In recent years, universities have faced unprecedented financial challenges, from declining enrollments, shrinking state appropriations, increased cost of goods, etc., all of which has been exacerbated by the COVID-19 pandemic. This has underscored the importance of data-driven decision-making in university budgeting and resource allocation. In this blog post, we will explore how data informs budgeting decisions, helping universities navigate through financial constraints and plan for a sustainable future, including restructuring an entire budget model.
The Emergence of Data-Driven Budgeting
The shift towards data-driven budgeting in higher education is a response to the growing need for transparency and accountability in financial management. I have been at the university long enough to see our budget go from asking a single person/office for money and either being told yes or not, to spending what is given to you however you need to do so with no questions asked on how the amount was derived, to budgets being pulled back in the middle of the year, to budgets decreasing and no one being happy, and frankly everywhere in between. The latter is a reality as the budget follows enrollment and many do not understand that. A data-driven approach involves leveraging data analytics to gain insights into spending patterns, enrollment trends, and operational efficiency and will not only show where the money has gone, but where it, perhaps, should be going. By analyzing the data, universities can make informed decisions about where to allocate resources, how to cut costs without compromising on quality, and how to optimize revenue streams. Of course, this is not an easy task and many times an unpopular task as it may expose past excess spending that needs to change, which is sometimes hard for some people to hear at times.
Case Studies
Several universities have successfully implemented data-driven budgeting models. Our university is currently in the middle of a budget model redesign, using data to allocate resources in an incentive-based model. Other institutions have used enrollment data to forecast future revenue and adjust their budgets accordingly, while others have analyzed departmental spending to identify areas where efficiencies can be gained. Here are a few case studies that highlight the potential of data to transform budgeting processes in higher education.
- McKinsey Case Study: A midsize liberal arts university facing a crisis adopted a data-driven approach to transform its budgeting and operations with the help of McKinsey. With the board leading the charge, they focused on student success and enrollment-driven revenue growth alongside cost management. By creating a centralized governance structure and using metric dashboards, the university achieved a 30% increase in its first-year class and revitalized its online and continuing education programs.
- Penn State University: Penn State developed a data-driven budget allocation model as part of a comprehensive effort to modernize the university’s budgeting approach. This model uses activity-based data such as student headcounts and credit hours, tuition, and research expenditures to inform budget allocations. It aims to create a clearer picture of the university’s overall revenue and costs to better align resources with Penn State’s mission and values.
- Carnegie Mellon University Library: In the case of the university’s library, activity-based costing (ABC) and other decision-making strategies were used to implement necessary budgetary cuts and reallocate resources effectively. This data-driven, ABC approach led to constructive organizational decision-making outcomes during a time of budgetary constraints.
- Post-Pandemic Student Success: The EDUCAUSE Review discusses how data analytics enabled campuses to support students in real-time during the pandemic. By leveraging data insights, universities improved student retention by pivoting services to remote support and investing in technology to connect online learners, proving the value of a data-informed approach in crisis times.
These examples highlight only a portion of the potential for the use of data to transform budgeting processes in higher education, from enhancing student recruitment and retention to streamlining library services and supporting students through unforeseen challenges like a global pandemic.
Strategies for Effective Data-Driven Budgeting
- Developing a Robust Data Infrastructure: Essential for collecting and analyzing relevant data. This involves investing in technology and training staff to effectively manage and interpret data.
- Engaging Stakeholders: Ensuring that all departments and units within the university understand and buy into the data-driven approach. This includes training staff in data literacy and creating a culture of data-informed decision-making.
- Scenario Planning: Using data to model various financial scenarios, helping universities prepare for different eventualities, including further economic downturns or changes in enrollment patterns.
- Monitoring and Evaluation: Continuously tracking the outcomes of budgeting decisions and adjusting strategies as needed. This ongoing process ensures that universities remain agile and responsive to changing circumstances.
Challenges and Considerations
While data-driven decision-making offers many benefits, it also comes with challenges. These include ensuring data accuracy, protecting student privacy, dealing with the rapidly evolving landscape of higher education, and most importantly, in my opinion, buy-in and transparency. Universities must navigate these challenges carefully to fully realize the benefits of a data-informed approach. Without buy-in and transparency, a shift in models will fail.
Conclusion
Data-driven/data-informed decision-making is a powerful tool for universities facing financial challenges. By leveraging data, institutions can make more informed decisions about budgeting and resource allocation, leading to greater financial sustainability and operational efficiency. As the higher education landscape continues to evolve, the importance of a data-driven approach will only grow.
To reshaping your budgets with data!
Brian M. Morgan
Chief Data Officer, Marshall University