Keeping a Pulse on Logical Model Health at Virgin Pulse

Data Mesh

2,000+ DB objects

About the company

Virgin Pulse is a benefits and care company headquartered in Providence, Rhode Island. Their objective is to “empower people to lead happier and healthier lives by engaging them in personal, rewarding ways throughout their health and wellbeing journeys.” With this mission, the company impacts over 100 million people across 190 countries. To achieve this, Virgin Pulse employs more than 2,000 employees working across eight different offices globally.

A company of this scale, supporting many Fortune 500 companies with their solutions and influencing organizations of all sizes, is tasked with maintaining a nuanced data infrastructure. To organize and maintain critical data schemas for this well-architected platform, Virgin Pulse identified SqlDBM as the top product for the job.

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Wellbeing Software Platform

Headquarters:

Providence, Rhode Island

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We have around 40 projects, some working with [sensitive] data, but we don’t have one big model that actually oversees everything in Snowflake, we are using the main pieces which are distributed across multiple projects in SqlDBM.”

Muaz Hrustanbegovic,
Sr. Manager of Data Systems

I have no doubt that, you know, in my mind that SQLDBM is the better of the tools in this space.”

Deep Sogani,
Vice President and Chief of Enterprise Data

Developing and reviewing Logical and Physical models

Given their already large and expanding scale, Virgin Pulse needed a way to manage and share data models across multiple teams and departments. The task of improving data model management was entrusted to Muaz Hrustanbegovic, Sr. Manager of Data Systems. “There was definitely a need to introduce a tool like SqlDBM into our system,” says Muaz. The team that maintains the conceptual and logical models in SqlDBM is also the team that controls their physical implementation, deployed primarily on Snowflake Data Cloud.

Previously, it was challenging for Muaz’s team to visualize the structure of the model using legacy modeling features with tools of the past. Challenges with reading DDL and an inability to collaborate on modeling projects with the same ease they began to leverage in Snowflake made it clear that they would need further tooling to meet the needs of their modern approach.

To maintain their Snowflake data models, Muaz’s team began to create and review their logical and physical models in SqlDBM. “We have around 40 projects, some working with health data, but we don’t have one big model that actually oversees everything in Snowflake,” said Muaz, “we are using the main pieces which are distributed across multiple projects in SqlDBM.” In such a way, the team has successfully harnessed SqlDBM to weave together the essential components of their model, enabling a comprehensive perspective of their Snowflake environment.

Spreading domain-level control for improved access

In an effort to organize their approach to the overall enterprise architecture, Virgin Pulse’s data team is adopting the Data Mesh philosophy. Data Mesh uses a modern, distributed architecture and centralized best practices to allow easy access to data where it lives without needing to move or transform before querying. The philosophy aims to address the challenges of scaling data infrastructure and making it more responsive to the needs of diverse and dynamic organizations.

For Virgin Pulse, this has resulted in the creation of ten data domains to own certain parts of their business. “Every domain is fully owned by a focussed team,” says Muaz, “and it is my data engineering team’s responsibility to provide the knowledge of modeling best practices to allow for distributed full ownership of what these teams are doing as well.” While the rollout of this philosophy in practice at Virgin Pulse is still in progress, using SqlDBM to organize and maintain their data models has helped to improve their ability to ensure accuracy in the physical models being created by these domain owners.

Modeling collaboratively and without complexity

As with any software money can buy today, there will likely be a set of features that are high value and thus high priority for any use case. In Virgin Pulse’s example, there are two SqlDBM features they have used repeatedly since onboarding the tool to help them succeed.

The first high-value feature is the low-code nature of SqlDBM. Thanks to this, Muaz’s team has been able to adopt SqlDBM quickly as part of their data stack, beginning to design models with ease and diving into DDL elements of the product as needed. On the flip side, SqlDBM is powerful because of its ability to understand and generate database-specific syntax—separating SqlDBM from the basic drag-and-drop tools that cannot work with SQL or YAML when modeling.

Secondly and equally as foundational to SqlDBM in terms of value offered is the ability to collaborate with colleagues using the tool. Being a cloud-native Software-as-a-Service solution allows users to get up and running quickly, receiving periodic updates immediately and without frustration, and critically for Muaz’s team, allows people to work together on data models like never before!

Next for Virgin Pulse’s data modeling practice

While the team at Virgin Pulse has been able to derive value from SqlDBM early in its adoption, there is plenty for them to look forward to in the near future. As SqlDBM continues to evolve, customers like Virgin Pulse receive non-stop feature additions and improvements. Through regular engagement sessions, Muaz and his team are kept aware of the SqlDBM roadmap and plan for ways to incorporate it into their workflow.

Table templates, releasing before year-end ‘23, are of particular interest to Muaz, as they will have a fast impact on their ability to standardize the structure and appearance of specific table types across departments. In 2024, SqlDBM’s Global Modeling feature is poised to revolutionize Data Mesh-like implementations—giving Virgin Pulse and organizations like it the ability to extend and reference model objects across projects in SqlDBM—a first-of-its-kind feature for collaborative modeling. These and many more innovative features are why Virgin Pulse is working closely with SqlDBM to help guide and define the road ahead.

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Keeping a Pulse on Logical Model Health at Virgin Pulse

Data Mesh​

2,000+ DB objects

About the company

How did SqlDBM help?

Features used

Direct Connection to Snowflake

Git integration

Documentation

Collaborative editing

Confluence integration

Jira integration

Wellbeing Software Platform​

Providence, Rhode Island​

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Developing and reviewing Logical and Physical models

Spreading domain-level control for improved access

Modeling collaboratively and without complexity

Next for Virgin Pulse’s data modeling practice

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Resources

Strategic advisors

Kent Graziano

Gordon Wong

For cloud data platforms

Try modeling now

Platform

Data Modeling

Model Governance

Snowflake Schema Monitoring

Integration

See all

Why SqlDBM?

Have questions?

Data Mesh

Wellbeing Software Platform

Providence, Rhode Island