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 to consolidate information about consumer data spread across multiple systems. This was especially important for privacy requirements such as Right to Know and Right to Delete, where teams need to locate personally identifiable information, understand where it exists, and support consumer privacy requests with confidence.

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

“We started engaging with OvalEdge to do the data cataloging first. A couple of years ago, we picked up a platform to understand how we can tackle data privacy — Right to Know, Right to Delete, all those things. 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 governance work: business glossary, PII classification, governed queries, workflows, custom views, and eventually Data Quality.

The result is a governance program that starts with privacy but extends naturally into trusted data. The same foundation that helps teams find and govern consumer data for compliance can also improve confidence in 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.

As Hallmark expanded its governance program, the existing OvalEdge foundation became an advantage. Source systems were already connected, teams were already working with the platform, and the same governed environment could be extended into adjacent use cases such as Data Quality.

“We were already using OvalEdge as part of our data cataloging tool. It was an easy pick for us to use the data quality tool that is available because the database is already connected, the source systems are already connected… rather than bringing in a new system, new vendor, and going through the entire motion.”

For Hallmark, this meant new governance capabilities could build on the catalog and privacy foundation already in place. Data Quality did not need to be evaluated in isolation. It could be tested against the same connected data environment and business context that Hallmark was already using for privacy and governance.

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

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.

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. Hallmark needs 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 query PII information and support Right to Know and Right to Delete workflows. It gives Hallmark a governed way to connect privacy requests with the underlying consumer data.

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 custom views to support vendor-related data governance, including personal data sharing and selling use cases.

Data Quality is the next step in the journey. Hallmark is currently evaluating OvalEdge Data Quality through an initiative focused on several business use cases related to consumer data, loyalty, membership, marketing, customer identity, and downstream reporting.

OvalEdge has helped Hallmark create a more governed, searchable, and operational foundation for consumer data.

The first impact is visibility. Hallmark can better understand where consumer data exists, how it is 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 second impact is 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.

The third impact is operational control. Privacy requests are not only about finding data; they also require workflows, ownership, routing, and follow-up. OvalEdge helps connect the data foundation with the operational processes needed to act on that data.

This 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:

“One thing is just the flexibility that it provides. You can use the out-of-the-box functionality, or in our case, because of the data we wanted back or the additional criteria we wanted to include, we were able to do custom SQL to get exactly what we want back with all the rules applied.”

Gopal also noted the 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 has not yet moved into production, but it shows how Hallmark can extend its existing catalog and privacy foundation into data quality. That progression matters because trusted consumer data supports more than compliance. It also supports 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.”

This is where Hallmark’s governance foundation becomes important. Catalog, glossary, PII classification, governed queries, privacy workflows, and data quality all create business and technical context around data. That context helps users and AI tools understand what data means, where it comes from, whether it is sensitive, whether it is trusted, and how it should be used.

Hallmark has not yet built the full context layer, but it is already part of the company’s data platform thinking.

“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 for conversational experiences around privacy and data quality. Users could ask where a consumer’s data exists, where a quality issue originated, whether the issue came from a source system or pipeline, and what action should be taken next.

For Hallmark, the OvalEdge foundation is therefore becoming a connected layer of data understanding: cataloged assets, business meaning, privacy context, governed workflows, quality signals, and trusted metadata. That foundation can help the organization move from privacy compliance to trusted analytics, and from trusted analytics to future AI-enabled data work.