Data-Driven vs Data-Informed Campuses

Is your campus data-informed or data-driven? Or do you interchange the terms to mean the same thing? Understanding the differences between these terms can have a long-term impact on how a campus addresses equity and student success.

Examples:

A)
Data-driven decision-making:
- Visits to the math tutoring center are up. In addition, students are having longer tutoring sessions. Fantastic metrics! No changes needed.
Date-informed decision-making:
- Math tutoring center metrics are up. Does that mean the tutoring center is having a positive impact on math course success rates for students participating in tutoring? Additional investigation shows minimal impact.
What’s the strategy now?

B)
Data-driven decision-making:
- Enrollment is down by 15% compared to the previous year. We should increase marketing efforts via radio ads and increase outreach efforts to all feeder high schools.
Data-informed decision-making:
- Enrollment is down. Further investigation reveals that the Latina/o/x student population in the service area is 55%, and the college Latina/o/x student population is 27%. Furthermore, Latina/o/x student enrollment is down by 30% compared to the previous year and down roughly 15% with all other groups.
What’s the strategy now?

C)
Data-driven decision-making:
- The student success rate across all English courses at the college is 50%. Invest in supplemental instruction.
Data-informed decision-making:
- English success rates are low, but further investigation shows Pell recipients and other students from resource-poor backgrounds are struggling the most. Should the campus continue to focus on students as the problem? Is our standard and expensive tutoring strategy the best option to remedy these achievement gaps? Are there policies and practices that are hindering equity and student success?
What’s the strategy now? (See Equity in Grading as an example strategy).

Data-driven decision-making is important, but without further investigation and follow-up we might miss the critical elements at hand. The risk of a purely data-driven culture is that it can result in wasted time and expensive interventions that fail to address the underlying challenges. Data-driven decision-making can often look like this.


There's often a direct line from data analysis to standard go-to interventions. Data-informed decision-making is much more "colorful." Depending on the campus, it looks something like this.



A data-informed culture informs what critical questions need to be answered in order to get at the root of the problem. In addition, a data-informed culture can reduce complacency when the data “looks good” (math tutoring center example).

Asking the right questions after data analysis isn’t always easy so here’s a tip:

I have found that the campuses experiencing improvements in student success and reductions in equity gaps have managed to significantly reduce the amount of student blame and deficit-mindset thinking. Instead, these institutions often focus on policies and practices it can control and improve.  In short, after data analysis, practitioners ask critical questions to get at the core of institutional barriers which foster inequitable outcomes and ultimately hinder student success.

Also visit:

5 Questions to Answer Before Launching Initiatives

Equity in Grading

Instructional Practices Key Finding

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(A. Solano)