Over the past few months, this series has explored what it really takes to modernize data in higher education, not just by adding new tools, but by aligning people, processes and platforms around institutional goals. We began with mission alignment, and most recently focused on the foundation: governance, architecture, and quality. Modernization is not a single initiative or migration, but instead, it is a connected journey. Each part builds on the last, and each decision either strengthens the institution’s long-term flexibility or adds friction that will surface later.  

After you align on mission and establish a foundation, the next step is focusing on the technology layer of modernization: cloud adoption, SaaS growth, and the interoperability required to make a hybrid ecosystem work. The goal is not to chase new platforms, but to build an environment that can evolve over time without sacrificing security, trust, or ownership of institutional data.  

Higher education is facing a familiar modernization challenge but with new urgency. Institutions are no longer debating whether to modernize their technology stack, but rather how to modernize without breaking what already works, losing control of institutional data, or creating yet another patchwork of disconnected systems. 

That tension sits at the heart of modernization today. Institutions need agility, scalability, and innovation, but not at the expense of trust, security, or long-term flexibility. 

Cloud and SaaS adoption alone isn’t the goal. The real objective is a supportable technology ecosystem that enables high-quality data that drives action, insight, and impact without introducing vendor lock-in. Modernization isn’t a blanket “move everything to the cloud” initiative, nor is it a single migration event. It’s a strategic shift that unfolds over time driving operational efficiency as you progress. Interoperability is what makes that possible, especially in environments where on-prem, SaaS, and cloud systems must coexist. 

Most Campuses Are Already Hybrid 

In reality, most institutions already operate in a hybrid world—and likely will for some time: 

• Student Information Systems may still run on-prem 
• SaaS platforms support learning, CRM, and student engagement 
• Cloud data warehouses, lakes, or lakehouses are emerging to power analytics 
• Existing reporting tools must continue to serve core needs without missing a beat 
• On-prem shadow systems (visible and hidden) often fill gaps where central platforms fall short 

This is a normal state in higher education. Vendor contracts span years. Staffing constraints shape what is possible. Departments adopt tools independently to meet immediate needs. Compliance and privacy expectations remain non-negotiable. Modernization in this environment isn’t about replacing everything at once. It’s about making the ecosystem more intentional.  

That journey takes time, and institutions need partners and strategies that support movement across the on-prem-hybrid-SaaS continuum at their own pace. 

A smarter modernization goal is to build a flexible technology ecosystem—one where data can be accessed (or moved when appropriate) safely, consistently, and predictably across supportable systems. Just as important, modernization should preserve the freedom to change. That means minimizing dependency on any single vendor or platform and designing interoperability into the environment from the start. 

Interoperability in a Hybrid Ecosystem 

Interoperability is often described as a technical feature, but in practice it is a modernization outcome. It is the ability to share, move, and reuse institutional data across systems without constantly rebuilding integrations, re-litigating definitions, or losing control of access and governance. 

In a hybrid environment, interoperability matters because it protects long-term flexibility. It reduces the risk of “tool spawl.” It allows institutions to adopt SaaS where it makes sense while keeping data portable and governed. It also ensures that modernization does not simply create a new generation of disconnected pipelines. 

Rather than chasing tools, institutions benefit most by focusing on capabilities that make interoperability real: reliable data movement, shared understanding, secure access, and architectures that can adapt as the ecosystem evolves.  

The Building Blocks that Support Interoperability 

As institutions consider their options, it’s critical to focus on components that support Interoperability. Modern technology ecosystems typically include several complementary solutions. The value is not any one component alone, but in how they work together to create predictable, trustworthy data and long-term flexibility. 

• Cloud data warehouses, data lakes, and lakehouses (Snowflake, AWS, Azure, etc.): These platforms provide scalable, resilient environments for storing and analyzing institutional data. When designed intentionally, they support both structured reporting and advanced analytics while reducing infrastructure overhead and improving performance. More importantly, they create a stable destination where data from across the ecosystem can be standardized, governed, and reused. 
• ETL/ELT automation tools (Python, Alteryx, Invoke Learning, and iPaaS): Automation tools handle the movement, transformation, and validation of data as it flows between systems. They reduce manual effort, improve repeatability, and help ensure data quality and trust as pipelines grow in number and complexity. In higher education, where IT teams are often lean, automation is one of the most practical ways to scale modernization cost effectively. 
• API-first platforms: API-driven access enables data to be shared programmatically and securely across operational systems. This approach supports near–real-time use cases, improves interoperability, and reduces reliance on brittle, point-to-point integrations. APIs also help institutions avoid “data captivity” be ensuring that information can move across platforms as needs change. With modern AI agents as a co-pilot, untangling sometimes messy API calls can be more easily accomplished with added confidence.
• Domain-aligned data models: Organizing data by business concepts—such as students, courses, finances, or outcomes—rather than by source system structure makes data easier to understand and use. Domain-aligned models promote consistency across campus and reduce the cycle of renegotiating and rebuilding data logic every time a new system is added. They also make self-service analytics more achievable because data is structured around how the institution operates instead of how the vendor stores it. This combined with human readable names and data definitions help ensure that insights are meaningful across functional teams. 

Taken together, these components form the foundation of a modern data ecosystem—one that prioritizes alignment, trust, and adaptability over one-off integrations or short-term fixes. 

A Pragmatic Approach to Modernization 

Modernization can feel enormous and often overwhelming for CIOs and institutional leaders. The good news is that it doesn’t have to happen all at once. A more realistic approach is to modernize in layers, with a clear focus on outcomes. 

Step 1: Start with the Foundation
You can’t modernize data workflows without governance, shared definitions, and quality controls. Without them, institutions simply scale confusion. If the foundation is not in place, technology changes will not produce reliable, trusted results.  

Step 2: Choose a Flexible Integration Strategy
Select tools and patterns that allow data to move freely between platforms—rather than getting trapped inside them. The goal is to reduce point-to-point fragility, improve repeatability, and create a predictable way to connect systems as your ecosystem evolves. 

Step 3: Modernize Around High-Value Use Cases
Begin with mission-critical analytics needs such as enrollment, retention, finance, advising, and equity. Modernize the pipelines that directly support these outcomes first. Early wins build confidence and make it easier to sustain momentum. 

Step 4: Prioritize Composable Systems
Avoid architectures that force every solution into a single closed environment. Modern institutions need flexibility. Tools should plug into the ecosystem and support interoperability—not take it over. 

Throughout your modernization discussions, whether focused on cloud, SaaS, or on-prem, a key question should guide decision-making:

How do we modernize while maintaining ownership and control of our institutional data? 

That question matters long-term. Technology stacks will change. Vendors will evolve. Priorities will shift. A strong data strategy must outlast all of it. 

What Comes Next: From Dashboards to Action

Once institutions modernize their technology environments—and, more importantly, establish interoperability and trustworthy data pipelines—something powerful becomes possible: analytics become actionable. 

At that point, the focus shifts from delivering reports to enabling decisions. That will be the focus of the next post: Analytics and Intelligence: From Dashboards to Action—how institutions move beyond static reporting to predictive insight, real-time decision support, and a culture where data drives outcomes across campus. 

The point of modernization isn’t just better infrastructure, it’s better impact.