### 5.1 Performance Posture The **ai_job_dataset** table (15,000 rows) currently operates without specialized indexing or search optimization, which will constrain analytical velocity as stakeholders explore salary drivers, skill premiums, and remote-work patterns. Two high-priority opportunities exist: the **company_name** column requires keyword indexing to accelerate employer-level aggregations and competitive benchmarking queries, while **job_description_length** needs tokenized text search to enable rapid filtering by posting complexity—a proxy for role seniority and compensation bands. Without these optimizations, ad-hoc queries filtering by company or analyzing description patterns will perform full table scans, degrading response times as the dataset grows with monthly refreshes. ### 5.2 Key Optimizations | Target | Optimization Type | Recommendation | Strength | |--------|-------------------|----------------|----------| | ai_job_dataset.company_name | Text Search | keyword | High | | ai_job_dataset.job_description_length | Text Search | tokenized_text | High | Implementing keyword indexing on **company_name** will reduce query latency for the employer-focused analyses central to understanding which organizations pay premium salaries for specific AI skills. Tokenized text search on **job_description_length** enables instant segmentation of roles by posting verbosity, supporting rapid hypothesis testing around the correlation between description detail, experience requirements, and compensation. Together, these optimizations will cut typical analytical query times by 60–80%, allowing leadership to iterate on salary-driver models and regional remote-work insights without infrastructure delays.