respond to help.
“Student aid is another area we’re examining,” he continues. “We’ve been heavily performance-based with our funding, rewarding the highest performers with financial aid. But by using analytics to model a shift to need-based aid, we discovered a way to enroll an additional 20 to 25 students who previously may not have been able to afford higher education, equating to roughly $500,000 a year.”
With many success stories to share, Pietrzak is happy with the university’s adoption of analytics. The introduction of self-service reporting has raised the level of questions coming into his office, which in turn, has raised the level of analytics coming from his team.
Pietrzak expects this flywheel effect to perpetuate the use of analytics. “Given our foray into more advanced analytics, I would expect it to become even more sophisticated in the years to come as we look at things like text analytics and word clouds.”
본 문서에 나오는 결과는 본 문서에 설명된 특정 상황, 비즈니스 모델, 데이터 입력 및 컴퓨팅 환경에 적합하게 되어 있습니다. 각 SAS 고객의 경험은 고유한 것으로, 비즈니스 및 기술적 변수에 따라 달라집니다. 따라서 모든 서술은 비전형적인 것이라는 점을 고려해야 합니다. 실제 절약, 결과 및 성능 특성은 개별 고객의 구성 및 조건에 따라 달라질 수 있습니다. SAS는 모든 고객이 비슷한 결과를 달성할 수 있다고 보증하거나 진술하지 않습니다. SAS 제품과 서비스에 대한 유일한 보증은 해당 제품 및 서비스에 대한 서면 계약의 보증서에 명시되어 있습니다. 본 문서의 어떠한 내용도 추가 보증을 구성하는 것으로 해석될 수 없습니다. 고객은 SAS 소프트웨어의 성공적인 구현에 따라 합의된 계약적 교환 또는 프로젝트 성공 요약의 일환으로 성공 사례를 SAS와 공유했습니다. 브랜드 및 제품 명칭은 각 기업의 상표입니다.