Infographic
The Six Data Detractors Holding Your Campus Back – And How to Respond
Data has never been more vital to higher ed. As enrollments decline and financial pressure mounts, colleges and universities are using data to make sure budget dollars, faculty time, and other valuable resources are allocated to support both efficiency and the institution’s mission and goals.
As everyone on campus—from the president’s cabinet to faculty to advisors—incorporates data into their work, data teams are asked to create dashboards and other self-service solutions that democratize data access. But creating a culture of data-informed decisions requires more than just access. It requires basic data literacy among all data users as well as widespread trust in institutional data. This leaves IR, IT, and other campus data teams with a challenging question: How do we respond to staff who do not understand data or simply don’t want to use it?
EAB has synthesized its research to outline six “data detractor” personas. Explore this infographic to better understand the personas that commonly hinder your efforts to create a data-informed culture—and discover how to respond.
Detractors you'll encounter when they're
accessing data
Rogue Operators
Definition
Take it upon themselves to find a solution rather than using vetted data from IR or another campus data team
Resulting damage
Contributes to competing narratives (e.g., eight different figures for freshman enrollment)
Solution: Build trust
- Establish campus-wide commitments to central data strategy efforts
- Communicate data lineage to increase transparency
- Enfranchise units as data stewards to provide agency and accountability over shared data
Reverse Engineers
Definition
Start with their desired conclusion rather than seeing what the data tells them
Resulting damage
May offer unreliable conclusions
Solution: Create a common vocabulary
- Establish a data dictionary that uses clear language and thoughtfully groups similar or related terms
- Use the dictionary to identify the types of data found in different campus data tools and their primary use cases
- Distribute widely and update regularly
Wishful Thinkers
Definition
Have unrealistic expectations because they don’t know how to ask the right questions
Resulting damage
Need significant support to use data; do not track the correct data for effective analysis over time
Solution: Encourage exploration
- Coach data users to generate hypotheses and review results, iterating as needs and priorities change
- Train teams together so faculty and staff can use their local context knowledge to create meaning out of the data and less enthusiastic data-loving participants can learn from their savvier peers
Detractors you'll encounter when they're
interpreting data
Data Deniers
Definition
Question data validity to disengage from the conversation
Resulting damage
Fuel mistrust in campus data; shut down conversations immediately
Solution: Prepare well
- Define terms
- Explain methodology proactively
- Think about questions the audience might ask
- Be prepared to shut down debate that moves beyond the point of being useful
Unrelenting Unicorns
Definition
Use special circumstances to nullify comparison
Resulting damage
Leave current practice unquestioned, limit opportunities to learn from other groups
Solution: "Yes and...."
- Accept the suggestion to minimize defensiveness and then redirect or expand the narrative
- Reiterate that unique factors don’t change the commitment to quality data and conversation
Status Quo Champions
Definition
Reset expectations to avoid any need to improve further
Resulting damage
Stall progress, potentially encouraging the same change-avoidance mentality among peers
Solution: Benchmark broadly
- Draw attention outward
- Choose increasingly diverse sources for comparison to bring out new ideas and innovations
Learn more
Read the blog post, “Data Democratization 101: What higher ed leaders need to know” for a quick primer for data leaders
See how
The University of Montana’s “free the data” campaign expanded data access to break the cycle of indecision—and saved IR a month’s worth of work each year
See how
St. Ambrose University embedded data in their program review process to improve resource allocation, increasing transparency and saving them $446,000
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