Enterprise Data Lakehouse

Overview

Villanova University is embarking on an Enterprise Data Lakehouse initiative to modernize its institutional data and analytics environment. This project is a critical step in replacing the current Banner Analytics Operational Data Store (ODS) and Enterprise Data Warehouse (EDW), which are reaching end-of-life.

The new platform, powered by Snowflake’s Data Cloud, will provide a scalable, secure, and future-ready data foundation to support reporting, analytics, and AI-driven insights across the University.

This initiative aligns with Villanova’s strategic goals to improve data accessibility, data management, enhance decision-making, and ensure the long-term sustainability of enterprise analytics capabilities.

Overall Status: Pilot In Progress 

What is the Enterprise Data Lakehouse?

The Enterprise Data Lakehouse is a modern cloud-based data and analytics platform that combines the flexibility of a data lake with the performance and governance of a data warehouse. This enterprise analytics modernization initiative leverages Snowflake’s Data Cloud to serve as the centralized analytics environment for institutional data.

With the Enterprise Data Lakehouse:

  • Institutional data from systems such as Banner, Slate, Degree Works, and others will be consolidated into a single, standardized platform.

  • Users will gain faster, more reliable access to trusted data for reporting and analytics.

  • Data will be securely governed using role-based access, dynamic data masking, and compliance with regulatory standards.

  • The platform will scale easily as data volumes, users, and analytical needs grow.

  • The platform will enhance data management, analytics capabilities, integration flexibility, performance, and long-term sustainability for the University.

  • The University will be positioned to adopt advanced analytics, artificial intelligence, and machine learning capabilities in the future.

This transition also removes reliance on aging on-premises infrastructure and eliminates dependence on tools being phased out by Ellucian.

Why is Villanova Making the Change?

The current Banner Analytics Operational Data Store (ODS) and Enterprise Data Warehouse (EDW) environments face several critical challenges:

  • End of Life: Ellucian has announced maintenance-only support, with no future enhancements.

  • Critical Business Impact: The platform supports essential reporting and analytics across academic, financial, and administrative units. For example, the ODS/EDW serves as the main data source for the Enterprise Performance Management system used by Finance and for the Data Insights Platform administered by the Office of Decision Support and Data Integrity (ODSDI). The data insights platform is utilized for external-facing institutional reporting related to students and faculty, as well as supporting the Office of the Provost and the entire Academic Enterprise with their internal data reporting and analytics needs for operational planning and strategic decision-making.

  • On-Premises Infrastructure Limitations: Limited scalability, flexibility, and sustainability for modern analytics needs.

  • Urgency: A replacement is required before final product sunset dates are announced.

  • Growing Demand: The growing need to have analytics requires more flexible solutions

The Enterprise Data Lakehouse addresses these challenges by delivering a resilient, cloud-native analytics foundation.

Project Team

The Enterprise Data Lakehouse initiative is guided by a cross-functional committee representing UTS, Academic Affairs, Finance, Enrollment Management, Advancement, Human Resources, and other key stakeholders. This ensures that the platform meets enterprise-wide needs while maintaining strong data governance and integrity.

Executive Sponsor

  • Jason Hughes, Assistant Vice President, Enterprise Systems and Data Solutions

Project Management and Change Management 

  • Sponsor, Change Manager & Technical Lead: Toyin Joseph, Manager, Business Intelligence & Analytics Services 
  • Project Manager: Katherine (Kasia) David, Senior Enterprise Project Manager  

Project Operational Team

  • Ryan Chace, Manager of Information Security Operations
  • Matt Ferro, Enterprise Systems Cloud Engineer
  • Indira Gudala, Business Intelligence Analyst
  • Mike Kriwonos, Senior Manager, Linux Systems Administration
  • Gavin Printz, Senior Network Security Administrator, Information Security
  • Pete Palladino, Solutions Architect
  • Josh Poinsett, Executive Director, Cloud & Research Computing Systems
  • Doudou Tian, Application Architect
  • Julie Turnbull, Assistant Director of Enterprise Applications

