{ "generated_at": "2026-10-25T04:14:45.359078+00:00", "summary": { "total_conflicts": 6, "critical": 1, "high": 2, "assets_at_risk": 63, "impact": { "total_impacted_assets": 63, "total_manual_hours_avoided": 31.5, "estimated_cost_avoided_usd": 2835 } }, "conflicts": [ { "kind": "silent_contradiction", "metric": "active_user", "severity": "critical", "confidence": 0.933, "blast_radius": 22, "impacted_assets": [ "urn:li:chart:(looker,arpu_trend)", "urn:li:dashboard:(looker,campaign_attribution)", "urn:li:dashboard:(looker,crm_campaigns)", "urn:li:dashboard:(looker,customer_value_forecast)", "urn:li:dashboard:(looker,fin_board_pack)", "urn:li:dashboard:(looker,fin_exec_overview)", "urn:li:dashboard:(looker,growth_weekly)", "urn:li:dashboard:(looker,retention_health)", "urn:li:dashboard:(tableau,cmo_review)", "urn:li:dashboard:(tableau,investor_reporting)", "urn:li:dashboard:(tableau,risk_ops_review)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.arpu,PROD)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.ltv_features,PROD)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.mau_revenue,PROD)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,mkt.mart.email_targeting,PROD)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,mkt.mart.mau_sessions,PROD)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.churn_risk,PROD)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.user_engagement,PROD)", "urn:li:mlModel:(mlflow,churn_predictor,PROD)", "urn:li:mlModel:(mlflow,credit_risk_model,PROD)", "urn:li:mlModel:(mlflow,ltv_predictor,PROD)", "urn:li:mlModel:(mlflow,send_time_optimizer,PROD)" ], "impact_graph": { "nodes": [ { "id": "metric::active_user::finance", "label": "Monthly Active Users\n(finance)", "type": "metric" }, { "id": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.mau_revenue,PROD)", "label": "mau_revenue", "type": "dataset" }, { "id": "urn:li:dashboard:(looker,fin_exec_overview)", "label": "fin_exec_overview", "type": "dashboard" }, { "id": "urn:li:dashboard:(looker,fin_board_pack)", "label": "fin_board_pack", "type": "dashboard" }, { "id": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.arpu,PROD)", "label": "arpu", "type": "dataset" }, { "id": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.ltv_features,PROD)", "label": "ltv_features", "type": "dataset" }, { "id": "urn:li:mlModel:(mlflow,ltv_predictor,PROD)", "label": "ltv_predictor", "type": "model" }, { "id": "urn:li:dashboard:(tableau,investor_reporting)", "label": "investor_reporting", "type": "dashboard" }, { "id": "urn:li:dashboard:(looker,customer_value_forecast)", "label": "customer_value_forecast", "type": "dashboard" }, { "id": "urn:li:chart:(looker,arpu_trend)", "label": "arpu_trend", "type": "dashboard" }, { "id": "metric::active_user::marketing", "label": "Monthly Active Users\n(marketing)", "type": "metric" }, { "id": "urn:li:dataset:(urn:li:dataPlatform:snowflake,mkt.mart.mau_sessions,PROD)", "label": "mau_sessions", "type": "dataset" }, { "id": "urn:li:dashboard:(looker,growth_weekly)", "label": "growth_weekly", "type": "dashboard" }, { "id": "urn:li:dashboard:(looker,campaign_attribution)", "label": "campaign_attribution", "type": "dashboard" }, { "id": "urn:li:dashboard:(tableau,cmo_review)", "label": "cmo_review", "type": "dashboard" }, { "id": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.user_engagement,PROD)", "label": "user_engagement", "type": "dataset" }, { "id": "urn:li:mlModel:(mlflow,churn_predictor,PROD)", "label": "churn_predictor", "type": "model" }, { "id": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.churn_risk,PROD)", "label": "churn_risk", "type": "dataset" }, { "id": "urn:li:dashboard:(tableau,risk_ops_review)", "label": "risk_ops_review", "type": "dashboard" }, { "id": "urn:li:mlModel:(mlflow,credit_risk_model,PROD)", "label": "credit_risk_model", "type": "model" }, { "id": "urn:li:dashboard:(looker,retention_health)", "label": "retention_health", "type": "dashboard" }, { "id": "urn:li:dataset:(urn:li:dataPlatform:snowflake,mkt.mart.email_targeting,PROD)", "label": "email_targeting", "type": "dataset" }, { "id": "urn:li:dashboard:(looker,crm_campaigns)", "label": "crm_campaigns", "type": "dashboard" }, { "id": "urn:li:mlModel:(mlflow,send_time_optimizer,PROD)", "label": "send_time_optimizer", "type": "model" } ], "edges": [ { "source": "metric::active_user::finance", "target": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.mau_revenue,PROD)" }, { "source": "metric::active_user::finance", "target": "urn:li:dashboard:(looker,fin_exec_overview)" }, { "source": "metric::active_user::finance", "target": "urn:li:dashboard:(looker,fin_board_pack)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.mau_revenue,PROD)", "target": "urn:li:dashboard:(looker,fin_exec_overview)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.mau_revenue,PROD)", "target": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.arpu,PROD)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.mau_revenue,PROD)", "target": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.ltv_features,PROD)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.ltv_features,PROD)", "target": "urn:li:mlModel:(mlflow,ltv_predictor,PROD)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.ltv_features,PROD)", "target": "urn:li:dashboard:(tableau,investor_reporting)" }, { "source": "urn:li:mlModel:(mlflow,ltv_predictor,PROD)", "target": "urn:li:dashboard:(looker,customer_value_forecast)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.arpu,PROD)", "target": "urn:li:dashboard:(looker,fin_board_pack)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.arpu,PROD)", "target": "urn:li:chart:(looker,arpu_trend)" }, { "source": "metric::active_user::marketing", "target": "urn:li:dataset:(urn:li:dataPlatform:snowflake,mkt.mart.mau_sessions,PROD)" }, { "source": "metric::active_user::marketing", "target": "urn:li:dashboard:(looker,growth_weekly)" }, { "source": "metric::active_user::marketing", "target": "urn:li:dashboard:(looker,campaign_attribution)" }, { "source