Utilizing data analytics to improve performance

Despite higher education’s current focus on operational performance, efficiency and effectiveness, many institutions rely on techniques that provide little to no quantifiable insights into performance improvement. To generate measurable results that also support mission achievement, colleges and universities are turning to data analytics. Higher ed data analyticsData analytics yields meaningful patterns in data — financial and nonfinancial — that can describe performance, and predict and guide improvements. Analytics is a multidimensional discipline that simultaneously applies statistics, computer programming and operations research to quantify performance and provide a clearer picture of what is working and where improvement is needed. Analytics can help you validate trends, pinpoint root causes of existing issues and take a comprehensive analytical overview of institutional performance as a whole — both within academic programs and underlying business operations. More importantly, analytics can be predictive as well as real-time, illustrating future possibilities to enable decisions about transforming performance.
Data analytics is a continuous, iterative exploration and investigation of past business performance to gain insight and drive business planning for the future. The results of data analytics can be used to streamline operations; increase cost efficiency; determine and optimize financial margin by department/student type; model and forecast performance (e.g., student trends, resource needs and revenue expectations); improve the budgeting process; and enhance overall mission effectiveness. With a true understanding of business performance based on data, statistical methods and predictive modeling, data analytics allows management to concentrate on the fundamental objectives of the institution and find ways to enhance mission achievement.
Steps to instituting data analytics
Prepare your institution for optimal results from data analytics:
• Invest in technology (e.g., data warehouse and analytical tools) to capture desired data and create correlations.
• Establish performance metrics beyond financial measures and agree on mission-driven indicators.
• Acknowledge the human element and the importance of effective change management.
• Build collaboration into the process in order to view the institution as a whole rather than as a collection of departments and interests.
• Develop or acquire personnel to bring data analytics and business intelligence skills into your institution.
• Commit to act on the trends and insights discovered through data analytics.
• Create a cross-functional steering committee that can set aside other biases in order to act on the analysis.
• Dedicate institution-wide focus to an ongoing data analytics program, as opposed to conducting a one-time exercis
Results can be financial, academic and sustainableAs an example of how data analytics can be used to enhance a university’s operations, contrast how classroom space planning has traditionally been done — assigning blocks of classrooms to department chairs based on faculty requests — and an enhanced approach that effectively allocates classrooms by leveraging metrics through a cross-functional analysis. In the latter approach, the metrics are analyzed according to factors such as cost-to-educate, margin by student type and program, under- and overcapacity departments, faculty availability, course popularity, core requirement or elective, building usage in nonpeak times, and the history of oversubscribed or undersubscribed sessions. These factors are brought together in a solution to identify inefficient use of physical space and as a response to board requests for space utilization studies before upgrading or building additional facilities.

Beyond reducing the cost to educate, performance improvement can also be measured by mission-related factors such as increased number of students served, improved on-time graduation rates due to course availability, expanded investments in learning and teaching technology, and increased allocation of research space, along with many other nonfinancial factors.

Data analytics can assist institutions that are struggling with their bottom-line financial performance. However, data analytics can also benefit those institutions that are financially stable by illuminating budget-neutral changes that can deliver enhanced educational outcomes. Financial performance as measured by operating margins only tells half the story; enhanced operational performance that has a significant impact on educational outcomes tells another story altogether. Maintaining a 5% or 10% operating margin is a financial measure; doing so while expanding program offerings, initiating degree programs or improving four-year graduation rates is a performance improvement measure.

Heightened interest in student outcomes is another driving force in data analytics. Constituents are demanding proof of educational outcomes, including student retention and graduation rates, and post-graduation job placements. Institutions that can illustrate success in these areas are at a significant competitive advantage.

Assessing and measuring budget trade-offs related to investments in specific intervention and milestone-driven initiatives is an area where data analytics shines. Data is aggregated, analyzed and explored across many dimensions, including at-risk students’ demographic and academic profiles (e.g., major, and full/part time, residential/nonresidential and employment status), as well as programmatic factors and programs (e.g., academic and career advisement, college readiness bridge programs, access to technology-enabled classrooms and interactive learning tools). Analytical models can be developed to compare past performance based on at-risk student retention and graduation, with predictions of future performance based on changes to specific criteria and programs. Scrutiny of academic performance and investments in new academic programs amidst the challenges of a competitive marketplace are prompting institutions to make decisions that are fact-based, satisfying strong faculty senates and governing bodies, which increasingly require a data-driven focus. Institutions continue to strive to understand their current academic model by looking at traditional metrics such as faculty productivity. But to deliver truly improved performance, they must consider additional data, including research quality; faculty tenure status; faculty salary-cost-to-revenue ratios (including adjuncts) by major; the ratio of sponsored research-to-release time by school, department and major; available teaching hours; enrollment patterns and oversubscribed courses; and the ratio of release time for curriculum innovation as compared to research. With so many factors to assimilate, a holistic and objective fact-based approach to data analytics can prepare an institution to navigate difficult — often political — waters and enhance performance in a financially sustainable manner.

State of Higher Ed 2016 - University management model
In analytics, human factors are as integral as dataTo perform meaningful analytics, databases and technology tools are critical. However, this is not simply an IT exercise — at its core, data analytics involves people and their commitment to the mission. It is essential to involve a variety of perspectives to ensure fair representation of stakeholders. Input must be sought about what data should be gathered and analyzed, how analytics will be interpreted and converted to performance insights, how decisions will be derived from the business insights, and how opinions about facts and data will be synthesized into action.

In the end, data analytics comes down to the human element. University leadership needs to champion the desire for a better, more holistic decision-making process based on an understanding of the relationships between business drivers and their outputs. The traditional analysis functions of departments such as Institutional Research, Finance, Academic Affairs, Student Affairs and Office of Sponsored Research need to be reassessed, and new skills introduced for analyzing different types of data. These changes require leadership and institutional commitment to success. The highly competitive nature of higher education, increased scrutiny by stakeholders, changing demographics of the nation and tuition/fee sensitivity create an environment in which improving financial and nonfinancial outcomes cannot be achieved based solely on the conventions of student selectivity and tuition increases. The true power of data analytics lies in establishing a dedicated, ongoing program that enables higher education institutions to gain insights into their operations and to improve performance into the future.

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The State of Higher Education in 2016