Modern data architecture is critical for embedded analytics, as poor design can slow applications and limit scalability. Seven approaches exist: transactional databases, views/stored procedures, aggregate tables, replication, caching, data warehouses/marts, and modern analytics databases. Each offers trade-offs between performance, complexity, and cost, with modern columnar or in-memory databases providing the best query speed for large volumes but requiring specialized skills. Best practices stress aligning architecture with goals, end-user needs, latency expectations, and scalability plans. Analysts recommend shifting from static, siloed BI to dynamic, multi-directional data flows with self-service access.

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