": "metric::active_user::marketing", "target": "urn:li:dashboard:(tableau,cmo_review)" }, { "source": "metric::active_user::marketing", "target": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.user_engagement,PROD)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.user_engagement,PROD)", "target": "urn:li:mlModel:(mlflow,churn_predictor,PROD)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.user_engagement,PROD)", "target": "urn:li:dashboard:(tableau,cmo_review)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.user_engagement,PROD)", "target": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.churn_risk,PROD)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.churn_risk,PROD)", "target": "urn:li:dashboard:(tableau,risk_ops_review)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.churn_risk,PROD)", "target": "urn:li:mlModel:(mlflow,credit_risk_model,PROD)" }, { "source": "urn:li:mlModel:(mlflow,churn_predictor,PROD)", "target": "urn:li:dashboard:(looker,retention_health)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,mkt.mart.mau_sessions,PROD)", "target": "urn:li:dashboard:(looker,growth_weekly)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,mkt.mart.mau_sessions,PROD)", "target": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.user_engagement,PROD)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,mkt.mart.mau_sessions,PROD)", "target": "urn:li:dataset:(urn:li:dataPlatform:snowflake,mkt.mart.email_targeting,PROD)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,mkt.mart.email_targeting,PROD)", "target": "urn:li:dashboard:(looker,crm_campaigns)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,mkt.mart.email_targeting,PROD)", "target": "urn:li:mlModel:(mlflow,send_time_optimizer,PROD)" } ] }, "logic_similarity": 0.133, "name_similarity": 1.0, "rationale": "'Monthly Active Users' (finance) and 'Monthly Active Users' (marketing) share a name but compute differently (logic overlap 13%).", "definitions": [ { "domain": "finance", "owner": "urn:li:corpGroup:finance-analytics", "display_name": "Monthly Active Users", "definition_text": "Users who completed at least one paid transaction in the trailing 30 days.", "sql_logic": "COUNT(DISTINCT user_id) WHERE txn_amount > 0 AND event_date >= CURRENT_DATE - 30", "affected_assets": [ "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.mau_revenue,PROD)", "urn:li:dashboard:(looker,fin_exec_overview)", "urn:li:dashboard:(looker,fin_board_pack)" ], "term_urn": "urn:li:glossaryTerm:finance.active_user", "tags": [ "tier:certified" ], "last_modified": "2026-05-14" }, { "domain": "marketing", "owner": "urn:li:corpGroup:growth-marketing", "display_name": "Monthly Active Users", "definition_text": "Any user with a session or app open in the last 30 days, bots excluded upstream by the safety pipeline.", "sql_logic": "COUNT(DISTINCT user_id) WHERE session_start >= CURRENT_DATE - 30 AND is_bot = false", "affected_assets": [ "urn:li:dataset:(urn:li:dataPlatform:snowflake,mkt.mart.mau_sessions,PROD)", "urn:li:dashboard:(looker,growth_weekly)", "urn:li:dashboard:(looker,campaign_attribution)", "urn:li:dashboard:(tableau,cmo_review)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.user_engagement,PROD)" ], "term_urn": "urn:li:glossaryTerm:marketing.active_user", "tags": [], "last_modified": "2024-03-02" } ], "impact": { "impacted_assets": 22, "asset_breakdown": { "dashboard": 11, "dataset": 7, "model": 4 }, "manual_reconciliation_hours": 11.0, "estimated_manual_cost_usd": 990, "risk_statement": "A wrong 'Active User' silently feeds 15 decision surface(s) (11 dashboards, 4 models) across 2 teams." }, "proposed_reconciliation": { "before": [ { "term_urn": "urn:li:glossaryTerm:finance.active_user", "domain": "finance", "display_name": "Monthly Active Users", "definition": "Users who completed at least one paid transaction in the trailing 30 days.", "status": "active" }, { "term_urn": "urn:li:glossaryTerm:marketing.active_user", "domain": "marketing", "display_name": "Monthly Active Users", "definition": "Any user with a session or app open in the last 30 days, bots excluded upstream by the safety pipeline.", "status": "active" } ], "after": { "canonical_term_id": "active_user", "display_name": "Monthly Active Users", "definition": "CANONICAL DEFINITION of 'Monthly Active Users'. Base (from marketing, highest coverage): Any user with a session or app open in the last 30 days, bots excluded upstream by the safety pipeline. Reconciliation note: 2 teams defined this differently. Agreed computation: COUNT(DISTINCT user_id) WHERE session_start >= CURRENT_DATE - 30 AND is_bot = false. Conflicting variants from finance are deprecated and mapped here.", "linked_assets": [ "urn:li:dashboard:(looker,campaign_attribution)", "urn:li:dashboard:(looker,fin_board_pack)", "urn:li:dashboard:(looker,fin_exec_overview)", "urn:li:dashboard:(looker,growth_weekly)", "urn:li:dashboard:(tableau,cmo_review)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.mau_revenue,PROD)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,mkt.mart.mau_sessions,PROD)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.user_engagement,PROD)" ], "deprecated_terms": [ "urn:li:glossaryTerm:finance.active_user" ], "status": "canonical" } }, "ai_explanation": { "finding": "'Active User' has 2 incompatible definitions across finance and marketing \u2014 same name, different computation.", "evidence": "Logic similarity 13%, confidence 93%. Definitions: finance: \"Users who completed at least one paid transaction in the trailing 30 day\u2026\"; marketing: \"Any user with a session or app open in the last 30 days, bots excluded u\u2026\". Governance signals: marketing: definition stale (last updated 2024-03-02).", "impact": "CRITICAL severity. 