How to Choose Embedded Analytics Platforms That Scale With User Growth

As products scale, the initial decisions surrounding data architecture, embedding methods, security, and pricing are crucial for determining whether analytics will drive adoption or become a hindrance. Traditional analytics often exist outside the product, causing users to leave the application to access data, which can feel like an add-on rather than a core feature. For successful scaling of embedded analytics, it is essential to utilize live warehouse queries, centralized governance, and a single source of truth, as exemplified by Sigma, which connects directly to the cloud data warehouse. This allows users to embed live analytics, maintain security, and extend into AI applications without rework as usage increases. The selection of an analytics platform should prioritize understanding who the analytics are for and what outcomes are required, considering factors like customization, security, and pricing. A warehouse-native architecture aids in scaling by avoiding data duplication and maintaining a single source of truth, while the choice of embedding method should allow analytics to feel native and evolve with user needs and design systems. Platforms like Sigma offer these capabilities, enabling analytics to grow alongside products and users without becoming a constraint, emphasizing the importance of early strategic decisions for long-term success.