Trility enabled a client to achieve stable reporting during major CRM/Loan Origination system migrations. We built a unified data foundation on Microsoft Fabric, integrating eight systems and 500 tables into a single source of truth. This ensured operational continuity and prepared the client to leverage AI efficiently in the future.
A leading national home builder was undertaking concurrent, strategic system modernizations, including new CRM and Loan Origination Systems. This created an immediate and critical business requirement to “live in two worlds” – seamlessly integrating historical data from legacy systems with operational data from the new platforms into consolidated, enterprise-level reports.
The client’s reporting environment was complex with challenging data integrity issues. With over 500 unique reports operating against fragmented data sources, their organization lacked a single, unified source of truth. This fragmented structure created excessive overhead for report maintenance and, most critically, rendered the organization incapable of executing its core strategic goals, such as building a foundation for advanced analytics and artificial intelligence – a key objective set by the board of directors.
The underlying technical landscape presented significant challenges common in an enterprise-scale environment:
Data Sprawl: Operational data scattered across over 20 different source systems.
Complex Logic Layers: Existing reports relied on deep, complex layers of logic (views, stored procedures), making data lineage difficult to trace and changes risky and time-intensive.
Data Quality Issues: Master and reference sales data hindered the accurate association of business activity across new and existing systems.
Parallel Project Dependencies: The project ran parallel to the new CRM and Loan Origination System implementations and was dependent on the stability and availability of these new systems' data, which introduced resource and project coordination challenges.
Trility approach focused on quickly delivering a foundational data model while navigating the client’s complex system transition.
Discovery and Analysis: Rapidly defined the client's data landscape and logical model by working with SMEs to clarify existing complexity and layers of inherited report logic.
Strategic Collaboration: Advocated for and established close working sessions with parallel project teams (CRM and Loan Origination) to ensure alignment and mitigate external risk.
MVP Delivery: Create a Minimum Viable Product (MVP) data model focused on the client's single most critical metric – high-level sales counts. This focused effort immediately delivered tangible progress and established a scalable foundation. Starting small and then layering on details (by division, community, and phase) proved effective for communication and momentum.
Iterative Expansion: The data model was expanded iteratively, driven by the requirements of additional, prioritized business reports, continually working backward from the necessary business outcome.
Technology Stack and Tools
Core Data Platform: Microsoft Fabric was chosen for its native integration with the existing ecosystem and cost-effectiveness, providing immediate access to AI (Copilot) and Data Science tooling.
Ingestion and Processing: Used Fabric Pipelines and Notebooks (Python, PySpark, SparkSQL) for scalable data transformation. Fabric Link enabled seamless replication from Dynamics 365.
Architecture: Leveraged modern Fabric components, including Lakehouses, Mirrored Databases, and Semantic Models to structure data for high performance.
Data Sources: Integrated 8+ source systems, including Azure SQL, SQL Server, Snowflake, and Dynamics 365.
ER Studio was used for advanced data modeling.
Trility delivered business value by securing operational stability during system migrations and unified the data foundation, reducing maintenance overhead and establishing the single source of truth required to enable future AI and strategic growth.
Operational Continuity: Divisions received critical reports that blended data from both legacy and new systems with no disruption to daily operations or financial reporting.
Data Unification: Integrated eight of the 20+ source systems and 500 tables into the platform, then built 61 unified data model tables to establish the single source of truth for the enterprise.
Strategic Readiness: The new data model directly supports the client’s board-level goals by providing the necessary foundation for AI and advanced analytics initiatives.
Reduced Overhead: The new, centralized data model reduced the complexity and overhead associated with updating and maintaining mission-critical business reports.
Internal Empowerment: Client staff were upskilled to manage and extend the new platform, shifting their focus to higher-level interactions and strategic data management.