22 downstream assets are at risk. A wrong 'Active User' silently feeds 15 decision surface(s) (11 dashboards, 4 models) across 2 teams.", "recommendation": "Escalate immediately: align finance and marketing leadership on a single canonical definition for 'Active User'. Freeze dependent pipelines until resolved. In Connected Mode, Rosetta can execute a human-approved remediation plan and verify the resulting metadata where supported." } }, { "kind": "silent_contradiction", "metric": "arr", "severity": "medium", "confidence": 0.952, "blast_radius": 6, "impacted_assets": [ "urn:li:dashboard:(looker,cfo_monthly)", "urn:li:dashboard:(looker,fin_board_pack)", "urn:li:dashboard:(salesforce,pipeline_dashboard)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.arr,PROD)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.gross_margin,PROD)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,sales.mart.arr_bookings,PROD)" ], "impact_graph": { "nodes": [ { "id": "metric::arr::finance", "label": "Annual Recurring Revenue\n(finance)", "type": "metric" }, { "id": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.arr,PROD)", "label": "arr", "type": "dataset" }, { "id": "urn:li:dashboard:(looker,fin_board_pack)", "label": "fin_board_pack", "type": "dashboard" }, { "id": "urn:li:dashboard:(looker,cfo_monthly)", "label": "cfo_monthly", "type": "dashboard" }, { "id": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.gross_margin,PROD)", "label": "gross_margin", "type": "dataset" }, { "id": "metric::arr::sales", "label": "Annual Recurring Revenue\n(sales)", "type": "metric" }, { "id": "urn:li:dataset:(urn:li:dataPlatform:snowflake,sales.mart.arr_bookings,PROD)", "label": "arr_bookings", "type": "dataset" }, { "id": "urn:li:dashboard:(salesforce,pipeline_dashboard)", "label": "pipeline_dashboard", "type": "dashboard" } ], "edges": [ { "source": "metric::arr::finance", "target": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.arr,PROD)" }, { "source": "metric::arr::finance", "target": "urn:li:dashboard:(looker,fin_board_pack)" }, { "source": "metric::arr::finance", "target": "urn:li:dashboard:(looker,cfo_monthly)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.arr,PROD)", "target": "urn:li:dashboard:(looker,fin_board_pack)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.arr,PROD)", "target": "urn:li:dashboard:(looker,cfo_monthly)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.arr,PROD)", "target": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.gross_margin,PROD)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.gross_margin,PROD)", "target": "urn:li:dashboard:(looker,cfo_monthly)" }, { "source": "metric::arr::sales", "target": "urn:li:dataset:(urn:li:dataPlatform:snowflake,sales.mart.arr_bookings,PROD)" }, { "source": "metric::arr::sales", "target": "urn:li:dashboard:(salesforce,pipeline_dashboard)" }, { "source": "metric::arr::sales", "target": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.gross_margin,PROD)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,sales.mart.arr_bookings,PROD)", "target": "urn:li:dashboard:(salesforce,pipeline_dashboard)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,sales.mart.arr_bookings,PROD)", "target": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.gross_margin,PROD)" } ] }, "logic_similarity": 0.097, "name_similarity": 1.0, "rationale": "'Annual Recurring Revenue' (finance) and 'Annual Recurring Revenue' (sales) share a name but compute differently (logic overlap 10%).", "definitions": [ { "domain": "finance", "owner": "urn:li:corpGroup:finance-analytics", "display_name": "Annual Recurring Revenue", "definition_text": "Sum of all active subscription MRR annualized, excluding one-time fees and professional services.", "sql_logic": "SUM(monthly_subscription_amount * 12) WHERE subscription_status = 'active' AND revenue_type = 'recurring'", "affected_assets": [ "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.arr,PROD)", "urn:li:dashboard:(looker,fin_board_pack)", "urn:li:dashboard:(looker,cfo_monthly)" ], "term_urn": "urn:li:glossaryTerm:finance.arr", "tags": [ "tier:certified" ], "last_modified": "2026-06-30" }, { "domain": "sales", "owner": "urn:li:corpGroup:sales-ops", "display_name": "Annual Recurring Revenue", "definition_text": "Total contract value of all active annual deals, including upsells and expansion revenue booked in period.", "sql_logic": "SUM(contract_value) WHERE deal_type = 'annual' AND stage = 'closed_won' AND contract_status = 'active'", "affected_assets": [ "urn:li:dataset:(urn:li:dataPlatform:snowflake,sales.mart.arr_bookings,PROD)", "urn:li:dashboard:(salesforce,pipeline_dashboard)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.gross_margin,PROD)" ], "term_urn": "urn:li:glossaryTerm:sales.arr", "tags": [], "last_modified": "2023-09-27" } ], "impact": { "impacted_assets": 6, "asset_breakdown": { "dashboard": 3, "dataset": 3 }, "manual_reconciliation_hours": 3.0, "estimated_manual_cost_usd": 270, "risk_statement": "A wrong 'ARR' silently feeds 3 decision surface(s) (3 dashboards, 0 models) across 2 teams." }, "proposed_reconciliation": { "before": [ { "term_urn": "urn:li:glossaryTerm:finance.arr", "domain": "finance", "display_name": "Annual Recurring Revenue", "definition": "Sum of all active subscription MRR annualized, excluding one-time fees and professional services.", "status": "active" }, { "term_urn": "urn:li:glossaryTerm:sales.arr", "domain": "sales", "display_name": "Annual Recurring Revenue", "definition": "Total contract value of all active annual deals, including upsells and expansion revenue booked in period.", "status": "active" } ], "after": { "canonical_term_id": "arr", "display_name": "Annual Recurring Revenue", "definition": "CANONICAL DEFINITION of 'Annual Recurring Revenue'. Base (from finance, highest coverage): Sum of all active subscription MRR annualized, excluding one-time fees and professional services. Reconciliation note: 2 teams defined this differently. Agreed computation: SUM(monthly_subscription_amount * 12) WHERE subscription_status = 'active' AND revenue_type = 'recurring'. Conflicting variants from sales are deprecated and mapped here.", "linked_assets": [ "urn:li:dashboard:(looker,cfo_monthly)", "urn:li:dashboard:(looker,fin_board_pack)", "urn:li:dashboard:(salesforce,pipeline_dashboard)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.arr,PROD)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.gross_margin,PROD)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,sales.mart.arr_bookings,PROD)" ], "deprecated_terms": [ "urn:li:glossaryTerm:sales.arr" ], "status": "canonical" } }, "ai_explanation": { "finding": "'ARR' has 2 incompatible definitions across finance and sales \u2014 same name, different computation.", "evidence": "Logic similarity 10%, confidence 95%. Definitions: finance: \"Sum of all active subscription MRR annualized, excluding one-time fees a\u2026\"; sales: \"Total contract value of all active annual deals, including upsells and e\u2026\". Governance signals: sales: definition stale (last updated 2023-09-27).", "impact": "MEDIUM severity. 