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What is the Real Value of Automating Your SIS Data Clean-up?

Written by Arnaud Friedel, Explorance. January 20, 2017

Ask yourself these questions: What is the true state of the data in my institution’s student information system (SIS)? How much do I trust that data to be correct and reliable during course evaluations?

If you’re responsible for course evaluation data preparation or for analyzing results, the sheer volume of information coupled with ‘dirty’ data can present some costly challenges. Think about the time you lose just preparing the data. Or the missed improvement opportunities whenever information is invalid or unavailable. For course evaluations to deliver strategic value (teaching effectiveness, curriculum development, faculty promotions, etc.) SIS data has to be accurate, complete, and kept up to date. Unfortunately, data management and maintenance continues to be an ongoing problem for institutions leaving little time for meaningful analysis and appropriate action. Below we explore the real value of automating your SIS data cleanup process prior to course evaluations.

The value of time:
If you could cut your data preparation process down by 30%, what would you do with that extra time? Manual data preparation time for course evaluations can take anywhere between 4-7 weeks. This time is usually spent chasing after people to confirm that the data they are responsible for is accurate and up to date. Wouldn’t you rather spend time analyzing results so that you can focus on strategic priorities?

Now, imagine a tool that seamlessly integrates with your SIS, centralizing control of the data preparation process. When used in conjunction with Blue course evaluations, the Data Integrity Gateway (DIG) enables you to manage and run multiple cleanup projects from within a single tool. With automated workflows, email notifications, and a follow-up engine, DIG decreases course evaluation data preparation time from seven to two weeks.
The Value of accountability:
Most SIS are built around the notion of self-discipline where every department or course coordinator is responsible for ensuring that their data is accurate and complete. In reality, the information can be updated too late or it doesn’t get done depending on the diligence of the person responsible. As a manual process, this requires an enormous amount of effort on the part of the individual responsible for preparing the data. Sometimes they can even be held accountable for the consequences of ‘dirty’ data.

The DIG system with its follow-up engine will ensure that data is assigned to and updated by the appropriate person well in advance of course evaluations. Regular notifications will give more visibility to the information faculty members are responsible for completing, increasing user engagement in the data preparation process. Now you can hold stakeholders accountable if they do not complete their data in a timely and accurate manner.
The value of maintenance:
Every institution should make SIS data integrity a long-term goal which requires proper monitoring and auditing. However, continuous data accuracy is one of the main challenges higher education institutions (HEIs) face. What if you have a ‘clean as you go’ system that helps eliminate long, tedious cleanup processes? As a proactive tool, DIG will ensure that modifications and changes are applied to your most current data, making it readily available. By maximizing data from all sources, the system ensures that course evaluations are run efficiently with pure data.
The value of analytics:
Higher education institutions (HEIs) that leverage analytics hold a competitive advantage. With recruitment expectations at an all-time high, HEIs are under constant pressure to improve their program offerings and services to attract and retain students. Insights from course evaluations can help improve student retention and engagement, predict student performance, and inform course and program design. The question is, how do you extract the kinds of useful, meaningful insights that result in competitive advantage if the data is incomplete or inaccurate?

That’s where DIG comes in. The system uses in-line validation to produce reliable and error-free records. Real-time integration and synchronization of data will enhance your system performance while automation will reduce manual errors. DIG doesn’t just clean your data, it maintain your data health – transforming the data you have into the data you need for strategic planning.