Ultraviolet light, when dispersed through a black light, allows us to see beyond the spectrum of the light we’re used to seeing. Ultraviolet data* works the same way – it is the data that your company is probably capturing but might not be apparent at first glance. This data is not available for analysis or insight until you find a way to “see” it.
In many cases, ultraviolet data relates to customer or user behavior, whether online or more physical, and might be as simple as a click, a swipe, or a timestamp. Ultraviolet data holds the information you don’t know about your audience, and capturing it can lead to relevant insights. In some cases, you might be aware that this data exists (somewhere), but not sure of how to access it.
One great example of ultraviolet data is card swipe data. To get an idea of how ubiquitous card swipes can be, think of a college campus. In a typical day, a student might swipe their ID card to enter classroom building, eat meals, go the library, work out at the gym, attend an event, and make purchases. All of this data is probably being captured somewhere, but it might not be readily apparent throughout the whole of the organization. This was the case at Ball State University until they sat down for some creative brainstorming.
Researchers at Ball State were focusing on student success – in particular, they wanted to predict which students were not likely to return the following year. They focused on the data they had, like standardized test scores, high school GPA, intended major, gender, and other attributes they’d picked up during the application process. They extracted as much data out of the database as they could and started to think creatively about what else they could use to predict performance.
Based on past evidence that students who wake up earlier tend to perform better, they were already extracting 8:00am class enrollment, but they realized that this didn’t capture all early morning behavior. This triggered a light bulb moment for one of their researchers – it occurred to him that to get his own son out of bed in the morning, he fed him breakfast. Spurred on by this realization, he pulled swipe data for various places around campus, including the number of times a student ate breakfast in the cafeteria during a given semester. They tested this and other variables and found they were indicators of student success, and that the ultraviolet data improved their models.
If you haven’t already seen the webinar that Dr. Kay Bales from Ball State presented, you can check it out at this link. Ball State has discovered the power of their ultraviolet data, and it’s allowed them to better see how their students actions are correlated to retention.
*The term “ultraviolet data” was first coined by coined by Marshall Sponder, the author of Social Media Veera Predict: Effective Tools for Building, Interpreting, and Using Metrics.
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