1. Alex Rudniy, Ph.D. Raymond Calluori, Ph.D. Perry Deess, Ph. D. Big Data Analytics for Institutional Effectiveness Presented at NJAIR 20th Annual Conference, Institutional Effectiveness: IE and IR. St. Peter’s University, Jersey City, NJ,4/4/2014.

2. Goals Simplify and automate IR activities Produce graphical and tabular reports in a user-friendly way For users with fewer technical skills within IR Self-serve report generation for other departments Simpler than Cognos The big picture for senior management Audit operational database data Overcome restrictions FERPA protects student data Many stakeholders don’t have access privileges Dynamic report generation overcomes complexity of large static reports

3. Technology Backend Microsoft SQL Server database Frontend designed in Microsoft Visual Studio Hosted on Windows Server Accessible from: Any platform via an internet browser Standalone desktop application

4. NJIT IRP Factbook

5. Past Reports as Adobe PDF

6. Current Reports as Excel Pivot Tables

7. Big Data Analytics In high demand by the industry and academia Scale differs by industry, e.g. bioinformatics vs. academics Features: Large scale of data Powerful servers are required for processing Components of a dashboard: Backend database Tabular representation Graphic representation

8. ETL Complications ETL = Extract, Transform, Load process ETL is needed to build a backend database Historical data is spread among multiple databases Data specifications lost/unknown Data need to be unified Attributes missing Attributes coded differently Attributes spread among multiple tables within the same database

9. The Dashboard More than 30 years of data Accurate: from 1982 Partial: 1957-1981 Multiple dimensions Tabular & graphical representation Overcomes FERPA restrictions by aggregating data Impossible to identify a person Privacy concerns Does not contain: names, SSNs,emails, etc. Access allowed for secured user accounts

10. Dashboard Main Screen

11. Dashboard Structure Consists of multiple tabs on the top Each tab contains a pivot table and a linked chart Pivot table has several areas: filter area, column headers, row headers, and data area Attributes can be moved between areas

12. Enrollment Tab, 1983-2013 This view of enrollment contains Student IDs in the data area Semester type and year in the row header area

13. Enrollment Tab, 1983-2013 (cont.) Added student level (U/G)

14. Enrollment Tab (continued) Past 5 years enrollment by level and attendance status

15. Bachelors Retention, 1988-2012 Cohorts Total retention by year

16. Bachelors Retention, 1988-2012 (cont.) Female vs. male retention

17. Bachelors Graduation, 1988-2007 cohorts Six-year full-time first-time undergraduates’ graduation rates By Ethnicity

18. Dashboard Screens Main dashboard (static) Dynamic: Applications Enrollment Bachelors Retention Masters Retention Bachelors Graduation Masters Graduation Awarded Degrees Ph.D. Retention Ph.D. Graduation