Page 6 - Campus Technology, June 2017
P. 6

Sponsored Report
Harness Institutional Intelligence
The data already in your campus can generate insights to solve higher ed’s biggest challenges
Challenges abound on campus —increasing tuitions, budget shortfalls, slowing enrollments, increasing access for
low-income students, and proving the value of a post- secondary education. One ongoing challenge rises above all others in most surveys of university and college leaders—getting more students to finish their degrees and do so in the appropriate number
of years.
The campus IT organization is increasingly involved
in the dynamics of student success and completion, along with the broad portfolio of institutional technology services, needs and concerns. The latest Educause survey pegged student success as the second most prominent issue for IT leaders (behind cybersecurity). The 2017 NMC-Horizon Report for global higher ed named the “college completion gap” as one of its six significant challenges. It was ranked as a “difficult” problem “for which solutions are elusive.”
 institution are able to tackle their issues. IT has a long  Apart from institutional and academic researchers and data scientists, departments outside of IT are often newer to this practice.
When users call, text or tweet to complain they can’t get access to Wi-Fi, for example, the help desk
knows to check wireless logs and similar machine- generated data (also called machine data) to resolve the problem. The same goes for resolving other challenges, such as cybersecurity issues.
These processes are typically awash in data
pouring in from multiple sources—networks, virtual machines, operating systems, web applications, security devices, databases, and other sources. IT often has difficulty managing this volume and variety of data. The data goes into silos, which greatly reduces the end-to-end visibility. The data often isn’t being isn’t being used to mine for insights.
More efficiently mining that data could have practical applications beyond IT issue resolutions, in helping colleges and universities address factors impacting student success. For example, Academic Affairs could use the same data to prioritize
the days and hours certain student services— resource labs, study groups, the writing center, the tutoring center—are open and available to help
More efficiently mining data has practical applications beyond
IT issue resolutions, in helping colleges and universities address student success issues.

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