A major Australian health insurer undertaking a data platform modernization needed to validate newly built foundation data models and derivative sales reporting models before production deployment. The organization had built centralized models in Snowflake using dbt but required specialized testing capability to maintain objectivity and rigor before finalization.
We provided specialized third-party testing capability combining technical expertise with business context, using AI-accelerated methodologies to validate the data models and identify foundational issues before production.
The health insurer successfully validated its foundation and sales models for production deployment, establishing testing standards and data quality benchmarks that will guide ongoing development. The engagement demonstrated the value of combining deep technical expertise with business domain knowledge.
If you are modernizing your data platform with Snowflake and dbt, you face the same validation challenge: ensuring that newly built models work correctly before they reach production and affect business decisions. The testing approach shown here applies to any organization building foundation models and derivative analytics on top of them, particularly in regulated industries like insurance, banking, and healthcare where data accuracy directly impacts business operations and customer trust. By bringing in specialized testing expertise early in the development cycle, you catch foundational issues before they cascade into downstream models, and you establish data quality standards that serve as guardrails for future development. Third-party testing rigor also gives your stakeholders confidence in model validity before rollout, accelerating adoption and enabling faster time-to-value from your data modernization investment.
A specialized testing team with both data engineering expertise and strong business domain learning capability worked closely with the client's internal data and analytics teams to develop comprehensive testing criteria and execute rigorous validation across multiple model layers.
A private equity-backed software group with independent subsidiaries on different systems needed unified reporting without sacrificing the ability to demerge individual units in the future. InterWorks delivered a multi-account data platform architecture that consolidated group-level analytics while preserving demerger readiness and enabling board-level visibility into cross-unit metrics.
A hospitality company solved fragmented marketing analytics across dozens of properties with a scalable, AI-ready data warehouse approach that empowered analysts and positioned the team for future technology changes.
A global hardware manufacturer evaluated options for migrating from core-based Tableau Server to Tableau Cloud as its existing infrastructure reached the end of its lifecycle.
Interworks GmbH
Ratinger Straße 9
40213 Düsseldorf
Germany
Geschäftsführer: Mel Stephenson
Kontaktaufnahme: markus@interworks.eu
Telefon: +49 (0)211 5408 5301
Amtsgericht Düsseldorf HRB 79752
UstldNr: DE 313 353 072
