The New Data Curriculum: Moving from Theory to Hands-On AI Literacy with the Data Intelligence Project

Every enterprise leader knows the challenge: data literacy is uneven, governance is often learned too late, and AI has raised the stakes for getting data right from day one. In this rapidly evolving world, the "old" way of managing information is colliding with a new reality where AI agents and automated systems are only as effective as the data that fuels them. Yet most graduates still enter the workforce having learned about data…without ever having worked within the systems that govern, curate, and operationalize it.

That gap is exactly why Alation launched the Data Intelligence Project (DIP). The program makes Alation available to university faculty and students, giving them hands-on experience with the same platform used by leading enterprises. 

By introducing students to data products and giving them experience with the AI tools they will encounter in the workforce, we ensure they aren't just spectators of the AI revolution, but the architects of it.

At the center of this effort is a team that blends enterprise rigor with academic thinking. And at the center of that team is someone whose career has always been about one thing: making information meaningful.

A librarian’s path to data intelligence

Lianne Birkett didn’t come to data intelligence through a traditional technical path. She trained in English, taught for years, and eventually returned to what had always spoken to her: libraries and archives, starting her digital career implementing a search function for an archive of 1.2 million digital assets. Today, as Alation’s Senior Information Analyst and the Data Intelligence Project Program Lead, she plays an instrumental role in developing DIP course content, which introduces students to the world of enterprise data management through a lens she knows well: How do we best organize the world’s information?

Her archival training was deliberately split between the old and the new, working with physical manuscripts and cultural artifacts on one hand, and digital archives on the other. That dual perspective—respect for rigor, structure, and provenance, paired with a deep understanding of digital management and scale—now shapes how Lianne approaches the Data Intelligence Project. 

Where others might see raw enterprise information, she sees something closer to an uncataloged collection: raw material that needs context, standards, metadata, and curation before it can deliver value.

Turning theory into practice

When Lianne first began working on DIP materials, the student resources were intentionally lightweight. 

As the program has matured, Lianne has developed a library of structured learning materials that progress from foundational concepts (such as what data is and why ethics matter) to more advanced topics, including data products, operating models, and modern data architectures in the age of AI. 

Of course, reading about data is only part of the equation. “We have developed a framework of exercises that the Faculty can use as a supplementary course, or select the exercises that fit with their own programs of learning, so that students can build on foundational skills,” Lianne explains.

The approach is deliberate and mirrors business implementation. Students first learn the concept, then explore it in context, and finally apply it directly in the catalog.  “At Alation, we start any project with a business use case and consider how the user can be more productive and achieve better outcomes that solve issues, create revenue opportunities, or improve efficiency,” reveals Lianne. “We wanted a learning path that would train students to use our tools based on real-world use cases.”

By framing every project through the lens of a business question, students learn early on that data and AI are not just academic exercises; they are tools to drive tangible outcomes.

This structure also mirrors how data work happens in real organizations. By the time students complete these exercises, they haven’t just learned definitions—they’ve practiced data stewardship, governance, and collaboration in a live environment. This approach prepares students to work with data in real situations, enabling them to use data and AI features from the first day after graduation.

Expanding the curriculum for AI

As the data management field evolves, so does the curriculum. One of the most notable recent additions to the Data Intelligence Project is a dedicated focus on AI.

Lianne has led the buildout of a comprehensive AI content track. Rather than jumping straight into tools, the materials first ground students in the history and concepts behind AI. “It was important to give it some kind of academic depth, and we wanted to give students access to a small library of the actual research that matters. All thought leadership from Alation and the pivotal papers of the last 80 years are available to the students, without the hype or noise,” she points out.

“From there, the program is evolving toward hands-on AI-related exercises in Alation—connecting AI use cases back to trusted data, metadata, and governance.”

At Alation, we approach learning and AI in the same way. AI is built using a series of building blocks, or deliberate layers, starting with foundational data, structured metadata, controlled glossary definitions, governance, and an active knowledge layer that grounds intelligent systems before automation ever takes place. The Alation platform embeds AI functionality across each step, making the building process, from cataloging to building agents, seamless. 

Just as AI systems are developed iteratively, we also understand that learning is iterative,” shares Lianne. “We have reflected that in the suggested exercises framework where students build, refine, test, and improve, understanding not only how AI systems work, but how to ground AI responsibly.”

The goal is to help future data leaders understand how AI systems depend on well-curated, well-governed data, which often take the form of data products. 

Teaching data products

A cornerstone of the program’s hands-on learning is its approach to data products. Exercises are differentiated by level, reflecting the fact that students enter the program with different capabilities.

At earlier stages, students learn to define what a data product is and identify critical data elements. At more advanced stages, they prepare specifications and eventually build data products using modern tooling, all while considering governance, ownership, and quality throughout.

This includes hands-on guided activities where students explore the benefits of data products and why they are fundamental as modern data collections by learning, preparing, and building a data product. Integrated AI features are part of the workflow, but so is the harder question of what could go wrong. As Lianne puts it: "Students will know the legal frameworks and the theoretical ethical implications of AI, but will also know how to manage data within the platform, making them trusted and valued data leaders."

Crucially, the exercises don't stop when things work. Debugging is a core skill, and chaos engineering and testing principles push learners to treat governance and AI ethics not as abstract ideals, but as frameworks that come to life in practice.

Why this matters for enterprises (and what comes next)

For enterprise leaders, the Data Intelligence Project offers a glimpse of what the next generation of data and AI professionals could look like: interdisciplinary, governance-aware, and fluent in the realities of modern data platforms.

Alation's bet is that the future of data isn't just better tools. It's better people. And by investing in that future now, we're helping build a world where the next wave of data leaders doesn't just ask for better data—they know how to build it, govern it, and make it last.

Curious to learn more? Explore the Data Intelligence Project today.