### 5.1 Performance Posture The current infrastructure is well-positioned for operational reporting, with the largest table—**sales_pipeline** at 8,800 rows—representing 98% of total volume and remaining comfortably within single-server query thresholds. All five tables are dimension-style structures with no detected foreign key relationships, meaning queries currently operate on isolated datasets without cross-table join overhead. The primary scaling concern is not volume but **discoverability**: the **data_dictionary** table contains 21 rows of metadata descriptions stored as free text, creating friction when sales operations or analytics teams search for specific pipeline fields, product attributes, or account definitions. ### 5.2 Key Optimizations | Target | Optimization Type | Recommendation | Strength | |--------|-------------------|----------------|----------| | data_dictionary.Description | Text Search | tokenized_text | High | Implementing tokenized text search on **data_dictionary.Description** will dramatically improve metadata lookup speed, enabling business users to locate pipeline stage definitions, account segmentation logic, or product hierarchy explanations in milliseconds rather than scanning 21 unindexed text entries. This optimization directly supports self-service analytics adoption—sales leaders exploring the **sales_pipeline** or **accounts** tables can quickly understand field semantics without escalating questions to IT, reducing time-to-insight for deal forecasting and territory performance analysis.