What to Know Before You Implement Embedded Analytics

Embedded analytics projects often struggle to scale due to foundational decisions made before implementation, such as data model design, tenant isolation, performance architecture, and licensing. A successful data model must be intuitive for users, perform data transformations before data reaches the analytics layer, and incorporate tenant isolation from the outset to support multi-tenancy and self-service exploration. Tenant isolation can be achieved through row-level security or connection-level isolation, each with its trade-offs regarding security and operational overhead. Performance issues usually arise from decisions about data model shape, warehouse partitioning, clustering strategy, and query concurrency configuration, which are costly to reverse once in place. Materializing results is beneficial for computationally expensive queries with stable underlying data, while real-time querying is best for scenarios requiring up-to-date information. Planning these elements early, alongside a thorough understanding of licensing costs, can prevent expensive rework and ensure the embedded analytics solution scales effectively with user demand.