What would it be like to work with educational data after graduation? This question is what initially piqued my curiosity about EVAAS.
The Educational Visualization and Analytics Solution (EVAAS) team at SAS is devoted to using data to help education professionals optimize students’ success. The software they develop can be used by educators to gain insights about individual students and by educational leaders to see trends in performance.
As a former K-12 math tutor, EVAAS intrigued me. I wanted to find out more about the team and what it would be like to work there. Institute alumni Brett Vogelsang ’22 and Chris Limer ’18 agreed to speak with me about their experiences on the ETL (Data Processing) team within EVAAS.
Julian: What do you do on a day-to-day basis in your current role?
Brett: In my role, we have a team huddle every morning for thirty minutes. You can think of this as a stand-up essentially where everyone on the ETL team goes through the work that they’ve done and what they’re expecting to have done for the day. And then we have a meeting right afterward with all of the EVAAS group at SAS, and it’s essentially the same kind of structure. It’s thirty minutes, and we give them an update on each client. Afterward, it can range from processing data, reading in new data, cleaning data, and responding to questions and Jira tickets. There’s been a lot of data validation as well.
Chris: There are two components of my role. I manage a data processing team. We deal primarily with education data. So, as a manager, my role is to make sure the data gets processed in a timely manner by those on my team. We’re trying to process it efficiently and with really high quality. So the data is then in a good format to send to our analytics team, to run models, perform research, and produce reporting. I try to help guide strategy for our team and the direction of the team as a whole.
I’m also a member of our team, and I do a lot of the data processing work myself for some clients. That involves receiving all sorts of education data in various formats, layouts, and file types. Then we begin processing it, bringing it in, investigating it, and asking questions of the client before producing a clean and usable data set for our analytics team.
Julian: What techniques and tools do you use on a regular basis for understanding, cleaning, and analyzing data?
Brett: On the ETL team, we use Base SAS 9.4, and there is the option, I believe, to use SAS Studio. We also have some Powershell code that we use to automate processes, and we use Git and GitHub for program version control. Also, there’s everyone’s favorite: Excel. Excel is the mastermind behind a lot of the data that we deal with. You can also count Jira as a tool as well. That’s a ticketing system that we use to track client questions or small pieces of the project.
Chris: Honestly, the biggest technique we use is curiosity. It sounds silly, but we know if we dig deep enough, we’re going to find something. If we look at everything at a surface value, we’ll likely get all of the variables that we would expect, but they may not have all the right values. So, we have to dig a little deeper. For tools, we primarily use SAS. There’s a lot of data step manipulation that we do. We use a lot of PROC SQL. I use a ton of PROC FREQ’s to look at everything. At a high level, those are the tools that we use.
Julian: Chris, I know that you were a high school math teacher before coming to the IAA. How has your experience as a teacher prepared you to work with educational data?
Chris: It’s certainly not something that’s required. I think there’s a lot of context that you get being a student. As a student, you have some sort of testing, which is the primary data that we deal with. Most places have some concept of a district, a school, and a grade. So, a lot of those things are pretty transferable, even just from your own education experience.
But I think there’s also some context beyond the basics that (coming from a teaching background) I didn’t have to pick up on. There are a ton of acronyms in education. I feel like knowing the acronyms and knowing the context is certainly helpful, but it’s not a requirement.
Julian: What are the unique challenges of working with educational data?
Brett: The unique challenges are that everybody has different data. For example, let’s say we’re trying to figure out all of the students who took a math test. There could be a ton of different names for these math subjects from different school districts. So, one issue that we are facing is how can we group a lot of these subjects together? Reading through a lot of that data is difficult.
Julian: What’s your favorite part about working on the EVAAS team?
Brett: For the ETL team specifically, everyone is super supportive and helpful. They want us to learn and would like the new people to be catalysts for change within the ETL group. There’s also room for growth. I wouldn’t be here if I didn’t think I could grow and learn from the people on the team. They have a lot of knowledge that I am more than happy to learn from. For EVAAS in general, everyone is very welcoming. I haven’t had a bad experience on the team yet.
Chris: There are two things that come to mind, and one is just the people that I get to work with. I really enjoy the team that we have and the people who I work with on a daily basis. I also really enjoy processing data. When I had the opportunity to be a part of this data processing team, it really spoke to me. You’re presented with this data in its rawest format. You get to ask a lot of questions. You get to dig into it and figure out what’s going on. That’s the part of the whole analytics life cycle that I enjoy the most.
I appreciate Brett and Chris for taking the time to share their insights on EVAAS with me!
Columnist: Julian Taylor