Miami University’s Modernization Initiative:
Evolving Toward an AI-Enabled Data Ecosystem
As institutions face increasing demands to modernize fragmented and outdated data systems, meaningful progress increasingly depends on establishing a strong technical and organizational foundation. One recent data modernization initiative at Miami University illustrates how a cloud-first infrastructure, scalable reporting environments, and deliberate preparation for AI adoption can work together to reshape enterprise data capabilities.
Using a flexible, non-exclusive implementation strategy built on commercially available platforms, the initiative aims to integrate data across systems such as HR, finance, and other core administrative domains. By streamlining internal data coordination and promoting more consistent, reliable access to analytics, the effort created a more unified data environment while positioning the university to explore AI and automation tools responsibly. The lessons emerging from this work offer a practical roadmap for institutions striving to align modern data architectures with long-term strategic goals.
Implementing a Cloud-First Infrastructure
Helping guide this work at Miami University is Randy Vollen, Director of Data & Business Intelligence, whose career has centered on building modern data platforms, strengthening governance, and improving decision-making in complex, highly regulated environments. Vollen has led data organizations across banking, engineering, and global legal services, delivering modernization programs tied to measurable business outcomes. His experience ranges from developing AI-enabled products at scale for Fragomen, to shaping the data strategy at Evoqua, and previously overseeing enterprise business intelligence (BI) capabilities, board-level reporting, and analytics for PNC’s technology portfolio. “Miami offered an opportunity to apply this experience in a different kind of environment, one where the institution needed to move from legacy, siloed systems to a modern, cloud-based platform aligned with Workday and Snowflake,” explains Vollen. “My role is to lead that shift, build governance, and create a foundation for AI and institutional insight.”
Since the University’s data environment relied on aging systems, fragmented transformations, scattered transformations, inconsistent definitions, and manual reconciliation, data modernization became a top priority. “Banner, legacy Oracle logic, spreadsheets, and departmental extracts created reliability issues and slowed decision making,” says Vollen. “The tipping point was the transition to Workday. An external consultant confirmed that we needed a stable integration layer for HR, finance, and student data while source systems were changing. They also identified gaps between our legacy reporting models and the structures required in a cloud ecosystem. Without modernization, the university risked falling behind on financial reporting, enrollment insight, compliance, and operational transparency. Modernization became essential to sustain core university functions.
“A cloud-first strategy gave us the scalability, automation, and resilience required to support Workday and modern analytics. Snowflake’s elastic compute and open architecture aligned with Miami’s long-term needs. Security and compliance were strengthened through role-based access, System for Cross-domain Identity Management (SCIM) identity integration, encryption, audit logging, and alignment to Family Educational Rights and Privacy Act (FERPA), Gramm–Leach–Bliley Act (GLBA), Health Insurance Portability and Accountability Act (HIPAA), General Data Protection Regulation (GDPR), and the Ohio Revised Code 1347. Infrastructure as Code provided consistent deployments and a defensible audit posture. Cost predictability improved through right sized compute scaling, automated resource management, and the planned retirement of aging on premise systems.”
Partnering with CBTS to Modernize Data Systems
Recognizing the need for modernization that respects existing systems, Miami University enlisted long-time EdgeMarket participant, CBTS, to help transform its data environment. “We partnered with CBTS because they understood our legacy environment and could help us modernize without losing continuity,” shares Vollen. “They had team members with prior experience at the University which gave them immediate insight into historical reporting patterns, business processes, and technical debt. That perspective allowed us to maintain our keep alive reporting while we built the new platform. CBTS also challenged us to think differently. They pushed us to view vendors as strategic partners rather than tool suppliers and encouraged us to evaluate buy versus build choices through a total cost of ownership lens. This helped us avoid short term fixes and prioritize architectures that would scale.”
CBTS supported the modernization process by recommending accelerators and providing data modeling expertise, while Miami University retained full ownership of governance, definitions, and institutional practices. “CBTS’s role has been to add speed, clarity, and industry practices but not to define Miami’s long-term policy or institutional data standards,” explains Vollen. “My guidance to others: choose a partner who brings context and is willing to challenge assumptions; keep governance and strategy internal; treat vendors as collaborators whose roadmaps influence long term success; bring total cost of ownership (TCO) into every architectural conversation. The partnership worked because CBTS added speed and healthy pressure while Miami kept control of direction and standards.” A key priority has been maintaining operational reporting continually through warehouse keep-alive efforts while building the modern platform in parallel. CBTS skills and talents were key in this approach.