6 downstream assets are at risk. A wrong 'ARR' silently feeds 3 decision surface(s) (3 dashboards, 0 models) across 2 teams.", "recommendation": "Open a data governance ticket for 'ARR'. Draft a canonical definition incorporating both teams' intent and circulate for sign-off in the next governance review cycle." } }, { "kind": "silent_contradiction", "metric": "conversion_rate", "severity": "high", "confidence": 0.92, "blast_radius": 11, "impacted_assets": [ "urn:li:dashboard:(looker,marketing_funnel)", "urn:li:dashboard:(looker,product_health)", "urn:li:dashboard:(looker,retention_health)", "urn:li:dashboard:(tableau,cmo_review)", "urn:li:dashboard:(tableau,risk_ops_review)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,mkt.mart.conversion_funnel,PROD)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.churn_risk,PROD)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.user_engagement,PROD)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,prod.mart.trial_conversion,PROD)", "urn:li:mlModel:(mlflow,churn_predictor,PROD)", "urn:li:mlModel:(mlflow,credit_risk_model,PROD)" ], "impact_graph": { "nodes": [ { "id": "metric::conversion_rate::marketing", "label": "Conversion Rate\n(marketing)", "type": "metric" }, { "id": "urn:li:dataset:(urn:li:dataPlatform:snowflake,mkt.mart.conversion_funnel,PROD)", "label": "conversion_funnel", "type": "dataset" }, { "id": "urn:li:dashboard:(looker,marketing_funnel)", "label": "marketing_funnel", "type": "dashboard" }, { "id": "urn:li:dashboard:(tableau,cmo_review)", "label": "cmo_review", "type": "dashboard" }, { "id": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.user_engagement,PROD)", "label": "user_engagement", "type": "dataset" }, { "id": "urn:li:mlModel:(mlflow,churn_predictor,PROD)", "label": "churn_predictor", "type": "model" }, { "id": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.churn_risk,PROD)", "label": "churn_risk", "type": "dataset" }, { "id": "urn:li:dashboard:(tableau,risk_ops_review)", "label": "risk_ops_review", "type": "dashboard" }, { "id": "urn:li:mlModel:(mlflow,credit_risk_model,PROD)", "label": "credit_risk_model", "type": "model" }, { "id": "urn:li:dashboard:(looker,retention_health)", "label": "retention_health", "type": "dashboard" }, { "id": "metric::conversion_rate::product", "label": "Conversion Rate\n(product)", "type": "metric" }, { "id": "urn:li:dataset:(urn:li:dataPlatform:snowflake,prod.mart.trial_conversion,PROD)", "label": "trial_conversion", "type": "dataset" }, { "id": "urn:li:dashboard:(looker,product_health)", "label": "product_health", "type": "dashboard" } ], "edges": [ { "source": "metric::conversion_rate::marketing", "target": "urn:li:dataset:(urn:li:dataPlatform:snowflake,mkt.mart.conversion_funnel,PROD)" }, { "source": "metric::conversion_rate::marketing", "target": "urn:li:dashboard:(looker,marketing_funnel)" }, { "source": "metric::conversion_rate::marketing", "target": "urn:li:dashboard:(tableau,cmo_review)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,mkt.mart.conversion_funnel,PROD)", "target": "urn:li:dashboard:(looker,marketing_funnel)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,mkt.mart.conversion_funnel,PROD)", "target": "urn:li:dashboard:(tableau,cmo_review)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,mkt.mart.conversion_funnel,PROD)", "target": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.user_engagement,PROD)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.user_engagement,PROD)", "target": "urn:li:mlModel:(mlflow,churn_predictor,PROD)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.user_engagement,PROD)", "target": "urn:li:dashboard:(tableau,cmo_review)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.user_engagement,PROD)", "target": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.churn_risk,PROD)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.churn_risk,PROD)", "target": "urn:li:dashboard:(tableau,risk_ops_review)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.churn_risk,PROD)", "target": "urn:li:mlModel:(mlflow,credit_risk_model,PROD)" }, { "source": "urn:li:mlModel:(mlflow,churn_predictor,PROD)", "target": "urn:li:dashboard:(looker,retention_health)" }, { "source": "metric::conversion_rate::product", "target": "urn:li:dataset:(urn:li:dataPlatform:snowflake,prod.mart.trial_conversion,PROD)" }, { "source": "metric::conversion_rate::product", "target": "urn:li:dashboard:(looker,product_health)" }, { "source": "metric::conversion_rate::product", "target": "urn:li:mlModel:(mlflow,churn_predictor,PROD)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,prod.mart.trial_conversion,PROD)", "target": "urn:li:dashboard:(looker,product_health)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,prod.mart.trial_conversion,PROD)", "target": "urn:li:mlModel:(mlflow,churn_predictor,PROD)" } ] }, "logic_similarity": 0.16, "name_similarity": 1.0, "rationale": "'Conversion Rate' (marketing) and 'Conversion Rate' (product) share a name but compute differently (logic overlap 16%).", "definitions": [ { "domain": "marketing", "owner": "urn:li:corpGroup:growth-marketing", "display_name": "Conversion Rate", "definition_text": "Percentage of website visitors who completed any tracked goal (signup, purchase, or