As colleges and universities face increasingly uncertain times, more discussions are happening in the higher education space that require data preparation to inform outcomes. However, those familiar with programming are likely acquainted with the well-known adage, “garbage in, garbage out.” Being unable to trust an analysis of elements such as program margins or enrollment trends because you’re not confident in the data underlying that information can be a serious deterrent to meaningful change. While preparing data to get it ready for use with tools like Program Economic Analysis (PEA) might seem like a purely technical issue, addressing data reliability requires conversations that span departments and offer value across campus.
Why Does Data Preparation Matter for Higher Education Institutions?
Data analysis at colleges and universities is often intended to inform high-stakes discussions around strategic decision making in higher education, such as where to invest, where to redesign, and where difficult trade-offs may be necessary. Unfortunately, when there are quality concerns with the data being used for these analyses, the results can raise more questions than they answer. Leaders may have to spend time reconciling numbers, debating definitions, or questioning assumptions instead of focusing on strategy. Campus community members might dismiss recommendations based on this data because they don’t trust that the information is accurate. Rather than waiting until after a potentially flawed analysis is complete, schools should consider a proactive approach to data preparation.
Start With Shared Definitions
One of the most familiar challenges in data analysis, particularly those involving multiple campus stakeholders, is the absence of a shared understanding of what constitutes each different data element involved with decision making. For example, academic affairs, finance, enrollment management, and institutional research may each use different definitions for a “program,” depending on their reporting needs. Programs may be defined as degrees, majors, departments, or the CIP codes, and interdisciplinary offerings may span multiple departments and subject areas. If these definitions aren’t aligned before analysis begins, comparisons across programs become difficult and results may be misinterpreted. Gaining a clearer understanding of how different offices on campus understand the same terms can save hours of confusion down the road when a school is trying to consolidate data from multiple sources.
Why Is Aligning Academic, Enrollment, & Financial Data Important?
Meaningful insight into decisions at a school requires the integration of data that often resides in separate systems, such as:
- Enrollment, course schedules, and demographic data can live in a student information system (SIS).
- Faculty data may be tracked through workload models or payroll systems.
- Financial data is typically housed in enterprise resource planning (ERP) systems and organized around cost centers or responsibility units, which may be different from the functional definitions of programs or departments.
Aligning these data sources requires careful attention to identifiers, timing, and structure. Often, data elements require “crosswalks” to help ensure that a data field in one system is correctly matched to its equivalent in another system. Even though many of these might be obvious, it’s important to remember that not all offices at an institution use the same fields in the same ways. Working through this alignment doesn’t require perfect precision, but it does require transparency. The different stakeholders will need to be willing to spend the time to coordinate how their systems interact and make sure that everyone involved understands how various data elements interact.
Scrutinize Data Quality Early
An ideal time to correct issues with institutional data quality was probably a few years ago. However, an alternative option is to assess your data before you implement a tool or strategic plan that will rely on that data. Addressing data quality issues can be a daunting task, especially when resources are already stretched thin at higher education institutions. Whenever possible, tying data quality checks to processes related to that data is ideal. Examples of this might include:
- System transitions, i.e., moving from one student information system to another
- Heavy reporting requirements, such as an upcoming accreditation visit
- Onboarding of new staff who work with the data
It’s also important to be systematic about addressing data quality. Partial or ad-hoc data cleaning efforts can exacerbate already existing issues by adding new instances where data isn’t uniform in each field or a given system. Small and well-documented changes can make a real difference in turning messy data into reliable data.
Establish Data Governance & Documentation
Strong data governance is essential to help ensure data is trusted for use in school decision making. Knowing exactly who is responsible for which elements of the data can cut down on the confusion that arises when there are questions about a certain data field, but no one seems to know who put the data there. Equally important is documentation. Recording data sources, assumptions, and known limitations creates transparency and supports consistency over time. Almost every school has that staff member who has some system for understanding how certain data elements are tied together, but has not written down that information in a way that other people can understand. Moving toward a culture of clearer documentation that isn’t dependent on a particular person to know the state of the data is key to making sure data stays clean and usable into the future.
A Practical Way to Get Started
Institutions preparing to utilize their data for strategic decision making (including the use of the PEA tool) can benefit from a straightforward, structured approach to help get beyond the paralysis of messy data. Consider taking the following steps to get started:
- Inventory where key academic, enrollment, and financial data resides. These might include a SIS, human resources (HR) and finance systems, or an ERP system.
- Align definitions and reporting periods across systems.
- Corroborate data by applying specified procedures for completeness, consistency, and reasonableness.
- Document processes, decisions, and data ownership.
This approach provides some simple but important steps in getting your data to where you want it to be.
Investing in Data to Help Enable Better Decisions
Data preparation is not just something that you should consider when you have to bring on a new analytical tool or when facing important institutional decisions. It’s a strategic investment that can provide meaningful value to conversations and help establish trust from campus stakeholders. When institutions proactively take time to understand and clean their data, they’re better equipped to move quickly, build trust in results, and engage in meaningful strategic academic planning with that data as the foundation. With the rapid changes and disruptive environment that higher education finds itself in, knowing that your institution can at least trust its own data when facing those uncertainties is worth the time and effort to get there.
How Forvis Mazars Can Help
Our higher education consulting team has a deep understanding of the industry, drawing from roles as faculty, academic staff, CFOs, analysts, administrators, and internal auditors, to provide mission-aligned support rooted in real-world experience. We can employ powerful tools like PEA, data, and analytics to help schools prepare for what’s next. Interested in seeing how PEA can empower your institution with data-backed insights for strategic planning? Request a demo today or explore additional PEA use cases. If you have any questions or need assistance, please reach out to a professional at Forvis Mazars.