The WhereScape team recently attended the 2026 HEDW Conference in Austin, Texas, held April 26 – 29th, 2026. HEDW describes itself as a community focused on knowledge management in colleges and universities, including data warehouses, institutional reporting strategies and decision support. For us, that made it one of the most relevant events of the year for understanding what higher education data teams are actually working through in practice.
We left with a clearer view of the pressures facing colleges and universities: more source systems, more reporting demands, more AI ambition, more pressure to modernize and all too-often often the same lean teams trying their best to hold everything together.
That combination matters to WhereScape because higher education is one of the clearest examples of why data warehouse automation exists in the first place. Institutions need better analytics, but they do not always have the time, budget or headcount to hand-build every pipeline, model, job and document from scratch. WhereScape helps automate up to 95% of coding effort for data warehouse projects, reducing manual development while accelerating delivery.
Okay, enough of the product plug: here is what stood out to us at the event!
Higher Education Data Teams Are Still Solving the Same Core Problem
One of the strongest themes from HEDW was that the technology landscape keeps changing, but the central data problem remains much the same.
Higher education institutions are still trying to bring together information from student systems, HR, finance, research, admissions, advancement, learning platforms and operational systems. The surrounding language might change, now being focused around: AI, lakehouses, Microsoft Fabric, Snowflake, Workday and cloud modernization, but the underlying goal is still a trusted institutional data foundation.
In booth conversations, the same two application names came up repeatedly: Banner and Workday.
That was not surprising. Many institutions still rely on Banner or similar student information systems, while Workday continues to grow across HR, finance and student-related processes. The challenge is not simply having these platforms. The challenge is integrating them into a reliable higher education data warehouse that supports institutional research, operational analytics, compliance reporting and strategic decision-making.
In other words, the higher education data conversation is not just “Which platform are you on?” It is more like:
- Can we integrate our core institutional systems without rebuilding everything manually.
- Can we keep reporting stable while source systems change.
- Can we understand what our data means across departments.
- Can we support AI without exposing unreliable or poorly governed data.
- Can we modernize while still respecting the systems that already work.
That is exactly the space where WhereScape is built to help: source discovery, model-driven design, automated development, orchestration, lineage and documentation.
AI Is on the Agenda, But Data Readiness Comes First
AI was part of the conversation at HEDW, but not in a vague or abstract way. The more practical question was: “How do we prepare institutional data so AI can actually be trusted?”
That lines up with what we are seeing more broadly across higher education. EDUCAUSE has noted that AI conversations in higher education are no longer limited to pedagogy or student engagement. AI is increasingly reaching the operational heart of institutions: data systems, workflows and reporting infrastructure.
This is where many institutions face a hard truth. AI does not remove the need for clean, governed and explainable data… it actually increases the need for it.
If an institution wants to use AI for enrollment insight, student success analytics, financial planning, research administration or operational efficiency, the data foundation has to be reliable. If the underlying data is fragmented, undocumented or poorly governed, AI simply accelerates confusion.
We heard interest in tools like Copilot, Snowflake AI capabilities and other emerging AI workflows. But beneath that interest, the real requirement was more fundamental: institutions need integrated, trusted, well-documented data that can support both analytics and AI.
That is why data governance and lineage are not side topics. They are becoming central to AI readiness in higher education. EDUCAUSE has also framed AI in regulated institutional data environments as a test of the entire data governance and security philosophy. (EDUCAUSE)
Our takeaway: higher education AI will not be won by the institution with the flashiest AI demo. It will be won by the institution with the clearest data foundation.
On-Premises Is Still Very Real
Another theme from HEDW was that modernization doesn’t mean the same thing for every institution, far from it.
Some teams are moving to the cloud. Some are exploring Snowflake, Microsoft Fabric or other cloud data platforms. Some are still heavily on-premises. Many are somewhere in between.
