Infrastructure for Personalized Learning Path Recommendations

How data is organized into queryable, relational formats

• Skills taxonomy must formally map organizational competency framework to course content metadata across the LMS catalog, enabling skill gap calculation at the individual employee level

Whether operational knowledge is systematically recorded

• Employee skills profile capture must aggregate assessment results, completed training, manager-assessed competencies, and self-reported proficiencies with timestamp and source attribution

How explicitly business rules and processes are documented

• Career path schema must define progression routes by role family with required competency thresholds at each level to anchor personalization to organizational advancement logic

Whether systems share data bidirectionally

• LMS integration must expose course completion status, assessment scores, and content metadata via API to feed the recommendation engine with current learning state

How frequently and reliably information is kept current

• Recommendation refresh cadence must be governed so that learning paths update after course completions, role changes, or skills taxonomy revisions without manual re-generation

Whether systems expose data through programmatic interfaces

• Employee access to their own skills profile and recommended path must be scoped correctly so individuals see actionable development suggestions without exposing manager-only performance annotations