Analytics is one of higher education’s top IT-related issues. Increased competition, accreditation, assessment and regulation are the major factors encouraging the adoption of analytics in higher education. Institutions need solid methods for campus BI/data reporting and analytics to support campus priorities and decision-making.
We’ve reached an inflection point where the maturation of analytics tools and the amount of data available have reached critical mass to engage in data informed solutions. Analytics can provide insight in areas such as reducing students’ time to degree, improving student learning outcomes, targeted recruitment, business process optimization, alumni relationship management, and increasing research productivity.
As the cost of higher education student recruitment rises, it becomes ever more important to retain students until they graduate, which will:
• Improve student learning outcomes, retention and graduation rates.
• Improve the institutional return on investment (ROI) on recruitment costs.
• Increase operational efficiency.
• Help the institution demonstrate success in a key area of focus for accrediting agencies and the Federal government.
Beyond the student life cycle, predictive analytics can be used across the academic enterprise – from advancement (“What’s the likelihood of an alumni subset making planned gifts or attending homecoming?”), to residential life (“If we make $x investment in dorm upgrades, will we recoup this through longer stays and higher rates?”), and to academic affairs (“Assuming current recruitment and retention rates, how many adjunct faculty members will we need in the College of Fine Arts in 4 years?”).
Analyze information on student enrollment, faculty, courses, finance, hiring/salary, facilities, for budget alignment, accreditation or resource forecasting.
Attract the right students, maximize retention and sustain strong relationships throughout the student life cycle.
Easily access and consolidate historical data on donors, alumni and prospects. Create predictive models that determine the likelihood of donor giving and target those most likely to give.
Quickly produce, view, interact with and interpret reports via the web or mobile devices, with IT maintaining control of the underlying data.