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Small colleges and academic units often believe robust data strategies are out of reach—too expensive or complex. But ignoring data problems leads to wasted time, inconsistent reporting, and missed opportunities. This article outlines a simple, focused approach for building a sustainable, reproducible data strategy—starting with just one report and growing from there.
Introduction: You Can Fix Your Data—One Step at a Time
You don’t need a team of data scientists or a big technology budget to make real progress with your institution’s data. What you do need is a champion in leadership, a clear starting point, and a team willing to grow. The key is not to solve everything at once—it’s to pick one place to start and build from there.
A Practical Roadmap: 11 Steps to Better Data 
1. Leadership Sets the Tone
Progress begins when leadership names this effort a strategic priority—not just a compliance requirement or tech upgrade. With visible support and clear expectations, staff can align their efforts and invest time in lasting change.
2. Designate a Project Owner
Assign one person to coordinate the work, track progress, and keep it moving forward. They don’t have to be a data expert, but they do need time, support, and a curiosity about how data can help.
3. Secure Technical Help
You’ll need someone—staff or contractor—who can help set up a basic database, automate data pulls, and advise on technical tools. Think small: a cloud-hosted PostgreSQL database and lightweight scripting tools are often enough to start.
4. Start Staff Education Early
Empower your team with training in tools like R, SQL, or Python. A little know-how goes a long way when it comes to transforming manual reports into automated, repeatable workflows.
5. Choose One High-Impact Report
Pick one report that matters—a frustratingly manual process, used regularly by leadership, and important to institutional goals like retention or course planning. This is your pilot project.
6. Find the Right Data Sources
Once you’ve chosen your report, identify where the data lives and how to access it. If your SIS or LMS supports queries, great. If not, simple scripts can help extract spreadsheet-based data reliably and repeatedly.
7. Understand the Users
Interview the people who rely on this report. Ask what questions they need answered and how they use the information. This ensures the end result will be relevant and helpful.
8. Build with Reproducibility in Mind
Use structured scripts to clean and analyze the data, and store it in a centralized location. Then build a live dashboard or regularly updated report—no more copy-pasting or version control issues.
9. Gather Feedback and Adjust
Launch the report, then ask users what works and what needs tweaking. Treat this as a cycle: adjust, improve, and learn.
10. Repeat and Build Momentum
Apply this same approach to the next report, and the next. Within a year, your team will have improved multiple key workflows and developed stronger technical capacity.
11. Expand Thoughtfully
By the second year, aim to transition core reporting into structured workflows. Continue staff development, grow your data infrastructure, and share wins to sustain buy-in.
Final Thoughts
You don’t have to fix everything at once. Just start with one problem, solve it the right way, and grow from there. Structured, reproducible workflows are doable—and once they take root, they make everything easier.
Let this be the year your institution gets a handle on its data.
Let’s get to work.
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.