This matters because higher education does not always move in clean, one-step modernization waves. Institutions often have long-standing platforms, limited resources, compliance constraints, complex reporting obligations and deeply embedded operational systems. That means modernization has to be practical, not ideological.
For WhereScape, this reinforces an important message: we do not believe modernization has to mean ripping everything out. Many institutions need to modernize incrementally, keeping valuable existing infrastructure while creating a clearer path to future cloud, hybrid or platform modernization.
That is why our support for traditional platforms like SQL Server and Oracle still matters, alongside newer platform investments such as Microsoft Fabric automation, Snowflake and other modern data platforms. At HEDW, SQL Server and Oracle were still part of the conversation, even when the booth visuals or wider market narrative leaned toward newer cloud platforms.
The lesson is simple: higher education teams need future-facing data architecture, but they also need help with the environments they actually run today.
Higher-Edu Has Its Own ‘Data Language’
One of the clearest reminders from HEDW was that higher education has its own vocabulary, operating model and reporting culture.
Institutional research teams, data warehouse teams, analytics teams and business stakeholders often talk about similar problems in higher-education-specific ways. A concept like “customer” does not ‘transfer’ cleanly when it comes to higher education. Institutions think in terms of students, applicants, faculty, programs, courses, terms, cohorts, enrollment, retention, completion, financial aid, research, advancement and accreditation.
That matters a lot for data modeling.
A generic data warehouse approach can get the technical structures right and still miss the language of the institution. If the data model does not reflect how the university understands itself, adoption suffers. If departments define the same concept differently, reporting becomes contested. If lineage is unclear, trust breaks down.
That is why higher education data projects are rarely just technical. They are semantic. They are organizational. They are about agreeing what things mean.
At WhereScape, we support this by helping teams move from discovery and profiling into repeatable design and automated build patterns. With WhereScape RED, teams can automate code generation, orchestration, documentation and lineage while still working within the business-specific structure their institution needs. RED provides end-to-end data automation for rapidly developing data infrastructure and business intelligence solutions, including native code generation, orchestration and automatic documentation.
Data Warehouses Are Still Central to Institutional Research
At HEDW, it was clear that institutional research remains a core driver of higher education data warehouse work.
That makes sense. Institutional research teams often sit close to the highest-value questions a college or university needs to answer:
- What is happening with enrollment?
- Which students are at risk?
- Which programs are growing or declining?
- How do financial, academic and operational patterns connect?
- What information is needed for accreditation, compliance or funding?
- How can leadership make better strategic decisions?
These questions cannot be answered well if data is trapped inside disconnected systems.
The data warehouse remains the place where institutions can bring those sources together, standardize definitions, preserve history and give analysts a shared foundation. For AI and advanced analytics, that foundation becomes even more important.
We have seen this before in higher education. For example, our higher-education customers such as Bucknell University and Cornell University used WhereScape to accelerate data warehouse delivery, improve scalability and reduce manual development effort. Bucknell created a new cloud data warehouse in 50% less time, while Cornell used WhereScape RED to automate and standardize data warehouse development.
The HEDW conversations reinforced the same point: higher education data teams are not chasing technology for its own sake. They are trying to deliver better institutional insight under real-world constraints.
Community and Word of Mouth Matter in Higher Education
Another thing that stood out is simply how much higher education data teams talk to each other.
At HEDW, attendees were not only evaluating sponsors or attending sessions. They were comparing experiences, asking peers what had worked, sharing warnings and pointing people toward tools or approaches they had seen succeed.
That peer-to-peer dynamic is especially important in higher education because institutional data teams often face similar challenges but with different levels of funding, staffing and platform maturity. A recommendation from another institution can carry more weight than a polished vendor message.
For WhereScape, that is a reminder that customer proof matters. It is not enough to say that automation can accelerate data warehouse delivery. Higher education teams want to understand what it looks like in a real institution, with real systems, real stakeholders and real constraints.
That is also why events like HEDW matter. They give teams space to have the conversations that do not always happen in a product demo: what broke, what worked, what surprised you, what would you do differently and what would you recommend to another institution starting the same journey?
