ibank-worklog-enrichment

To generate iBank worklog seed data, automate the creation of tags, team-scoped evaluations, and current user skills from existing worklogs, teams, and personas. The process populates tb_meta_tag, tb_worklog_tag, tb_user_evaluation, and tb_user_skill tables with validated, SQL-ready outputs.

Inferring user skills from historical worklogs involves analyzing past entries to create current user skill snapshots. The system populates the tb_user_skill table using stable skill names and levels 1-5, ensuring unique (user_id, skill_name) pairs for accurate skill tracking.

Yes, existing iBank worklogs, teams, departments, and personas are required. The seed data generation applies specifically when these foundational elements already exist and you need to populate tb_meta_tag, tb_worklog_tag, tb_user_evaluation, and tb_user_skill across the dataset.

Worklog data enrichment validates referential integrity during seed data generation for tags, evaluations, and skills. This validation ensures that all generated links between worklogs, tags, and user evaluations maintain consistent, accurate relationships across the entire dataset.

The iBank worklog enrichment process produces SQL-ready outputs designed for direct database population. These outputs target the tb_meta_tag, tb_worklog_tag, tb_user_evaluation, and tb_user_skill tables with validated data relationships and stable skill naming conventions.

Yes, you can generate team-scoped evaluations with evidence for selected evaluatees using iBank worklog data. The system populates the tb_user_evaluation table by analyzing existing worklogs, teams, and personas to create accurate, evidence-backed evaluation records.