1. Alice Faraone, Villanova University Dan McGee, Villanova University
Data with a Purpose: Finding the Right Formula for Data Driven Decision Making Success
Alice Faraone, Villanova University
Dan McGee, Villanova University

2. CPE Credits
To receive CPE credits for this session (if eligible), complete the CPE Attendance Form on the PABUG Annual Conference website
For additional questions please contact Jean Witinski CPE - Coordinator

3. Villanova University Who are we? Fast Facts (Fall 2018)
Founded in 1842
A private Augustinian Catholic College
About 12 miles outside Philadelphia
Fast Facts (Fall 2018)
Total Enrollment 11,000
Undergraduate 6500
Faculty Count 1061
Staff Count (2017)
Alumni Worldwide 125,000

4. Agenda 1 2 3 4 5 Analytics Initiative: Goals and Objectives History
Current 4
What’s Next 5
Take Away

5. Overall Objective: Exploit Analytics
Embrace data analytics and leverage information assets to provide data-driven, actionable insights that inform decision makers.
Data as a strategic University asset
Exploit new data insights to improve decision-making and business processes
Broaden the University culture and use of analytics
Invest in Data Governance structure
Easy, secure access to / visibility of data with new reporting and analytics tools
Learning analytics strategy to improve student outcomes
Enterprise infrastructure that is agile
Mature Analytics

6. Maturity Model

7. Analytics Initiative: Goals and Objectives
Data as Strategic University Asset
Self Service Analytics
Organization
Analytics Culture
Data Governance
Analytics Infrastructure

8. Analytics Initiative Goals Analytics Initiative Objectives
Data as Strategic University Asset
Data Governance
Define and establish a formal Data Governance structure by building on current governance.
Analytics Culture
Advocacy, communication, marketing.
Executive Leadership
Analytics Infrastructure
Review data integration strategy
Enterprise Analytics Platform
Reduce dependency on legacy tools like Discoverer
Self Service Analytics
Invest in a Self Service Data Portal
Training for analytics users and support staff
Organization
Funding, resources, structure, and time commitment
Mature Analytics

9. History
BI / Analytics distributed environment - processes, technologies, people
Key business areas for operational reporting and some analytics
Provost, Colleges, Institutional Research, Enrollment Management
Advancement / Alumni
Financial Services, Auxiliary Services, Athletics
Multiple data sources and data warehouses; difficult to leverage
Banner, Banner warehouse (legacy), Ellucian Analytics (ODS/EDW/APM)
Blackboard Analytics , Salesforce, Hobsons
Various tools for BI
Discoverer, Cognos, ODBC, MS Access, Excel, SQR
Tableau, Qlik, Power BI, Watson Analytics
Application analytics capability – ex. Slate, Salesforce, Blackboard, Cayuse
Staffing for BI in business areas, and IT
~35 positions across the University that have BI / Analytics responsibilities
ex. Enrollment Management – 7 positions, but equivalent of 2 FTE’s

10. User Pain Points Large packages with ambiguous metadata terms
Data workloads and number of data sources expected to increase over time
Use of Excel and legacy Discoverer tool is not optimal
Expand Data Sources
Delays in information delivery
Optimize User Experience
Disparity in numbers

11. Blackboard Analytics & Learn Banner Warehouse (SQR)
Current: Technology
Blackboard Analytics & Learn
Blackboard
Salesforce
Banner
Slate
Ellucian Analytics
Discoverer
Stage
Microsoft Access
Banner Warehouse (SQR)
ODS
Cognos
SQL/SQR
EDW
APM

12. Current: Analytics Committee
Enrollment Management
Communication & Marketing
Institutional Research
Athletics
Provost
Student Life
Advancement
Financial Affairs
Human Resources
Information Technology

13. Data Request Form
Data Requests were distributed with limited record of the request or resolution.
University Analytics Committee initiative
Working Group formed
Partnered with University Information Technology

14. Data Cookbook Data as strategic asset
Data Cookbook Workflow
Data as strategic asset
Move beyond traditional, reactive and silo-based data management
Managed – even predictive – approach
Data standards, documentation, quality, security
Value beyond analytics

15. Data Cookbook @ Villanova

16. Current: Data Governance
Data Governance Work Group
Representatives from Advancement, Athletics, Compliance, Enrollment Management, Finance, General Council, Library, OPIR, Provost, Research, UNIT
Discussed Scope and Objectives
Reviewed Data Governance Models
Reviewed peer institutions
Compiling benchmark
Reviewed current University committees/work groups and current University policies
Timeline: proposal/recommendations by end of 2018
Identify current/pressing data governance issues to be addressed

17. Analytics Roadmap

18. Analytics Concept Architecture

19. Data & Analytics Strategy Tips
Leadership is critical, especially in the business areas
Data/Information Governance
Change Advocacy & Leadership
Collaboration between business and IT
Data & Analytics is about culture, not technology
Everyone is going to have to change, and everyone is going to have to learn.
Collaboration between organizational units. To make this collaboration happen, business and IT must work together on vision, strategy, roles and metrics. Everyone is going to have to change, and everyone is going to have to learn.

20. Data Pipeline

21. Open to the Floor
Questions
Comments

22. Thank You
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