“We partnered with CBTS because they understood our legacy environment and could help us modernize without losing continuity. They had team members with prior experience at the University which gave them immediate insight into historical reporting patterns, business processes, and technical debt. That perspective allowed us to maintain our keep alive reporting while we built the new platform. CBTS also challenged us to think differently. They pushed us to view vendors as strategic partners rather than tool suppliers and encouraged us to evaluate buy versus build choices through a total cost of ownership lens. This helped us avoid short term fixes and prioritize architectures that would scale.”
Improving AI Readiness
Building AI readiness starts with careful, disciplined data management, laying the groundwork for insights that are both reliable and responsible. “AI readiness is about creating the conditions for reliable outcomes, not rushing to deploy models,” says Vollen. “At Miami, this includes clean, modeled, and governed data in Snowflake; ingestion frameworks with schema detection, validation, and lineage; and consistent definitions and quality rules. We must also have policies covering access, privacy, retention, and lawful use; SCIM based identity integration and role aligned access; and a medallion architecture supporting Census, IPEDS, workforce, and financial modeling. Once these fundamentals are being established, the university will be positioned to pursue AI use cases such as forecasting, intelligent workflow support, anomaly detection, and advanced modeling.”
While AI readiness focuses on preparing data for advanced insights, the practical realities of system integration remain complex. Connecting HR and financial systems with analytics platforms is a persistent challenge in higher education. “Integrating Workday with legacy reporting required rethinking long standing logic,” shares Vollen. “Workday’s operational structure does not directly match analytic needs and historical Oracle and Banner models contained years of embedded exceptions. The external assessment confirmed several challenges including gaps between Workday structures and institutional reporting requirements, the need for standardized payload designs and CDC patterns, limited lineage for existing transformations, and data quality issues that impacted reconciliation. The main lesson is that operational data and analytic models serve different purposes. Modernization requires redesign rather than replication.”
Balancing the need for rigorous governance with the urgency of modernization required a thoughtful, integrated approach that allowed progress without compromise. “We advanced governance by embedding it into delivery,” notes Vollen. “Access controls, metadata, lineage, tagging, and ownership structures were built into the development process rather than deferred. Internally, the Data Coordination Group defined vocabulary, quality expectations, and accountability patterns while the platform work progressed. Governance and execution moved together which allowed us to maintain speed without compromising standards. The principle was clear: deliver quickly but never deliver without structure.” Miami’s modernization follows a phased approach, with current focus on establishing the technical foundation and warehouse operations while building toward future phases of automation, community training, and advanced analytics capabilities. While the technical foundation is in place, full deployment across all university functions remains ongoing work.
“My guidance to others: choose a partner who brings context and is willing to challenge assumptions; keep governance and strategy internal; treat vendors as collaborators whose roadmaps influence long term success; bring total cost of ownership (TCO) into every architectural conversation. The partnership worked because CBTS added speed and healthy pressure while Miami kept control of direction and standards.”
Creating a Successful Modernization Journey
Demonstrating the success of Miami’s data modernization involves tracking progress across reliability, quality, adoption, and impact. “Early indicators suggest reliability improves through automated ingestion, IaC deployments, and elastic scaling,” explains Vollen. “Quality improves through shared definitions, lineage, and consistent data quality rules. Adoption increases as more units rely on standardized data sets and Tableau dashboards supported by consistent business logic. Impact increases through better insight into enrollment, workforce planning, financial performance, and student outcomes. As the platform matures, unexpected gains include stronger cross campus collaboration and earlier alignment on AI use cases now that the platform is stable and governed. We also gained ROI on cost avoidance, moving to single stack MS or Oracle was determined cost prohibitive during the analysis phase.”
Modernizing data systems can be daunting, but Miami’s experience provides a model for building momentum and avoiding common pitfalls. “For institutions beginning their own modernization journeys, there are three recommendations that can help guide early success,” says Vollen. “First, establish a clear architecture on day one using modular Snowflake patterns, Infrastructure as Code, dbt pipelines, and identity integration for a stable foundation. Second, prioritize definitions and ownership because shared vocabulary resolves more issues than any tool. Lastly, treat AI as a later stage outcome because a secure, governed, automated environment creates the conditions for responsible AI adoption. The key to modernization is sequencing: stabilize, govern, then scale. By approaching the transformation process with structure and intention, an organization can build the foundation for responsible, scalable AI and successfully reshape their enterprise data capabilities.”
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