The Best Higher Education Data Content Is Practical
One of our own sessions at HEDW reinforced something we already believed: technical audiences need practical, specific content.
High-level thought leadership has its place, but higher education data teams want to see how ideas apply to their own environments. They want examples … they want architectural patterns., they want to understand tradeoffs and above all they want to know whether a product or methodology can handle the messy, specific reality of their institution.
That has direct implications for our future higher education content.
We believe the strongest content for this audience will be industry-specific, scenario-based and grounded in real workflows. For example:
- How to integrate Banner and Workday data into a governed warehouse.
- How to modernize a SQL Server-based higher education data warehouse.
- How to prepare institutional research data for AI.
- How to build repeatable analytics patterns for enrollment, retention and student success.
- How to maintain lineage and documentation across changing source systems.
- How to move toward Microsoft Fabric, Snowflake or another cloud platform without breaking existing reporting.
This is also where our developing Industry Blueprint concept fits naturally. Higher education is a strong candidate for blueprint-style content because the sector has recognizable systems, shared reporting needs and common modernization challenges.
What Surprised Us!
Several things stood out after the event.
First, AI interest was real, but it was not detached from core data work. People were not simply asking, “How do we use AI?” They were asking, directly or indirectly, “How do we make our data good enough for AI?”
Second, the cloud story was mixed. Cloud modernization is clearly happening, but on-premises and legacy platforms are still very much present. Any vendor story that ignores SQL Server, Oracle or long-standing institutional systems is likely to miss a large part of the market.
Third, higher education teams care deeply about fit. A generic enterprise data message is less compelling than one that understands Banner, Workday, institutional research, student success, accreditation, enrollment and the realities of university data governance.
Fourth, existing customers may not always know the full set of WhereScape capabilities available to them. Conversations about documentation, lineage, platform support and future upgrades showed us that continued customer education is just as important as new lead generation.
Finally, the higher education community is highly connected. When someone has a good experience, people hear about it. When someone is struggling with a tool or partner, people hear about that too. That makes credibility especially valuable.
Why Higher Education Matters So Much to WhereScape
Higher education matters to WhereScape because the sector brings together many of the exact challenges our products were designed to solve.
Colleges and universities have complex source systems. They have strong reporting demands. They need governance, lineage and documentation. They often operate with lean teams. They need to modernize, but cannot afford disruption. They have growing AI ambitions, but need trusted data first.
That is a natural fit for data automation.
WhereScape helps institutions:
- Automate repetitive warehouse development work.
- Integrate diverse data sources more quickly.
- Build and evolve models with less manual coding.
- Generate native target-platform code.
- Maintain documentation and lineage as part of the build process.
- Support cloud, hybrid and on-premises modernization strategies.
- Reduce reliance on tribal knowledge.
- Give data teams more time to focus on institutional outcomes.
The value is not simply speed. It is speed with structure. Speed with governance. Speed with a design that can evolve as source systems, reporting demands and institutional priorities change.
Recap: Our Key Takeaways From HEDW 2026
HEDW 2026 confirmed something important for us: higher education data teams are not short on ambition. They are being asked to support more analytics, more modernization, more AI exploration and more strategic decision-making than ever before.
The challenge is that many institutions are being asked to do all of this while still managing long-standing systems, limited resources, fragmented data and heavy reporting obligations.
That is the gap WhereScape is fixated on helping close.
For higher education institutions, the next stage of data maturity will not come from adding one more dashboard or one more platform in isolation. It will come from building a stronger data foundation: one that connects core systems, captures institutional meaning, supports governance, automates repetitive development and gives teams the flexibility to evolve.
That was the message we heard at HEDW and it is also the work we are continuing to support.
To learn more, explore our higher education data automation solutions or read more about how WhereScape supports data warehouse automation for institutions looking to modernize, without adding unnecessary complexity.
