### 2.1 Integration Assessment The **ai_job_dataset** operates as a self-contained analytical table with 15,000 job postings capturing salary, experience, location, company attributes, and skill requirements in a single structure. This flat architecture supports immediate analysis of salary drivers and remote-work patterns without dependency on external tables. To answer strategic questions about market competitiveness and regional benchmarks, the dataset would benefit from integration with external reference data—particularly economic indicators by country, cost-of-living indices for the 78 company locations represented, and standardized skill taxonomies that map the current free-text competencies to industry-recognized certification frameworks. ### 2.2 Recommended Actions - **Enrich with economic context**: Join World Bank GDP per capita and purchasing power parity data by company location to normalize salary figures and reveal whether compensation reflects local market conditions or global talent competition. - **Integrate cost-of-living indices**: Link Numbeo or similar datasets to the 78 distinct company locations to calculate real-value compensation, enabling apples-to-apples comparison of remote versus on-site roles across geographies. - **Map skills to certification frameworks**: Connect the skills column to external taxonomies (e.g., O*NET, LinkedIn Skills Graph) to standardize competency naming, quantify skill adjacencies, and identify emerging versus commoditized capabilities commanding salary premiums. - **Append company financial data**: Where company size is recorded, integrate revenue or funding stage information to test whether larger organizations systematically outbid startups for senior AI talent or specific skill sets.