trial) in the session.", "sql_logic": "COUNT(DISTINCT goal_completions) / COUNT(DISTINCT sessions) * 100", "affected_assets": [ "urn:li:dataset:(urn:li:dataPlatform:snowflake,mkt.mart.conversion_funnel,PROD)", "urn:li:dashboard:(looker,marketing_funnel)", "urn:li:dashboard:(tableau,cmo_review)" ], "term_urn": "urn:li:glossaryTerm:marketing.conversion_rate", "tags": [], "last_modified": "2026-02-11" }, { "domain": "product", "owner": "urn:li:corpGroup:product-analytics", "display_name": "Conversion Rate", "definition_text": "Percentage of trial users who converted to a paid plan within 14 days of signup.", "sql_logic": "COUNT(DISTINCT user_id WHERE subscription_start <= trial_start + 14) / COUNT(DISTINCT trial_user_id) * 100", "affected_assets": [ "urn:li:dataset:(urn:li:dataPlatform:snowflake,prod.mart.trial_conversion,PROD)", "urn:li:dashboard:(looker,product_health)", "urn:li:mlModel:(mlflow,churn_predictor,PROD)" ], "term_urn": "urn:li:glossaryTerm:product.conversion_rate", "tags": [ "tier:certified" ], "last_modified": "2026-04-08" } ], "impact": { "impacted_assets": 11, "asset_breakdown": { "dashboard": 5, "dataset": 4, "model": 2 }, "manual_reconciliation_hours": 5.5, "estimated_manual_cost_usd": 495, "risk_statement": "A wrong 'Conversion Rate' silently feeds 7 decision surface(s) (5 dashboards, 2 models) across 2 teams." }, "proposed_reconciliation": { "before": [ { "term_urn": "urn:li:glossaryTerm:marketing.conversion_rate", "domain": "marketing", "display_name": "Conversion Rate", "definition": "Percentage of website visitors who completed any tracked goal (signup, purchase, or trial) in the session.", "status": "active" }, { "term_urn": "urn:li:glossaryTerm:product.conversion_rate", "domain": "product", "display_name": "Conversion Rate", "definition": "Percentage of trial users who converted to a paid plan within 14 days of signup.", "status": "active" } ], "after": { "canonical_term_id": "conversion_rate", "display_name": "Conversion Rate", "definition": "CANONICAL DEFINITION of 'Conversion Rate'. Base (from marketing, highest coverage): Percentage of website visitors who completed any tracked goal (signup, purchase, or trial) in the session. Reconciliation note: 2 teams defined this differently. Agreed computation: COUNT(DISTINCT goal_completions) / COUNT(DISTINCT sessions) * 100. Conflicting variants from product are deprecated and mapped here.", "linked_assets": [ "urn:li:dashboard:(looker,marketing_funnel)", "urn:li:dashboard:(looker,product_health)", "urn:li:dashboard:(tableau,cmo_review)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,mkt.mart.conversion_funnel,PROD)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,prod.mart.trial_conversion,PROD)", "urn:li:mlModel:(mlflow,churn_predictor,PROD)" ], "deprecated_terms": [ "urn:li:glossaryTerm:product.conversion_rate" ], "status": "canonical" } }, "ai_explanation": { "finding": "'Conversion Rate' has 2 incompatible definitions across marketing and product \u2014 same name, different computation.", "evidence": "Logic similarity 16%, confidence 92%. Definitions: marketing: \"Percentage of website visitors who completed any tracked goal (signup, p\u2026\"; product: \"Percentage of trial users who converted to a paid plan within 14 days of\u2026\".", "impact": "HIGH severity. 11 downstream assets are at risk. At least one ML model trains on this data \u2014 a wrong definition corrupts model signals silently. A wrong 'Conversion Rate' silently feeds 7 decision surface(s) (5 dashboards, 2 models) across 2 teams.", "recommendation": "Schedule a cross-team definition review for 'Conversion Rate' within this sprint. Propose one canonical term, notify all 11 asset owners, and deprecate the conflicting variant." } }, { "kind": "silent_contradiction", "metric": "customer_ltv", "severity": "high", "confidence": 1.0, "blast_radius": 12, "impacted_assets": [ "urn:li:dashboard:(looker,cfo_monthly)", "urn:li:dashboard:(looker,pricing_experiments)", "urn:li:dashboard:(looker,retention_health)", "urn:li:dashboard:(tableau,cmo_review)", "urn:li:dashboard:(tableau,risk_ops_review)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,ds.mart.ltv_historical,PROD)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.customer_ltv,PROD)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.churn_risk,PROD)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.user_engagement,PROD)", "urn:li:mlModel:(mlflow,churn_predictor,PROD)", "urn:li:mlModel:(mlflow,credit_risk_model,PROD)", "urn:li:mlModel:(mlflow,ltv_forecaster,PROD)" ], "impact_graph": { "nodes": [ { "id": "metric::customer_ltv::finance", "label": "Customer Lifetime Value\n(finance)", "type": "metric" }, { "id": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.customer_ltv,PROD)", "label": "customer_ltv", "type": "dataset" }, { "id": "urn:li:dashboard:(looker,cfo_monthly)", "label": "cfo_monthly", "type": "dashboard" }, { "id": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.user_engagement,PROD)", "label": "user_engagement", "type": "dataset" }, { "id": "urn:li:mlModel:(mlflow,churn_predictor,PROD)", "label": "churn_predictor", "type": "model" }, { "id": "urn:li:dashboard:(tableau,cmo_review)", "label": "cmo_review", "type": "dashboard" }, { "id": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.churn_risk,PROD)", "label": "churn_risk", "type": "dataset" }, { "id": "urn:li:dashboard:(tableau,risk_ops_review)", "label": "risk_ops_review", "type": "dashboard" }, { "id": "urn:li:mlModel:(mlflow,credit_risk_model,PROD)", "label": "credit_risk_model", "type": "model" }, { "id": "urn:li:dashboard:(looker,retention_health)", "label": "retention_health", "type": "dashboard" }, { "id": "metric::customer_ltv::data_science", "label": "LTV\n(data_science)", "type": "metric" }, { "id": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ds.mart.ltv_historical,PROD)", "label": "ltv_historical", "type": "dataset" }, { "id": "urn:li:mlModel:(mlflow,ltv_forecaster,PROD)", "label": "ltv_forecaster", "type": "model" }, { "id": "urn:li:dashboard:(looker,pricing_experiments)", "label": "pricing_experiments", "type": "dashboard" } ], "edges": [ { "source": "metric::customer_ltv::finance", "target": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.customer_ltv,PROD)" }, { "source": "metric::customer_ltv::finance", "target": "urn:li:dashboard:(looker,cfo_monthly)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.customer_ltv,PROD)", "target": "urn:li:dashboard:(looker,cfo_monthly)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.customer_ltv,PROD)", "target": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.user_engagement,PROD)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.user_engagement,PROD)", "target": "urn:li:mlModel:(mlflow,churn_predictor,PROD)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.user_engagement,PROD)", "target": "urn:li:dashboard:(tableau,cmo_review)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.user_engagement,PROD)", "target": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.churn_risk,PROD)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.churn_risk,PROD)", "target": "urn:li:dashboard:(tableau,risk_ops_review)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ml.features.churn_risk,PROD)", "target": "urn:li:mlModel:(mlflow,credit_risk_model,PROD)" }, { "source": "urn:li:mlModel:(mlflow,churn_predictor,PROD)", "target": "urn:li:dashboard:(looker,retention_health)" }, { "source": "metric::customer_ltv::data_science", "target": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ds.mart.ltv_historical,PROD)" }, { "source": "metric::customer_ltv::data_science", "target": "urn:li:mlModel:(mlflow,ltv_forecaster,PROD)" }, { "source": "urn:li:mlModel:(mlflow,ltv_forecaster,PROD)", "target": "urn:li:dashboard:(looker,pricing_experiments)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,ds.mart.ltv_historical,PROD)", "target": "urn:li:mlModel:(mlflow,ltv_forecaster,PROD)" } ] }, "logic_similarity": 0.0, "name_similarity": 1.0, "rationale": "'Customer Lifetime Value' (finance) and 'LTV' (data_science) share a name but compute differently (logic overlap 0%).", "definitions": [ { "domain": "finance", "owner": "urn:li:corpGroup:finance-analytics", "display_name": "Customer Lifetime Value", "definition_text": "Predicted gross margin a single account will generate over a five-year horizon, discounted to present value.", "sql_logic": "SUM(projected_margin * discount_factor) OVER five_year_horizon", "affected_assets": [ "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.customer_ltv,PROD)", "urn:li:dashboard:(looker,cfo_monthly)" ], "term_urn": "urn:li:glossaryTerm:finance.customer_ltv", "tags": [ "pii", "sensitive" ], "last_modified": "2026-03-22" }, { "domain": "data_science", "owner": "", "display_name": "LTV", "definition_text": "Historical dollars collected from each customer since signup. No projection, no discounting.", "sql_logic": "SUM(invoice_amount) GROUP BY customer_id", "affected_assets": [ "urn:li:dataset:(urn:li:dataPlatform:snowflake,ds.mart.ltv_historical,PROD)", "urn:li:mlModel:(mlflow,ltv_forecaster,PROD)" ], "term_urn": "", "tags": [ "pii" ], "last_modified": "2023-11-02" } ], "impact": { "impacted_assets": 12, "asset_breakdown": { "dashboard": 5, "dataset": 4, "model": 3 }, "manual_reconciliation_hours": 6.0, "estimated_manual_cost_usd": 540, "risk_statement": "A wrong 'Customer LTV' silently feeds 8 decision surface(s) (5 dashboards, 3 models) across 2 teams." }, "proposed_reconciliation": { "before": [ { "term_urn": "urn:li:glossaryTerm:finance.customer_ltv", "domain": "finance", "display_name": "Customer Lifetime Value", "definition": "Predicted gross margin a single account will generate over a five-year horizon, discounted to present value.", "status": "active" }, { "term_urn": "", "domain": "data_science", "display_name": "LTV", "definition": "Historical dollars collected from each customer since signup. No projection, no discounting.", "status": "active" } ], "after": { "canonical_term_id": "customer_ltv", "display_name": "Customer Lifetime Value", "definition": "CANONICAL DEFINITION of 'Customer Lifetime Value'. Base (from finance, highest coverage): Predicted gross margin a single account will generate over a five-year horizon, discounted to present value. Reconciliation note: 2 teams defined this differently. Agreed computation: SUM(projected_margin * discount_factor) OVER five_year_horizon. Conflicting variants from data_science are deprecated and mapped here.", "linked_assets": [ "urn:li:dashboard:(looker,cfo_monthly)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,ds.mart.ltv_historical,PROD)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.customer_ltv,PROD)", "urn:li:mlModel:(mlflow,ltv_forecaster,PROD)" ], "deprecated_terms": [], "status": "canonical" } }, "ai_explanation": { "finding": "'Customer LTV' has 2 incompatible definitions across finance and data_science \u2014 same name, different computation.", "evidence": "Logic similarity 0%, confidence 100%. Definitions: finance: \"Predicted gross margin a single account will generate over a five-year h\u2026\"; data_science: \"Historical dollars collected from each customer since signup. No project\u2026\". Governance signals: finance: handles sensitive/PII data; data_science: no owner assigned \u2014 stewardship unclear; data_science: not registered in the business glossary; data_science: handles sensitive/PII data; data_science: definition stale (last updated 2023-11-02).", "impact": "HIGH severity. 