Community college answers complex ‘why’ questions with data visualization and analytics


Reporting can reveal last year’s graduation rates or this semester’s completion rates at a local community college. But drilling further into that data to ask why students aren’t graduating or why they aren’t enrolling requires more complex analysis.

Karl Konsdorf, Director of Research, Analytics and Reporting at Sinclair Community College

At Sinclair Community College in Dayton, Ohio, college administrators and professors are using data visualization and analytics to improve completion rates, optimize class sizes and assist graduates with job placements that pay well.

Karl Konsdorf, Director of Research, Analytics and Reporting at Sinclair Community College says, “Every year, we see improvements in the number of students transferring in and earning degrees. And we are seeing fewer dropouts.”

To hear more about Sinclair’s use of analytics, read the full interview with Konsdorf below. 

How are you currently using data visualization and analytics at Sinclair?

Karl Konsdorf:  We have an enterprise reporting system that helped us move from decentralized to centralized reporting for everything from enrollment and academic affairs to compliance. We deployed a federated data warehouse to capture data from about 20 to 25 systems, and over time, layered data visualization and analytics on top of it to surface information for our users. People can use it to answer complex “Why?” questions (for example, why students dropped out of school), not just “What?” questions (such as how many students dropped out).

 Can you give us an example?  Currently we are looking into student success initiatives. We want to be able to look at students, identify at-risk students, and see how we can encourage them. What are some of the interventions we can do to help them complete? Our overall goal is to increase course completion rates as well as having students earning labor-market value credentials. So use data visualization and analytics to identify specific strategies that we can put into place to properly advise these students, as well as, to measure the outcomes of these programs as well.    

Ohio has moved to a performance-based funding model. How has data visualization and analytics helped with this?

Karl Konsdorf:  Yes, at Sinclair we have performance-based funding for the state of Ohio. Meaning we’re funded now not on student access but on student completions. So completions are more critical than ever before. Using data visualization, program managers, department chairs, and individual faculty can go in and look at and interact with their course data. They can see how their departments contribute to overall success, and they can see the impact of the completion rates on the overall formula. They can look at their individual courses or the department can look at their courses and see why some have a 100 percent completion rate and some have a maybe as low as a 20 or 30 percent rate. This drives questions: what are we doing well in those high-performing classes? Where are some of the opportunities in those lower-performing classes?

How has the use of data visualization and analytics aided your users and institution?

Karl Konsdorf:  Data Visualization and self-service reporting has opened up analytics to a whole new set of users – business users. They can take care of most needs themselves because it’s so easy to use and renders data so quickly. The colleges really started to see the value in having this information as it’s quick and accessible and it’s at their fingertips. As such, we are expanding - so in addition to deploying some tools around completion rates, we’ve also deployed a tool around classroom efficiency.  What we call average class size. So, with this, department chairs can better manage and meet their average class size targets.

At the institutional level, we’ve seen upward mobility in student success. Every year, we see improvements in the number of students transferring in and earning degrees. And we are seeing fewer dropouts.

Can you share what you think is important when creating a data informed culture?

Karl Konsdorf:  To cultivate an organizational culture that embraces data-informed decision making, you need to secure executive leadership for our data and analytical initiatives from the president and vice presidents all the way down to midlevel managers. They talk to employees about the need for using data for decision making, as well as model using it in meetings and other contexts.

Executive leadership from the top down is vital, as non-data people are often intimated by working with data and analytical reports themselves, which can hinder adoption. One way to address this issue is to use data to tell a story that people can relate to. You need to build a connection between users and the data. For example, a story could relate data analysis to student success so they can see how it provides the insights they need to improve student success.

It is also import to find internal champions who will embrace analytics and reporting and spread the good news. Balance this with proactive, systematic internal marketing. Meaning, putting on your marketing hat and developing a marketing plan to build awareness and a community around analytics. For instance, develop internal newsletters that inform people about new reports and data sets, new software and its capabilities, how to access what’s available using mobile devices, and how people in other departments are using analytics and visualizations to make better decisions.                          

What’s important in order to gain support for these data visualization and analytics projects?

Karl Konsdorf:  First, you have to make sure that the data is of high quality, so people will have to believe in it, have to trust it, and also make sure that they understand it. Number two, the data has to be presented in an easily consumable format. You don’t want just charts and graphs, or tables of numbers, you need something that’s meaningful, that’s targeted, and that department chairs or faculty can connect with. They want to be able to connect with the data, just like they would want to have a connection with a student. So we make sure that the visualizations that we present are connectable, as well as, actionable.

How are you able to find people with the right skills to accomplish these types of data visualization and analytics projects?

Karl Konsdorf:  Finding talent has been a challenge for us, because it’s a unique blend of skills that are needed. So we’re not looking for just the data analysis skills. We’re looking for a little bit of combination of the technical skills but also the people skills. So, for example, we ask the candidates, in the second round, do a presentation. We give them a mini-project and we ask them to do a kind of a presentation to show how they would show the results and communicate these results in a meaningful manner. What we’re really looking for is to see how well they can establish that connection with the audience. There are several programs in our region that have been able to generate analytical talent, but it’s not something that we just can go out and pick off a tree. It takes a while for us to find and cultivate that right talent. We get about 60 percent of what we need, and then we cultivate that rest of that talent in-house, so it takes some time to develop.

Why did you choose SAS?

Karl Konsdorf: We’ve been using SAS for over 10 years, and we chose to partner with SAS for several reasons. Number one, we viewed them, and a lot of other outside agencies would view SAS as kind of the leader in data mining and predictive analytics. So we really wanted to make sure that we were buying the best tool to accomplish those objectives. We were also looking for a reporting tool that could surface general reports, and allow us to do data visualizations, so we could present data in a visual format that’s easily consumable by the end users. And I think the icing on the cake for us was the fact that SAS has a tremendous history of being committed to higher education. So that was really important that we not only had a partner that understands us, but one that understands the challenges that we have and believes in the mission of higher education.

To learn more, read this whitepaper, Best Practices for Modernizing Enterprise Decision Making, featuring Karl Konsdorf. Also, the SAS higher education website has more information on how we partner with universities to help with their data, analytics and reporting needs. 

About Sinclair

Sinclair Community College is a public, nonprofit, comprehensive, non-residential community college based in downtown Dayton, Ohio. With our enrollment of 24,000 students, our single-campus college in downtown Dayton, Ohio, is among the largest community college campuses in America.


About Author

Georgia Mariani

Principal Product Marketing Manager

Georgia Mariani has spent nearly a quarter-century exploring and sharing how analytics can improve outcomes. As a Principal Industry Marketing Manager at analytics leader SAS, supporting the education industry, she passionately showcases customers using analytics to tackle important education issues and help students succeed. Georgia received her M.S. in Mathematics with a concentration in Statistics from the University of New Orleans.

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