Campus Partners

  • Academic Affairs 
  • Advancement 
  • Enrollment Management 
  • Facilities 
  • Office of Decision Support & Data Integrity 
  • Finance 
  • Human Resources 
  • Investment Office  
  • Payroll 
  • University Technology Services 

 

Timeline

 

Discovery Assessment (March 2025 – May 2025) 
Conducted a comprehensive assessment of the current analytics environment, including the existing (“As Is”) architecture, data management practices, data flows, and consumption patterns. Document the findings and evaluate potential platform solutions to address identified gaps and ensure alignment with strategic business objectives.

Security Review and Procurement Phase (June 2025 - October 2025)
Information Security, Procurement, and Legal teams have reviewed and approved the Snowflake platform and associated contracts.

Planning and Pilot Phase (November 2025 – January 2026)
The pilot phase establishes the initial Enterprise Data Lakehouse environment, validating the architecture, ingestion methods, and reporting capabilities. It will include loading and testing eighteen (18) high‑volume, critical Ellucian Banner tables, and confirming reliable connectivity from Snowflake into the Data Insights platform on Amazon Redshift. This phase also verifies Snowflake’s compatibility with Villanova’s existing analytics tools, including Argos and Power BI, to ensure smooth, integrated reporting across platforms.

 

Production Implementation Planning (December 2025 to January 2026) 

Complete a comprehensive inventory of Banner and ODS data required for operational reporting and analytics. Confirm that network connectivity and firewall configurations are validated to support secure communication with Snowflake. Set up Snowflake platforms, roles, and integrations to enable data ingestion.  

 

Implementation Phased Rollout and Adoption (January 2026 and Beyond) 

Migrate all identified Banner historical and operational data spanning Finance, Student, HR/Payroll, Financial Aid, and Advancement to Snowflake and ensure data accuracy and structural integrity. This migration supports institutional reporting, data insights platform by Heliocampus, Oracle Enterprise Performance Management (EPM) financial processes, and replacement of legacy ODS workloads. 

 

Assess and document all critical end-user reports used for daily operations, then methodically migrate existing Cognos and Argos reports and workloads to a new enterprise reporting platform. Partner closely with business stakeholders and end users to define requirements and design robust data models in Snowflake that support both operational reporting and decision-support analytics. On-board additional data sources (e.g. Degreeworks, Slate, etc.) where needed to support robust data analysis.  

Data domains and functional areas will be transitioned in phases to ensure continuity of reporting and minimize disruption. Training and change management will support adoption across campus. 

Frequently Asked Questions

 

What are the benefits of the Enterprise Data Lakehouse?

  • Modernized Platform
    • Cloud-based, scalable, and high-performance data environment
    • Real-time and near-realtime data availability 
  • Advanced Analytics
    • Supports various BI tools: e.g., Power BI, Tableau 
    • Enables AI/ML capabilities and trend analysis
  • Flexible Integration
    • Simplifies integration of new and emerging data sources
    • Reduces dependency on legacy on premise systems 
  • Strong Security & Governance
    • Regulatory compliance (HIPAA, PCI DSS, SOC 1/2) 
    • Dynamic data masking
    • Role-based access control 
  • Improved Performance & Cost Management
    • Elastic compute scaling 
    • Pay for use optimization
    • FinOps tools available to manage spend (Keebo, Bluesky, Foundational) 
  • Data Sharing and Collaboration
    • Secure and seamless cross-departmental and external data sharing 

What are the features of the platform? 

  • Core Capabilities
    • High-performance cloud compute
    • Unified structured & unstructured data storage
    • Standardized data architecture  
    • DevOps driven processes 
  • Key Platform Enhancements
    • Dynamic data masking 
    • Cross cloud capabilities
    • AI driven insights 
    • Robust integration ecosystem using Fivetran
  • Data Governance & User Experience
    • Consistent access control across departments
    • Simplified data consumption 
    • Streamlined BI reporting and dashboards