12 downstream assets are at risk. At least one ML model trains on this data \u2014 a wrong definition corrupts model signals silently. A wrong 'Customer LTV' silently feeds 8 decision surface(s) (5 dashboards, 3 models) across 2 teams.", "recommendation": "Schedule a cross-team definition review for 'Customer LTV' within this sprint. Propose one canonical term, notify all 12 asset owners, and deprecate the conflicting variant." } }, { "kind": "silent_contradiction", "metric": "revenue", "severity": "medium", "confidence": 0.921, "blast_radius": 6, "impacted_assets": [ "urn:li:dashboard:(looker,cfo_monthly)", "urn:li:dashboard:(looker,fin_exec_overview)", "urn:li:dashboard:(salesforce,pipeline_dashboard)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.gross_margin,PROD)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.net_revenue,PROD)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,sales.mart.bookings,PROD)" ], "impact_graph": { "nodes": [ { "id": "metric::revenue::finance", "label": "Net Revenue\n(finance)", "type": "metric" }, { "id": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.net_revenue,PROD)", "label": "net_revenue", "type": "dataset" }, { "id": "urn:li:dashboard:(looker,fin_exec_overview)", "label": "fin_exec_overview", "type": "dashboard" }, { "id": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.gross_margin,PROD)", "label": "gross_margin", "type": "dataset" }, { "id": "urn:li:dashboard:(looker,cfo_monthly)", "label": "cfo_monthly", "type": "dashboard" }, { "id": "metric::revenue::sales", "label": "Revenue\n(sales)", "type": "metric" }, { "id": "urn:li:dataset:(urn:li:dataPlatform:snowflake,sales.mart.bookings,PROD)", "label": "bookings", "type": "dataset" }, { "id": "urn:li:dashboard:(salesforce,pipeline_dashboard)", "label": "pipeline_dashboard", "type": "dashboard" } ], "edges": [ { "source": "metric::revenue::finance", "target": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.net_revenue,PROD)" }, { "source": "metric::revenue::finance", "target": "urn:li:dashboard:(looker,fin_exec_overview)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.net_revenue,PROD)", "target": "urn:li:dashboard:(looker,fin_exec_overview)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.net_revenue,PROD)", "target": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.gross_margin,PROD)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.gross_margin,PROD)", "target": "urn:li:dashboard:(looker,cfo_monthly)" }, { "source": "metric::revenue::sales", "target": "urn:li:dataset:(urn:li:dataPlatform:snowflake,sales.mart.bookings,PROD)" }, { "source": "metric::revenue::sales", "target": "urn:li:dashboard:(salesforce,pipeline_dashboard)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,sales.mart.bookings,PROD)", "target": "urn:li:dashboard:(salesforce,pipeline_dashboard)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,sales.mart.bookings,PROD)", "target": "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.gross_margin,PROD)" } ] }, "logic_similarity": 0.158, "name_similarity": 1.0, "rationale": "'Net Revenue' (finance) and 'Revenue' (sales) share a name but compute differently (logic overlap 16%).", "definitions": [ { "domain": "finance", "owner": "urn:li:corpGroup:finance-analytics", "display_name": "Net Revenue", "definition_text": "Gross bookings minus refunds and chargebacks, recognized in the period.", "sql_logic": "SUM(booking_amount) - SUM(refund_amount) - SUM(chargeback_amount)", "affected_assets": [ "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.net_revenue,PROD)", "urn:li:dashboard:(looker,fin_exec_overview)" ], "term_urn": "urn:li:glossaryTerm:finance.net_revenue", "tags": [ "tier:certified" ], "last_modified": "2026-06-01" }, { "domain": "sales", "owner": "urn:li:corpGroup:sales-ops", "display_name": "Revenue", "definition_text": "Total value of closed-won deals booked in the period, before refunds.", "sql_logic": "SUM(booking_amount) WHERE stage = 'closed_won'", "affected_assets": [ "urn:li:dataset:(urn:li:dataPlatform:snowflake,sales.mart.bookings,PROD)", "urn:li:dashboard:(salesforce,pipeline_dashboard)" ], "term_urn": "urn:li:glossaryTerm:sales.revenue", "tags": [], "last_modified": "2025-01-19" } ], "impact": { "impacted_assets": 6, "asset_breakdown": { "dashboard": 3, "dataset": 3 }, "manual_reconciliation_hours": 3.0, "estimated_manual_cost_usd": 270, "risk_statement": "A wrong 'Revenue' silently feeds 3 decision surface(s) (3 dashboards, 0 models) across 2 teams." }, "proposed_reconciliation": { "before": [ { "term_urn": "urn:li:glossaryTerm:finance.net_revenue", "domain": "finance", "display_name": "Net Revenue", "definition": "Gross bookings minus refunds and chargebacks, recognized in the period.", "status": "active" }, { "term_urn": "urn:li:glossaryTerm:sales.revenue", "domain": "sales", "display_name": "Revenue", "definition": "Total value of closed-won deals booked in the period, before refunds.", "status": "active" } ], "after": { "canonical_term_id": "revenue", "display_name": "Net Revenue", "definition": "CANONICAL DEFINITION of 'Net Revenue'. Base (from finance, highest coverage): Gross bookings minus refunds and chargebacks, recognized in the period. Reconciliation note: 2 teams defined this differently. Agreed computation: SUM(booking_amount) - SUM(refund_amount) - SUM(chargeback_amount). Conflicting variants from sales are deprecated and mapped here.", "linked_assets": [ "urn:li:dashboard:(looker,fin_exec_overview)", "urn:li:dashboard:(salesforce,pipeline_dashboard)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,fin.mart.net_revenue,PROD)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,sales.mart.bookings,PROD)" ], "deprecated_terms": [ "urn:li:glossaryTerm:sales.revenue" ], "status": "canonical" } }, "ai_explanation": { "finding": "'Revenue' has 2 incompatible definitions across finance and sales \u2014 same name, different computation.", "evidence": "Logic similarity 16%, confidence 92%. Definitions: finance: \"Gross bookings minus refunds and chargebacks, recognized in the period.\"; sales: \"Total value of closed-won deals booked in the period, before refunds.\". Governance signals: sales: definition stale (last updated 2025-01-19).", "impact": "MEDIUM severity. 