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Reclaiming the Data Lakehouse: A Business-Centric Approach to Modernization

After two failed modernization attempts, a major international fraternal service organization partnered with Resultant to rebuild its data lakehouse using a business-first approach. By aligning the platform with stakeholder needs and enterprise goals, the organization created a trusted, scalable data foundation that now supports CRM operations, enterprise analytics, and long-term growth.

When modernization loses sight of business goals

A major international fraternal service organization providing insurance and financial protection services to its members struggled with an underperforming data lakehouse that was blocking key business initiatives.

The organization needed to rebuild its data platform in a way that would support rapid subject area onboarding, historical reporting, and enterprise analytics for strategic growth.

Two previous attempts to modernize data infrastructure failed to produce the desired results. Those efforts centered on moving data from point A to point B without first defining the business questions the data needed to answer. As a result, team members struggled with multiple issues.

Fragmented Data

Disconnected sources made an enterprise-wide single source of truth difficult to achieve.

Low Stakeholder Trust

Poor data quality hindered critical initiatives like the rollout of a unified Microsoft Dynamics CRM.

Operational Risk

The organization was forced to maintain costly legacy systems while paying for a new cloud infrastructure that wasn't delivering value.

When the organization’s CIO reached out to Resultant after seeing our expertise in the Data Vault community, their focus shifted from technology-driven decisions to a business-first approach, using business requirements as the foundation for solution design.

The strategy: Lead with business needs

Because this project was a critical dependency for the organization's CRM and enterprise reporting, we prioritized stakeholder buy-in from the C-suite down. Rather than starting with the source systems, we began by engaging over 70 stakeholders across multiple domains to define future-state goals from end-user perspective.

This approach ensured that the project wasn’t seen as merely an IT initiative, but as the foundational engine for targeting new customers and supporting members.

Developing the ecosystem for sustainable scalability

Our team built the technical solution on a Snowflake foundation using Data Vault 2.1 methodology and best practices, with the ecosystem rounded out by integrating tools selected specifically to meet the organization’s business needs.

Automation at Scale: By utilizing VaultSpeed, we accelerated the generation of Data Vault structures, moving from months of manual coding to automated, auditable deployments.
Intelligent Modeling: We leveraged Erwin (Quest Software) to support enterprise data modeling, ensuring every technical structure remained strictly aligned with core business concepts.
Unified Ingestion: Data ingestion and integration were streamlined through Qlik / Talend Cloud, creating a consistent flow from fragmented internal and external sources.
Precision Orchestration: We utilized Airflow to orchestrate end-to-end pipelines, managing complex dependencies to ensure data was available exactly when the business needed it.
Data Quality as a Standard: We integrated Validatar for automated testing and Melissa for address standardization, ensuring that when data reached the CRM, it was accurate, trusted, and actionable.

Resultant understands the importance of strong vendor partnerships. Our longstanding relationships enable clear communication and allow us to extend vendor responsiveness and service quality directly to our clients.

Conclusion

Data Modernization initiatives need to be approached as a business transformation rather than just a technical upgrade. By aligning the technical implementation with business priorities and managing the vendor ecosystem with precision, we turned a history of failed attempts into a foundational success that supports the organization’s long-term mission.

Reclaiming the Data Lakehouse: A Business-Centric Approach to Modernization

When modernization loses sight of business goals

Fragmented Data

Low Stakeholder Trust

Operational Risk

The strategy: Lead with business needs

Developing the ecosystem for sustainable scalability

Conclusion

Data Architecture Modernization Boosts Hospital Foundation’s Efficiencies, Collaboration

Building a Repeatable ROI Model for Workforce Success

Staffing Firm Drives Growth And Enhances Customer Experience Through Digital Transformation And BI...

Ready to challenge your thinking?

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Reclaiming the Data Lakehouse: A Business-Centric Approach to Modernization

When modernization loses sight of business goals

Fragmented Data

Low Stakeholder Trust

Operational Risk

The strategy: Lead with business needs

Developing the ecosystem for sustainable scalability

Conclusion

Impact Stories

Data Architecture Modernization Boosts Hospital Foundation’s Efficiencies, Collaboration

Building a Repeatable ROI Model for Workforce Success

Staffing Firm Drives Growth And Enhances Customer Experience Through Digital Transformation And BI...

Ready to challenge your thinking?

Cookie Notice