Date of Award

12-2024

Document Type

Project

Degree Name

Master of Science in Information Systems and Technology

Department

Information and Decision Sciences

First Reader/Committee Chair

Dr. Conrad Shayo

Abstract

In today’s competitive job market, career services in higher education are crucial for equipping students with the skills and readiness they need to succeed professionally. Yet, traditional career service models often fall short of addressing the evolving needs of students due to limited abilities in tracking engagement, evaluating program effectiveness, and predicting job market trends.

This study addresses three key research questions: Q1. How can BI tools improve data-driven decision-making within career services? Q2. What role does predictive analytics play in enhancing career success forecasting? Q3. How can BI tools assist in optimizing resource allocation within career centers?

The findings are: Q1. BI tools significantly enhance career services by consolidating data from multiple sources, allowing career centers to analyze student engagement trends, forecast employment outcomes, and make informed decisions about resource allocation. Q2. Predictive analytics, a standout feature of BI, empowers career services to anticipate and address student needs by identifying at-risk students and aligning programs with projected market demands. Q3. The integration of BI tools thus supports a proactive, student-centered approach, enabling career centers to align their offerings more closely with institutional objectives and job market trends.

In conclusion, BI tools offer transformative potential for career services, shifting support from reactive to proactive models that are both strategic and data driven. By embedding data-informed strategies into their operations, career centers are better positioned to prepare students for the demands of an

increasingly dynamic job market. Future research could expand on these findings by exploring cross-institutional applications of BI tools, addressing challenges like data integration and staff training, and examining ethical considerations around data privacy. As the capabilities of BI tools continue to evolve, there is also a need to refine predictive analytics models, further enhancing career services’ ability to forecast career success and provide relevant, timely support to students.

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