6 downstream assets are at risk. A wrong 'Revenue' silently feeds 3 decision surface(s) (3 dashboards, 0 models) across 2 teams.", "recommendation": "Open a data governance ticket for 'Revenue'. Draft a canonical definition incorporating both teams' intent and circulate for sign-off in the next governance review cycle." } }, { "kind": "hidden_synonym", "metric": "customer_churn~attrition", "severity": "medium", "confidence": 0.808, "blast_radius": 6, "impacted_assets": [ "urn:li:dashboard:(looker,cs_health)", "urn:li:dashboard:(looker,product_health)", "urn:li:dashboard:(looker,retention_health)", "urn:li:dashboard:(tableau,cs_qbr)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,cs.mart.attrition,PROD)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,prod.mart.churn,PROD)" ], "impact_graph": { "nodes": [ { "id": "metric::customer_churn::product", "label": "Churn Rate\n(product)", "type": "metric" }, { "id": "urn:li:dataset:(urn:li:dataPlatform:snowflake,prod.mart.churn,PROD)", "label": "churn", "type": "dataset" }, { "id": "urn:li:dashboard:(looker,product_health)", "label": "product_health", "type": "dashboard" }, { "id": "metric::attrition::customer_success", "label": "Customer Attrition\n(customer_success)", "type": "metric" }, { "id": "urn:li:dataset:(urn:li:dataPlatform:snowflake,cs.mart.attrition,PROD)", "label": "attrition", "type": "dataset" }, { "id": "urn:li:dashboard:(looker,cs_health)", "label": "cs_health", "type": "dashboard" }, { "id": "urn:li:dashboard:(tableau,cs_qbr)", "label": "cs_qbr", "type": "dashboard" }, { "id": "urn:li:dashboard:(looker,retention_health)", "label": "retention_health", "type": "dashboard" } ], "edges": [ { "source": "metric::customer_churn::product", "target": "urn:li:dataset:(urn:li:dataPlatform:snowflake,prod.mart.churn,PROD)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,prod.mart.churn,PROD)", "target": "urn:li:dashboard:(looker,product_health)" }, { "source": "metric::attrition::customer_success", "target": "urn:li:dataset:(urn:li:dataPlatform:snowflake,cs.mart.attrition,PROD)" }, { "source": "metric::attrition::customer_success", "target": "urn:li:dashboard:(looker,cs_health)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,cs.mart.attrition,PROD)", "target": "urn:li:dashboard:(tableau,cs_qbr)" }, { "source": "urn:li:dataset:(urn:li:dataPlatform:snowflake,cs.mart.attrition,PROD)", "target": "urn:li:dashboard:(looker,retention_health)" } ] }, "logic_similarity": 0.769, "name_similarity": 0.0, "rationale": "'Churn Rate' and 'Customer Attrition' appear to be the same metric under different names (logic overlap 77%).", "definitions": [ { "domain": "product", "owner": "urn:li:corpGroup:product-analytics", "display_name": "Churn Rate", "definition_text": "Share of paying customers who cancelled their subscription in the period.", "sql_logic": "COUNT(DISTINCT customer_id) WHERE subscription_status = 'cancelled' / COUNT(DISTINCT customer_id)", "affected_assets": [ "urn:li:dataset:(urn:li:dataPlatform:snowflake,prod.mart.churn,PROD)" ], "term_urn": "urn:li:glossaryTerm:product.customer_churn", "tags": [], "last_modified": "2026-01-15" }, { "domain": "customer_success", "owner": "urn:li:corpGroup:customer-success", "display_name": "Customer Attrition", "definition_text": "Fraction of paying customers who cancelled their subscription during the period.", "sql_logic": "COUNT(DISTINCT customer_id) WHERE subscription_status = 'cancelled' / COUNT(DISTINCT customer_id)", "affected_assets": [ "urn:li:dataset:(urn:li:dataPlatform:snowflake,cs.mart.attrition,PROD)", "urn:li:dashboard:(looker,cs_health)" ], "term_urn": "urn:li:glossaryTerm:cs.attrition", "tags": [], "last_modified": "2025-11-03" } ], "impact": { "impacted_assets": 6, "asset_breakdown": { "dashboard": 4, "dataset": 2 }, "manual_reconciliation_hours": 3.0, "estimated_manual_cost_usd": 270, "risk_statement": "A wrong 'Customer Churn vs Attrition' silently feeds 4 decision surface(s) (4 dashboards, 0 models) across 2 teams." }, "proposed_reconciliation": { "before": [ { "term_urn": "urn:li:glossaryTerm:product.customer_churn", "domain": "product", "display_name": "Churn Rate", "definition": "Share of paying customers who cancelled their subscription in the period.", "status": "active" }, { "term_urn": "urn:li:glossaryTerm:cs.attrition", "domain": "customer_success", "display_name": "Customer Attrition", "definition": "Fraction of paying customers who cancelled their subscription during the period.", "status": "active" } ], "after": { "canonical_term_id": "attrition", "display_name": "Customer Attrition", "definition": "CANONICAL DEFINITION unifying synonyms: Customer Attrition / Churn Rate. Definition: Fraction of paying customers who cancelled their subscription during the period. Computation: COUNT(DISTINCT customer_id) WHERE subscription_status = 'cancelled' / COUNT(DISTINCT customer_id).", "linked_assets": [ "urn:li:dashboard:(looker,cs_health)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,cs.mart.attrition,PROD)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,prod.mart.churn,PROD)" ], "deprecated_terms": [ "urn:li:glossaryTerm:product.customer_churn" ], "status": "canonical" } }, "ai_explanation": { "finding": "'Customer Churn' and 'Attrition' are duplicate metrics across product and customer_success \u2014 same logic, different names.", "evidence": "Logic similarity 77%, confidence 81%. Definitions: product: \"Share of paying customers who cancelled their subscription in the period\u2026\"; customer_success: \"Fraction of paying customers who cancelled their subscription during the\u2026\".", "impact": "MEDIUM severity. 6 downstream assets are at risk. A wrong 'Customer Churn vs Attrition' silently feeds 4 decision surface(s) (4 dashboards, 0 models) across 2 teams.", "recommendation": "Open a data governance ticket for 'Customer Churn vs Attrition'. Draft a canonical definition incorporating both teams' intent and circulate for sign-off in the next governance review cycle." } } ], "rosetta_provenance": { "generator": "Rosetta \u2014 https://github.com/jdinovitser/rosetta-datahub", "demo_mode": false, "statement": "Generated against a live DataHub instance. Data source is the connected DataHub graph.", "rosetta_constructed": [ "Conflict analysis (rosetta/detector.py)", "Blast-radius walk (rosetta/orchestrator.py)", "Canonical proposals (rosetta/broker.py)" ] } }