AiThority Interview with GT Volpe, Senior Director of Product Management at Alation

GT Volpe, Senior Director of Product Management at Alation, chats about the role of AI in metadata management, decision-making and governance, trends in data intelligence, and key takeaways for CIOs and CDOs in this interview:

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Cloud migration is no longer optional. From your perspective, what’s the most overlooked aspect of cloud transformation when it comes to data management?

One of the most overlooked aspects of cloud transformation in data management is the failure to strategically prepare data for business and AI consumption.

In the rush to migrate, many organizations simply lift and shift their on-premise data—along with all its legacy baggage—into the cloud. While this may accelerate timelines, it doesn’t deliver meaningful value. The result is often a cloud environment filled with the same fragmented, poorly governed data that existed before, just in a new location.

What’s missed is the opportunity to rethink data in the cloud as a set of reusable, purpose-built, business-ready data products—assets that are not only governed and high-quality, but also designed with both current business needs and future AI interoperability in mind.

Without this intentional design, organizations find themselves needing to re-engineer large portions of their cloud data estate after the fact—delaying the very outcomes the cloud migration was meant to accelerate. To realize the full value of cloud transformation, data must be modernized, not just moved.

Also Read: Building Scalable AI-as-a-Service: The Architecture of Managed AI Solutions

How does Alation leverage metadata to enhance decision-making and governance?

Alation leverages metadata as a powerful lens to drive smarter decision-making and stronger data governance. By capturing how and where data is used, the processes it supports, and the context around its usage, Alation helps organizations make informed choices about how to govern and manage their data. For example, we use metadata to surface high-impact data assets—those most frequently used or supporting critical business processes—so teams can prioritize governance efforts and apply the right data quality checks where they matter most. This same metadata also enhances trust in decision-making: users consuming dashboards or reports can be alerted to upstream data issues or changes, ensuring transparency and confidence in the insights they rely on.

AI is transforming how we interact with data. What role does AI play in the future of metadata management and data governance?

AI is set to play a pivotal role in the future of metadata management and data governance by driving automation, improving accuracy, and aligning data practices with business goals. Here’s how:

Creating High-Quality Metadata: AI can automatically discover, classify, and tag data across systems, drastically reducing manual effort and inconsistency. By leveraging natural language processing and machine learning, AI ensures metadata is more complete, accurate, and contextually relevant—laying a strong foundation for data discovery, lineage, and compliance.

Orchestrating Governance Processes for Business Outcomes: Beyond metadata, AI can help orchestrate governance workflows—such as policy enforcement, data quality checks, and access controls—based on business context and usage patterns. This enables organizations to dynamically align data practices with strategic goals, ensuring that governance is not just about control, but about enabling trusted, value-driven decision-making.
Feeding on High-Fidelity Governed Data Products: AI thrives on high-quality, well-governed data. As organizations mature their data governance programs and produce trusted data products, AI models can consume this data with greater confidence. This creates a positive feedback loop: better data enables smarter AI, and smarter AI improves governance and metadata quality.
In short, AI will not only enhance how metadata is managed, it will become a core enabler of intelligent, adaptive, and outcome-driven data governance.

What’s a trend in data intelligence that’s being underestimated right now? What should organizations be paying more attention to?

A highly underestimated trend in data intelligence right now is the real implementation of well-structured, governed data products—not just the buzzword versions that surged in popularity and then faded or were watered down in execution.

Over the past few years, “data products” has become a hot topic, often tied to data mesh and domain ownership. But in many organizations, the hype didn’t translate into meaningful, scalable implementations. Instead, what emerged were loosely defined collections of datasets with minimal governance, poor metadata, limited reusability, and unclear ownership—hardly the kind of foundation needed to support enterprise-scale AI.

As AI adoption accelerates, this oversight is becoming a critical gap. While humans can navigate the nuance of schema quirks and interpret messy data relationships, AI systems can’t. AI requires high-quality, self-describing, governed data products—with clear semantics, strong metadata, data quality assurances, and business context baked in. These are not “nice to haves”; they are prerequisites for AI to deliver trustworthy, explainable, and repeatable results at scale.

Organizations should pay much closer attention to the structure and maturity of their data products—not just in name, but in practice. Those who’ve treated data products as foundational building blocks, with strong metadata, ownership, and governance, will be in a prime position to unlock real AI value. Those who haven’t may soon realize their AI ambitions are being held back by the very data infrastructure they thought was “good enough.”

Also Read: Edge Computing vs. Cloud AI: Striking the Right Balance for Enterprise AI Workloads

For CIOs and CDOs trying to future-proof their data strategies, what is one key takeaway you’d want them to remember?

For CIOs and CDOs trying to future-proof their data strategies, one key takeaway is this:

When building data products and governance processes, design not just for today’s business needs, but for tomorrow’s AI-driven landscape. That means ensuring your data assets are not only trusted, high-quality, and aligned to current use cases, but also interoperable, well-documented, and machine-actionable.

Business users need immediate value, but AI needs structured, standardized, and scalable foundations. If your data products aren’t built with that dual lens—serving both human understanding and future machine consumption—you risk creating technical debt that could stall AI adoption when it matters most.

The organizations that win in the AI era will be those whose data strategies are built for both usability and adaptability

Before we wrap up, share any upcoming innovations or big moves on the horizon at Alation.

At Alation, we’re building on our agentic data intelligence platform vision with several exciting innovations on the horizon. We’re developing purpose-driven data management agents that guide and automate end-to-end workflows, helping customers achieve measurable business impact. We’re also enhancing our core platform to improve interoperability—ensuring agents have the access they need to read, interact with, and take action across the catalog experience. In addition, we’re focused on delivering high-quality, consistent data and metadata from the catalog into downstream processes, supporting critical strategic and regulatory initiatives. Finally, we’re continuing to scale our data quality capabilities to enrich both the catalog and broader agentic use cases to ensure the right context on data is available at all times.

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