Tableau: It sounds like you get a lot of satisfaction in your job.
Robin: To be able to provide information to folks when they need it, as they need it is, as an institutional research professional, is actually really very rewarding for us because it's one of those things where, me personally, I take my IR job almost to heart way too much because I know that they depend on what we do to make their programs better.
Perhaps they're deciding not to do something anymore. Perhaps they're looking at what types of students to bring in or activities to actually start implementing. And were they successful? What tweaks do they need to make?
And so, personally, being a part of that story is very, very rewarding for me. I feel like I'm making a difference just in the little bitty things that we put together in the reports.
One of the most rewarding things is learning how we change processes to make it more efficient. I'm going to use our national survey of student engagement as an example.
The first time that we built that, they brought us four books of data and said, "Hey, can you help us get some meaning out of this?"
Tableau: How did you do that?
Robin: We did a question-by-question analysis because they had a lot of questions and they wanted to split it. We weren't really sure exactly what we were going to glean from that first instance.
Since then, fast forward, we have, through reshaping the data, learned that we can set a parameter and let them pick groups of questions, let them pick individual questions that's specific for their particular area.
Tableau: Can you explain?
Robin: So let's say the college of business likes questions one, two, seven, fourteen, and maybe the college of education likes these pieces, four, seven, eight, and thirteen. Whatever the case may be.
They can do a custom list of questions that go along with what the university goals are. Just them being able to have that custom report helps them do their job a little bit better. Knowing that we made that transition from the question-by-question, first iteration out and adding to our skills, listening to what they needed, making the tweaks, learning how to do things with the data a lot better.
We have given them a stronger report. And so, again, we're waiting for that conversation to say, "Okay, here's the next step, this is the next thing that we need to go with that."
Tableau: It sounds like you've really embraced all the things Tableau can help you with.
Robin: I consider myself to be really lucky. My whole team, all of us actually work with Tableau. And I am the luckiest person because they're just wonderful people to work with. They have embraced trying to use the tool to help the community, and they see the value in it.
Tableau: Can you give me an example of a Tableau success story?
Robin: We worked with our office of student success, particularly our dean of student success to find that daily retention rate.
Imagine that you have to report the first-time-in-college students to the federal government on retention rates on four-year, five-year, and six-year graduation rates. And so, naturally, she's going to be interested during that whole registration period how many people are returning in each of those groups and how they're progressing and what groups she actually needs to work with.
We got wind, actually, that she was going out and trying to find those groups of students herself. And, you know, you want to save the dean time. It's the one thing that we can do where we feel like we make a definite impact.
We put together a report that matches our daily enrollment file with our first-time retention file that we built, and set it up in such a way that she not only sees the retention rate, but she sees the student list, and she can filter down to a particular group to help her target certain areas that help her have communications.
The first-year experience folks that they went out, they were using the report as well. And we'd be walking across campus and we'd see them and we'd get the thumbs up, you know, 'cause they were loving the report.