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

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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 1565 (2017) Alumni Worldwide 125,000

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

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

Maturity Model

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

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

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

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

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

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

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

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

Data Cookbook @ Villanova

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

Analytics Roadmap

Analytics Concept Architecture

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.

Data Pipeline

Open to the Floor Questions Comments

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