Hallmark: Building a Trusted Data Catalog Foundation for Privacy, Data Quality, and AI-Ready Analytics

Hallmark is a leading consumer brand known for helping people connect, celebrate, and express care across life’s moments. The company operates across retail, digital, consumer, loyalty, marketing, and partner channels, where trusted data is essential for privacy, customer experience, reporting, and future analytics.

As Hallmark’s data landscape grew across systems and business functions, the organization needed a stronger way to understand where consumer data lived, how it was classified, how it could be accessed, and how it could be governed consistently.

Hallmark’s governance journey with OvalEdge began with data cataloging.

The organization needed a more structured way to understand consumer data spread across multiple systems. This was especially important for privacy requirements such as Right to Know and Right to Delete, where teams needed to identify personally identifiable information across the data landscape and support consumer privacy requests consistently.

The challenge extended beyond locating PII. Hallmark also needed a governed way to route privacy requests to the appropriate teams, support the actions required across systems, and track those requests through completion.

As Gopal Dhandapani, Director of Data Engineering and Analytics, explained:

“Data was all over the place, and we wanted to bring data together, all the attributes together, to understand how we can solve the privacy use case.”

That catalog foundation gave Hallmark a more structured way to understand consumer information across systems. It also created a base for broader work in this area, including business glossary, PII classification, governed queries, workflows, custom views, and, more recently, the evaluation of Data Quality.

The result is a governance program that started with privacy and is expanding into broader data-governance use cases.

The same foundation that helps Hallmark find and govern consumer data for compliance can support BI, reporting, loyalty, marketing, customer intelligence, and future AI-enabled use cases.

Hallmark selected OvalEdge to support its data cataloging and privacy-governance needs across a complex consumer data landscape.

The initial requirement was practical and business-critical: Hallmark needed to understand where consumer PII existed, bring attributes together across connected systems, and support privacy workflows in a more governed way. OvalEdge provided the cataloging foundation for that work.

The fit extended beyond cataloging, with OvalEdge bringing PII classification, Business Glossary, Governed Data Query, and Service Desk / Jira-supported workflows into the same environment. This gave Hallmark a governed way to locate consumer PII across systems, connect that data to Right to Know and Right to Delete requests, and route those requests to the appropriate teams for action.

As the program expanded, that existing foundation became an advantage. Source systems were already connected, teams were already working with the platform, and Hallmark could evaluate additional capabilities such as Data Quality without introducing a separate system and vendor.

Hallmark has implemented OvalEdge across several connected governance areas, including Data Catalog, Business Glossary, PII classification, Governed Data Query, privacy workflows, Service Desk / Jira integration, custom views, and vendor-related data governance.

The Data Catalog is the foundation. It helps Hallmark understand where consumer data exists across connected systems and gives teams a central place to discover, classify, and govern important data assets.

This data landscape spans multiple consumer-data systems, including Salesforce Service Cloud, Salesforce Commerce Cloud, Akamai Identity Cloud, Redshift, Teradata, and several MySQL-based consumer databases.

The Business Glossary supports consistent understanding of sensitive data and helps reinforce naming conventions, business meaning, and classification standards across teams.

PII classification is central to the privacy use case, making it possible to identify and understand personally identifiable information across systems so privacy requests can be handled accurately and consistently.

Governed Data Query supports privacy operations by helping teams locate consumer PII across systems using identifiers such as name, email, or loyalty number. It gives teams a governed way to connect Right to Know and Right to Delete requests with the underlying consumer data and the workflows required to act on those requests.

Service Desk and Jira integration help operationalize the privacy workflow by routing requests and follow-up actions across teams. Hallmark also uses custom fields and filtered views to identify vendors that receive shared or sold personal data, helping teams support downstream privacy actions when required.

Data Quality is the next step in the journey, with Hallmark currently evaluating it across several business use cases related to consumer data, loyalty, membership, marketing, customer identity, and downstream reporting. The initiative includes both out-of-the-box capabilities and Hallmark-defined rules using custom SQL.

OvalEdge has helped Hallmark create a more structured, searchable, and operational environment for consumer data, supporting both PII discovery and the privacy workflows required to act on it.

That visibility now extends to where consumer data exists, how it's classified, and how it supports privacy obligations.. This is especially important for Right to Know and Right to Delete workflows, where teams need accurate visibility into PII across systems.

The same foundation also supports greater consistency. With cataloging, glossary, classification, and governed query capabilities in one environment, Hallmark can build a more consistent understanding of consumer data across teams and use cases.

OvalEdge also helps strengthen operational control, connecting PII discovery with the workflow needed to process privacy requests — routing requests to the appropriate teams, supporting action on the data, and tracking the process through completion.

That foundation is now supporting Hallmark’s next governance step: Data Quality. Hallmark has been able to test business-relevant quality rules, use custom SQL where needed, and evaluate how OvalEdge can help identify quality issues in consumer and customer data.

Gopal highlighted the flexibility of the platform:

“Overall, the customization, dashboarding, and the results have been really good. Beyond the out-of-the-box rules, we can plug in our own SQL to provide additional context and get the answers we need.”

He also highlighted OvalEdge’s preparation and collaboration during the Data Quality initiative:

“OvalEdge came very prepared for this. They looked at the use case, they knew that this use case could be tackled this way, and they were asking the right questions. When we provided the custom SQL, they plugged it in and were able to show us the SQL they ran and the result they got.”

The Data Quality initiative demonstrates how Hallmark can extend its existing catalog and privacy foundation into Data Quality. That progression matters because governed consumer data supports more than compliance. It can also support BI, analytics, loyalty programs, marketing campaigns, customer identity, and future customer intelligence initiatives.

Hallmark is also looking ahead to AI, self-service analytics, and broader data platform modernization.

The company is already exploring AI across multiple areas, including SDLC and consumer-facing experiences such as product and card recommendations. From a data perspective, Hallmark is also exploring self-service analytics through semantic models, Copilot-style capabilities, and natural-language access to trusted answers.

Gopal described the direction this way:

“We are exploring a lot of self-serve analytics using semantic models that we have built — Copilot, for example, within Power BI. The semantic model would help us get the answers through natural language.”

There's also future potential for more conversational experiences around privacy and Data Quality.

Users could ask where consumer data exists, where a quality issue originated, whether the issue came from a source system or pipeline, and what action may be needed next.

For Data Quality, Hallmark’s longer-term vision goes beyond identifying an issue. The goal is to help users trace a problem back to the source system, data pipeline, or transformation that created it, providing the context needed to investigate and resolve the issue.

That future direction also includes the idea of a broader context layer. Hallmark has not yet built that layer, but it is part of the company’s current data platform evaluation.

“As part of our data platform evaluation, this is one of our use cases where we would want to build a context layer. We would want to make sure the semantic model has the right information and keep the semantic model at the data level rather than building it at the Power BI level.”

Hallmark also sees potential in agentic workflows taking the next operational step — for example, creating Jira or ServiceNow tickets automatically once an issue is identified, rather than requiring users to act manually.

The opportunity now is to extend what's already been built around cataloging, business meaning, privacy, and workflows into a richer data context that can support self-service analytics and future AI-enabled use cases.