The Six Data Detractors Holding Your Campus Back – And How to Respond

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

“I actually found the data myself and made my own dashboard.”

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)

Reverse Engineers

"I need data to confirm that ___."

Definition

Start with their desired conclusion rather than seeing what the data tells them

Resulting damage

May offer unreliable conclusions

Wishful Thinkers

"I asked you x but I really meant y. Why doesn’t your analysis reflect this?”

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

Detractors you’ll encounter when they’re
interpreting data

Data Deniers

“These numbers aren’t right.”

Definition

Question data validity to disengage from the conversation

Resulting damage

Fuel mistrust in campus data; shut down conversations immediately

Unrelenting Unicorns

“Our situation is unique.”

Definition

Use special circumstances to nullify comparison

Resulting damage

Leave current practice unquestioned, limit opportunities to learn from other groups

Status Quo Champions

“Where we are is good enough.”

Definition

Reset expectations to avoid any need to improve further

Resulting damage

Stall progress, potentially encouraging the same change-avoidance mentality among peers


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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