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This data visualization shows the traffic patterns of UA freshmen, as told by their CatCard usage, in a
select area of campus between 10 a.m. and 2 p.m. on a weekday. (Image courtesy of Sudha Ram)
Ram acknowledges

that there are privacy
BUILDING THE FUTURE YOU WANT
concerns when dealing
redictive analytics get even better the more data you have, Ram says. For her Smart
PCampus research, she hopes to eventually be able to incorporate UA Wi-Fi data from with individuals’
the 8,000 hubs on campus to get an even more accurate picture of students’ movement and
behavior. personal information.
Ram acknowledges that there are privacy concerns when dealing with individuals’
personal information. That’s why the CatCard data she collected were completely That’s why the CatCard
anonymized so that she could not personally identify individual students by name, ID
number or any other attribute. That information ultimately would be shared only with the data she collected were
students’ adviser.
“Almost every prediction we make is personalized — without knowing who the
individual is,” Ram says. completely anonymized
In the end, Ram believes the potential benefits — getting students the individualized
attention and support they need, while helping the institution meet its goals — make the so that she could not
process worthwhile.
The same goes for any industries leveraging the power of big data and predictive personally identify
analytics to make better decisions for themselves and those they serve.
“It’s all about thinking about the future,” Ram says. “It’s about planning for the future individual students.
and making sure you’re doing things in a way that enables the future to happen the way
you want it — for everyone’s benefit.”

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