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Launch helped a global safety certification leader unify fragmented data into a single, centralized model—improving access, performance, and business alignment.
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A global 125-year-old safety certification company with operations in over 40 countries faced growing inefficiencies in its analytics ecosystem. With over 60 on-prem Analysis Services tabular models and no centralized architecture, data sprawl led to strained IT-business relations and inconsistent insights. The business demanded a shift toward self-service analytics and a unified, performance-driven data model to support enterprise-wide decision-making.
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By unifying 26+ data sources into a single tabular model, Launch transformed analytics from fragmented to focused—unlocking deeper insights and faster decisions.
Launch leveraged an iterative development approach to assess, design, and implement a scalable and centralized data model. We began by identifying architectural gaps and gathering requirements from key business stakeholders. A prioritized backlog guided development, culminating in the creation of a consolidated tabular model supported by a robust semantic layer and security framework. Launch also enabled a new intake process and trained internal developers on best practices for scalable, insight-driven analytics
The organization now operates with a unified source of truth for analytics. The new centralized data model enabled the client to modernize analytics operations, streamline governance, and improve user experience across the enterprise. Key outcomes included:
With a scalable, centralized analytics model in place, the client is better equipped to drive data-informed decisions across the enterprise, reduce IT strain, and scale future analytics initiatives with confidence.
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