3 reasons your colleagues don’t want to use data—and how to change their minds
Part one of a two-part series on data adoption
April 7, 2023, By James Cousins, Senior Strategic Leader, Data and Analytics
Has this ever happened to you? Your office spent weeks collecting data, building reports, and setting up a new dashboard for your colleagues. Several months down the road you look at the adoption rates, only to discover they are less than you had hoped.
Or perhaps this sounds more familiar: You’re in yet another meeting where one voice exclaims, “We need data!” only to have all other voices fall silent when someone asks for volunteers to actually investigate the data. The causes may vary but the end result is the same: despite significant efforts, some of your colleagues just won’t use data.
As a strategic leader for Rapid Insight, I spend most of my days working with IR staff and other data analysts to build impactful predictive models, create useful dashboards, and help them conduct high-impact analyses. As such, I often hear the analyst’s perspective on frustration about low adoption.
Below is what I’ve learned from those conversations. Read on for three common reasons people don’t want to engage with data, and ideas to win them over.
Three Reasons Your Colleagues Don’t Use Data
Reason 1: A “That’s Not My Problem” Mentality
Supporting a student through the entirety of their experience takes a village, as the saying goes. It’s hard to argue that any facet of the student’s career is isolated from the other facets. While most staff and faculty would agree with that sentiment, some may feel that they either have nothing to add to data initiatives or don’t need to concern themselves. The key is to help colleagues recognize that everyone has a role to play in data analysis.
Solution: Creating Strategic Partnerships
In our experience, it’s a fair bet that the perception of data falling “outside” of a department’s concerns comes down to thus-far-unearthed common ground. Wherever we are in our data project—collection, validation, analysis, modeling, reporting, or otherwise—we often think only of how it benefits our direct stakeholders. But as analysts, we’re well suited for the lateral thinking that might be required to help indirect stakeholders see that our work really does merit their interest.
Case Study
Longwood University sought to improve student retention using predictive models. They built a predictive model that provided an estimation of risk that an incoming student would end up on academic probation within their first semester. But many of us who have performed a similar analysis know that having the data does not immediately translate to actions that impacts change. To operationalize the insights, the analyst at Longwood formed a partnership with their librarians. A proactive librarian mentor program became a popular way to orient and support students at the institution, even expanding beyond the initially identified risk pool!
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