EnterpriseGovernance - Schema ============================= Synthetic enterprise data-governance snapshot across 12 business units. Point-in-time with 24-month operational-signal trajectory and annual self-assessment. Tables ------ business_units.csv (12 rows) ---------------------------- Column Type Notes -------------------------- ------- ------------------------------------------------------------------- bu_id string Primary key. bu_name string Display name. business_criticality string critical, high, medium, low. has_dedicated_data_steward boolean Bias signal: 4 of 12 BUs have no steward (IT director substitutes). steward_role_if_substitute string Empty if BU has dedicated steward; otherwise role of substitute. employee_count integer Drives asset count. capabilities.csv (8 rows) ------------------------- Column Type Notes ------------------------- ------------- ------------------------------------------------------------------------------------------------- capability_id string Primary key. capability_name string One of: Catalog, Lineage, Quality, Access, Retention, Classification, Stewardship, Data Literacy. weight_in_aggregate_score decimal Used to compute weighted enterprise capability score. target_level integer (1-5) Maturity-model target for each capability. assets.csv (~6,300 rows) ------------------------ Column Type Notes ----------------------- ------- ------------------------------------------------------------------------------------------------------- asset_id string Primary key. Format AST-NNNNN. bu_id string FK -> business_units.bu_id. asset_type string One of 7 types (Semantic Model, Report, Dashboard, etc.). certification_status string Certified, Promoted, Master Data, Untagged. Distribution strongly biased by has_dedicated_data_steward. in_enterprise_catalog boolean ~78% true for stewarded BUs, ~42% for non-stewarded. has_lineage_documented boolean Subset of catalog presence. has_data_classification boolean Independent of catalog. last_review_date date When the asset was last reviewed. capability_scores.csv (~2,300 rows) ----------------------------------- The operational-signal view of capability - the unbiased input. Column Type Notes ---------------- ----------------- ------------------------------------------------ month date (ISO) First of month, 24 months ending 2026-05. bu_id string FK. capability_id string FK. capability_level decimal (1.0-5.0) Maturity score derived from operational signals. source string Constant operational-signal for this table. self_assessment.csv (96 rows) ----------------------------- The biased input the dashboard exists to correct. Column Type Notes ---------------- ----------------- -------------------------------------------------------------------------------------------------------------------- assessment_date date Most recent annual survey: 2026-05-01. bu_id string FK. capability_id string FK. self_rated_level decimal (1.0-5.0) Systematically biased: stewarded BUs over-rate by +0.3 to +1.0; non-stewarded BUs are honest or slightly under-rate. source string Constant self-assessment-survey. respondent_role string data-steward or IT-director-substitute. The dataset's reason for existing --------------------------------- The bias in self_assessment.csv versus the truth in capability_scores.csv is the entire reason this dataset is interesting. The triangulation - operational signal + self-assessment + the gap between them - is exactly what the EnterpriseGovernance starter BRD is about. A chapter exercise that uses self-assessment as ground truth is teaching the wrong discipline. Suggested chapter exercises --------------------------- - Chapter 11 (prep for AI): Author the AI instructions that tell Copilot to weight operational signals higher than self-assessment when ranking gap severity. - Chapter 13 (when AI misleads): Ask Copilot "which BU has the largest governance gap?" without instructions. Watch whether it uses self-assessment (where the answer favors non-stewarded BUs because they self-rate low) or triangulates (where the answer reveals the stewarded BUs over-claiming). - Chapter 14 (responsible AI): This dataset embeds the canonical responsible-AI question - whose view of reality drives the recommendation? Use it to teach explicit triangulation as an ethical discipline, not a technical one. Reproducibility --------------- Generated by scripts/datasets/generate-enterprisegovernance.js. Source seed: 0xE17EC0B0.