Building a Data-Driven Institution: A Practical, Sustainable Approach
1 Introduction: You Can Fix Your Data—One Step at a Time
You don’t need a team of data scientists. You don’t need a massive budget. What you do need is:
- A leader who prioritizes this initiative by providing time, visibility, and resources.
- A dedicated person to manage the process—someone who understands institutional needs and has leadership support.
- IT or technical staff who are on board with building a centralized data warehouse—or the willingness to contract external support.
With these foundations in place, you can start addressing one specific problem at a time, gaining quick wins and building momentum over the next year.
Let’s get to work.
2 Steps you must take
2.1 Step 1: Leadership Must Set the Tone
The most common reason data initiatives fail? Lack of leadership support. If this is going to work, leadership must:
- Make it a priority—data improvements should be seen as a strategic investment, not just an IT or compliance project.
- Provide time and resources—institutional research and assessment staff need space to implement and refine new processes.
- Raise visibility—communicate why this effort matters for accreditation, retention, and institutional effectiveness.
- Stay engaged—this isn’t a one-time fix but an ongoing commitment to data-informed decision-making.
Action: Schedule a leadership meeting to set expectations, assign a responsible lead, and commit to supporting the process over the next year.
2.2 Step 2: Appoint a Project Owner
You need one person driving this initiative—someone who understands institutional data, can coordinate efforts, and has direct access to leadership. This person doesn’t need to be technical but should be:
- Respected within the institution—they should have the influence to push change.
- Organized and process-minded—tracking progress and keeping momentum is key.
- Curious about data—they don’t need to be an expert but should be eager to learn.
Action: Identify this person and give them the authority and time to make this happen. If you don’t have someone in-house, consider a contract consultant.
2.3 Step 3: Secure Technical Support
Your institution’s IT or technical team must be on board. If you lack in-house expertise, contract a data engineer or third-party consultant to help with setup and initial training.
They will be responsible for:
- Setting up a lightweight data warehouse (e.g., PostgreSQL, DuckDB)
- Automating data extraction from institutional systems (student information systems, learning management systems, financial aid platforms)
- Ensuring data security and governance
Action: Meet with internal IT staff (or find an external consultant) to discuss data infrastructure needs and initial implementation.
2.4 Step 4: Commit to education
As faculty and staff get involved in the project, you should commit to ongoing education. Many (or even most) of them should receive training in R, SQL, or Python, depending on the approach that you use to manipulating data. It need not be expensive, but the more empowered they are, the more they will use data to inform their decisions.
Action: Develop an initial plan for educating your staff. Know that it will evolve over time, but get the first wave of employees in training as soon as possible.
2.5 Step 5: Start with a High-Impact Report
Instead of trying to fix everything at once, choose one pain point that matters.
Action: Identify a report or analysis that is:
- Frustratingly manual (e.g., takes hours to compile every term)
- Critical for decision-making (e.g., retention tracking, course demand forecasting)
- Frequently used by leadership (so impact is immediately visible)
Once identified, this will be your pilot project.
2.6 Step 6: Identify & Extract the Data
Once you’ve chosen your first report, figure out where the data lives.
Action: Map out all sources (student information systems, faculty evaluations, financial aid reports) and determine how to extract data automatically.
- If the system supports direct queries (e.g., SQL database, LMS API), pull data automatically.
- If data is locked in spreadsheets, consider using R/Python scripts to extract and clean it.
- If extraction isn’t possible, start evaluating replacement technologies.
2.7 Step 7: Understand the Users & Their Needs
Who will use this report? What decisions does it inform?
Action: Conduct short interviews with key users to understand:
- What questions they need answered
- What actions they take based on the data
- What’s missing from the current report
This ensures your new report is useful and actionable.
2.8 Step 8: Build the Report the Right Way
Use structured, reproducible workflows to create the new report.
Action: - Store raw data in a centralized data warehouse (PostgreSQL, DuckDB) - Use SQL or R scripts for data transformations and cleaning - Design a dynamic dashboard (Tableau, Power BI, Google Data Studio) that pulls from live data
No more copy-pasting. No more version confusion. The report updates automatically.
2.9 Step 9: Review & Improve
Action: After launching the report, gather feedback.
- Is it answering the right questions?
- Does it save time?
- Are there any errors or gaps?
Adjust and refine as needed.
2.10 Step 10: Scale the Process
For the next 12 months, repeat steps 5–9 one report at a time.
Action: Select the next most important report and apply the same method. Each cycle builds expertise and efficiency within your team.
2.11 Step 11: Expand Institution-Wide
By the end of Year 1, several departments will have built confidence in structured data workflows. Now, expand efforts across the institution.
Action: Develop a two-year roadmap to:
- Transition all major reports into automated, reproducible workflows
- Expand database infrastructure to accommodate more data sources
- Continue training employees in SQL, R, and dashboard tools