How Verizon Built a Thriving Data Product Economy with Alation

In today’s complex enterprise landscape, organizations are increasingly turning to data products to transform raw data into trusted, reusable assets that drive business value. At the Gartner Data & Analytics Summit, Latheef Syed, Associate Vice President, Data and Analytics at Verizon, joined Steve Wooledge, VP of Product Marketing at Alation, to share how Verizon is building a scalable data product economy — and how Alation helps make it possible.

This case study captures the highlights from their conversation and offers actionable insights for data leaders pursuing a data product operating model.

Verizon’s data & AI journey: From fragmented data to connected intelligence

As a $100+ billion enterprise, telecommunications provider Verizon operates across multiple business units, including wireless and network operations. But Verizon’s scale comes with a challenge: fragmentation. Decades of mergers and acquisitions have left each business unit functioning as a standalone enterprise, with its own processes, systems, and data definitions.

“Verizon is made up of a lot of acquisitions and mergers… Every business unit is its own enterprise.” — Latheef Syed

This fragmented data landscape made it difficult for Verizon to gain a unified, trusted view of data across the organization — a critical barrier to driving insights and innovation.

Over the past two decades, Verizon’s data strategy has evolved in step with technological progress. The company’s journey has moved from basic descriptive analytics to predictive and prescriptive insights, and now to GenAI-powered automation. Today, AI plays a pivotal role in Verizon’s business, driving innovation across:

• Customer and employee experiences — from personalized recommendations to proactive customer service.

• Products and services — embedding AI into the network and digital platforms.

• The connected data and AI ecosystem — aligning data, metadata, and AI models across business units.

“We have been focusing heavily on Gen AI-related products… looking at all aspects of business where we can bring our unique difference, we can monetize on it, building solutions that support our employees as well as our customers.” — Latheef Syed

Yet for AI to deliver value at scale, Verizon needed to fundamentally rethink its approach to data management — breaking down silos and turning fragmented data into trusted, reusable data products. This is where Alation became a critical enabler.

Data challenges: Silos, quality, and business alignment

Verizon’s data challenges are common to large enterprises:

• Siloed data and redundancy: Business units stored, managed, and defined data independently, resulting in duplicative work and conflicting insights.

• Data quality and trust: With real-time and batch data pouring in from numerous channels, ensuring data quality, lineage, and trust was daunting.

• Business alignment: Without clear communication between data teams and business stakeholders, it was difficult to align data products to measurable business outcomes.

“When it comes to big data, we have very large datasets, and a high veracity, volume, and frequency, both real-time data and pre-existing. How do we ensure we not only tackle that data but also enable it with insights for the business teams?” — Latheef Syed

The answer? Transitioning to a data product operating model supported by Alation.

From siloed data to trusted data products

Verizon set out to transform its approach to data management, adopting a data product operating model designed to:

• Break down silos by creating reusable, certified data products that span business units.

• Ensure every data product is trusted, governed, and aligned to business needs.

• Support both human analysts and AI agents with rich metadata and context.

Alation: The foundation for Verizon's data product economy

To operationalize data products at scale, Verizon turned to Alation, using its data catalog as the central platform for:

A hub-and-spoke operating model

• A central hub defines governance, standards, and the data product lifecycle.

• Each business unit (the spokes) creates and curates data products aligned to local needs.

• All products flow through a centralized repository powered by Alation, ensuring consistent metadata, lineage, and quality monitoring.

“Without having a centralized repository where you can manage your data with the right labeling and lineage, the trust factor will be lost.” — Latheef Syed

Business glossary and metadata backbone

Metadata powers everything. Verizon invested heavily in ontology creation, tagging, labeling, and lineage tracking within Alation to ensure every data product is fully described, governed, and discoverable — for both humans and AI agents.

Results: A thriving data product economy

The results speak for themselves. Verizon’s Slack-based data discovery channel, linked to Alation, answers hundreds of questions daily — helping users find and trust the right data products faster. And redundant data work has been dramatically reduced, thanks to better product discovery and alignment across teams.

“The platform now supports 8,000+ users, and we’re expanding to more business units.” — Latheef Syed

Enhanced AI performance

Verizon’s AI models are more accurate and easier to prompt — because they consume metadata-rich data products built for AI from day one.

“In order for GenAI to be very effective, make sure your core metadata is solid… have the right labeling, tagging, ontologies, and taxonomy.” — Latheef Syed

Faster innovation with business alignment

With clear governance from the hub and agility at the spokes, Verizon’s data teams can deliver new products faster — while ensuring every product is aligned to real business goals.

Key lessons for data leaders

Verizon’s story offers a blueprint for other organizations building their own data product economy:

1. Build trust first

Data products must be certified, governed, and trusted — not just raw data dumps in fancy packaging.

“Make sure your data is organized as trusted assets, not just data thrown into a repository.” — Latheef Syed

2. Balance governance and agility

A hub-and-spoke operating model balances central standards with local flexibility and expertise, enabling innovation without chaos – or data bottlenecks or breadlines for data consumers seeking access.

3. Streamline access

Leverage a centralized data & AI products marketplace to make your certified, trusted assets discoverable to the public. Ensure they have rich metadata so newcomers get the context they need to leverage these assets effectively. 

4. AI-Ready from day one

Leverage metadata to design data products for both human and AI consumers — ensuring every product is context-rich and machine-readable. Metadata isn’t just documentation — it’s the fuel for discovery, governance, and AI performance.

5. Continuous improvement

Treat data products like software products, with ongoing feedback, updates, and lifecycle management.

“Continuously evaluate your models to ensure data accuracy is in check at all times.” — Latheef Syed

Conclusion: A scalable data product economy

Verizon’s success with data products demonstrates that with the right operating model and the right platform, enterprises can break down silos, accelerate innovation, and boost AI performance — all while building trust in data.

“Without context, both humans and AI are flying blind. Metadata is the glue that connects data to business value.” — Latheef Syed

For organizations building their own data product economy, Alation offers the proven platform and approach to make it happen — at scale.