AI4RA Tip of the Week #23 

Video Spotlight: Peeking Through the Window at a Data Analyst 

Many research administration offices rely on data work every day, often without a clearly defined data role. In this session, AI4RA data scientists Nathan Layman (University of Idaho) and Nathan Wiggins (Southern Utah University) look at what data professionals actually do in research administration, and how offices can get more value from the data they already have. 

Watch the full session: https://www.youtube.com/watch?v=kBdaGSgPVsA

What the Session Covers 

  • Who does what. Data analysts tend to describe what has already happened, through reports and dashboards. Data scientists look forward, using forecasting, modeling, and building the pipelines data travels on. Developers build the tools and connections that make it all accessible. In small offices, one person often wears all three hats.
  • Where data people sit. A data specialist can be embedded directly in sponsored programs or work from a separate analytics unit. The presenters recommend having at least one embedded “data champion” who understands both RA processes and IT systems.
  • Two real-world examples. The first is a budget justification tool that links narrative values to the budget spreadsheet and uses AI to flag mismatches. The second is a live demo of an MCP server that connects an AI assistant to federal regulations, so answers come with sources you can check.
  • Data principles. Data work is the opposite of “leave no trace”: keep the raw data, record every change, and build tested, repeatable pipelines. The session also shows how the same numbers can tell opposite stories depending on how they’re grouped, which is why interpreting data takes human judgment. 

Why This Matters for Research Administrators 

Accurate reporting and compliance depend on decisions made long before a dashboard is built: how terms are defined, where data lives, and who owns it. The session’s core message applies to every office, with or without a data scientist on staff. AI and data tools should augment, not replace research administration expertise. They can bring you the sources, but the judgment call is still yours. 

How to Use This Resource 

  1. Watch it with your team. It runs about 50 minutes, and the case studies and Q&A work well as a team discussion starter.
  2. Find your hidden data talent. The presenters suggest your office likely already has a strong problem-solver acting as a bridge to IT. Campus interns, statisticians, and computer science students are also good sources of help.
  3. Start small. Write down definitions for key terms, identify who owns each dataset, and invest in data literacy across the office. 

How can I learn more about this topic? 

AI4RA Office Hours: every fourth Tuesday at 11 AM PST, with live Q&A and demos (https://ai4ra.uidaho.edu/#events) 

Share your questions or success stories 

How does your office handle data roles? Share your experience on the AI4RA community forum (https://groups.ai4ra.uidaho.edu/g/main/topics). Your tips help the whole research‑admin community thrive. 

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