Smaller colleges and universities often struggle to manage data effectively—trapped in spreadsheets, siloed systems, and manual workflows. The DevOps framework, long used in software development, provides a model for transforming higher education data practices. By focusing on flow, feedback, and continuous learning, institutions can streamline operations, improve trust in data, and build a sustainable data-informed culture.
Introduction: Data Is a Problem You Can Fix
If you work in institutional research, assessment, or academic administration, you’ve likely encountered the frustration of inconsistent reports, scattered data sources, and slow turnaround times. It’s not that institutions don’t value data—it’s that the infrastructure for managing it hasn’t kept up. Fortunately, a solution exists in a place you might not expect: software development. By borrowing ideas from the DevOps model, institutions can improve how data flows, how it’s validated, and how people learn to use it.
What Is DevOps?
DevOps is a framework originally created to improve collaboration between developers and IT operations. It emphasizes automation, rapid feedback, and iterative improvement. These same principles can dramatically improve institutional data practices. In The DevOps Handbook (2nd ed.), authors Gene Kim and colleagues outline three core principles: Flow, Feedback, and Continuous Learning. These ideas offer a practical roadmap for improving data work across IR, assessment, and administrative teams.
Three Core Principles of DevOps
Flow: Automate Data and Eliminate Bottlenecks
At many institutions, critical data lives in multiple systems—student records in Banner, survey results in Qualtrics, financials in ERP systems—and reports are assembled manually. The DevOps principle of Flow encourages us to automate and streamline this process. Start by creating a central data warehouse using tools like PostgreSQL or DuckDB. Use R or Python scripts to automatically extract, clean, and combine data from key systems. Reducing manual work not only improves accuracy, it frees up staff to focus on analysis and strategy.
Feedback: Build Trust Through Transparency
If stakeholders don’t trust your data, they won’t use it. DevOps encourages short feedback loops so issues are spotted and addressed early. Higher ed can apply this by building transparent dashboards in tools like Tableau or Power BI, documenting data sources and methods, and scheduling regular feedback sessions with users. You can also automate data quality checks to flag missing values or anomalies. When faculty and administrators see how data is validated and reported, trust increases—and so does use.
Continuous Learning: Make Data Skills Part of the Culture
Reliable data systems only matter if people know how to use them. DevOps cultures prioritize continuous learning and experimentation. For colleges and universities, that means offering basic training in SQL or R and encouraging teams to explore new ways of working with data. Create opportunities for staff to reflect, test new tools, and share lessons learned. Over time, this builds confidence and capability across the institution.
Conclusion: Start Small, Improve Continuously
Transforming your institution’s data practices doesn’t require a complete overhaul. Start with one report or one team. Focus on flow—automate what you can. Build feedback loops that surface issues and spark trust. Invest in learning so that staff can work with data confidently and creatively. These principles don’t just improve reporting—they make better decisions possible. And if you’d like help putting these ideas into action, we’re here for that, too.
Furman University’s Center for Innovative Leadership offers online Data Analytics for Education courses—R Scripting and Data Visualization—to help higher education professionals enhance their data skills using R. Innovate with the power of automation by learning how to convert work done in spreadsheets to using scripts that can be easily read, executed and shared. Learn more and register today.