Governance Drift: How to detect degradation early and respond without panic fixes

{
  "id": "db_governance_drift_detection_response_v0_1",
  "type": "decision_block",
  "title": "Governance Drift: How to detect degradation early and respond without panic fixes",
  "dama_alignment": {
    "knowledge_areas": [
      "Data Governance",
      "Data Quality Management",
      "Metadata Management",
      "Data Integration & Interoperability"
    ],
    "principles": [
      "Governance is a living system that drifts under operational pressure",
      "Leading indicators beat lagging indicators",
      "Drift must trigger a controlled response (not ad-hoc rule tightening)"
    ]
  },
  "scope": {
    "domains": [
      "Business Partner",
      "Material",
      "Reference Data"
    ],
    "processes": [
      "Change Request",
      "Approval",
      "Exception Handling",
      "Replication",
      "Rules Maintenance"
    ],
    "systems": [
      "MDG",
      "S/4HANA",
      "Downstream Consumers",
      "Monitoring/Reporting"
    ]
  },
  "tags": [
    "governance_drift",
    "operating_model",
    "leading_indicators",
    "bypass",
    "exceptions",
    "mdg"
  ],
  "decision_question": "How do we detect governance drift early and apply the right intervention (process, ownership, rules, tooling) without increasing bypass risk?",
  "context": {
    "when_to_use": [
      "3–12 months after go-live (classic drift window)",
      "Business starts complaining that MDG is slow again",
      "Exception volume increases and becomes routine",
      "Rule changes become frequent and reactive",
      "Downstream manual fixes start rising"
    ],
    "preconditions": [
      "Basic metrics collection exists or can be introduced quickly",
      "RACI/ownership is defined (even if imperfect)",
      "Exception mechanism exists or is planned"
    ]
  },
  "inputs": {
    "signals_observable": {
      "leading_indicators": [
        "bypass_rate is trending up (changes done outside MDG)",
        "exception_rate is trending up (exceptions per 100 CRs)",
        "approval_cycle_time_p90 is trending up (pain indicator)",
        "rejection_without_comment_rate > 0 (or trending up)",
        "backlog_aging_over_sla is rising",
        "cr_abandonment_rate is rising (users start but do not finish)"
      ],
      "lagging_indicators": [
        "dq_defect_rate downstream is rising (incidents due to master data)",
        "manual_post_replication_fix_rate is rising",
        "replication_error_rate_semantic is rising",
        "audit findings / recurring control failures"
      ],
      "qualitative_signals": [
        "People say: 'it's faster to just fix in S/4'",
        "Approvers are unavailable or approvals feel random",
        "Teams create 'temporary spreadsheets' that never end"
      ]
    },
    "constraints": [
      "Some drift is normal during peak periods and cutover windows",
      "Over-tightening rules often increases bypass",
      "Ownership gaps cannot be fixed by IT alone",
      "Operational urgency may require temporary governance"
    ],
    "stakeholders": [
      "Data Owner",
      "Data Steward",
      "MDG/IT Operations",
      "Business Process Owners",
      "Architecture/Integration",
      "Compliance/Audit (if applicable)"
    ]
  },
  "options": [
    {
      "id": "A",
      "name": "Controlled Drift Response Playbook (recommended)",
      "summary": "Use thresholds and response levels: diagnose root cause category and apply targeted interventions (ownership/process/rules/tech).",
      "tradeoffs": [
        "Requires discipline and a small governance cadence",
        "Needs agreement on thresholds and response ownership"
      ]
    },
    {
      "id": "B",
      "name": "Rule tightening (common but risky)",
      "summary": "When drift appears, increase blocking validations and approvals to regain control.",
      "tradeoffs": [
        "Feels like control",
        "Usually increases bypass and reduces trust"
      ]
    },
    {
      "id": "C",
      "name": "Ignore drift until incidents happen (avoid)",
      "summary": "No action until a major failure forces emergency fixes.",
      "tradeoffs": [
        "No short-term effort",
        "High long-term cost, reputational damage"
      ]
    }
  ],
  "decision_logic": {
    "thresholds_and_levels": [
      {
        "level": "L1 (early drift)",
        "trigger_examples": [
          "exception_rate increases 20–30% for 2 consecutive weeks",
          "approval_cycle_time_p90 exceeds SLA for 2 consecutive periods",
          "rework_loops_per_cr rises noticeably"
        ],
        "goal": "Reduce friction and clarify standards before bypass becomes normal."
      },
      {
        "level": "L2 (active drift)",
        "trigger_examples": [
          "bypass_rate shows sustained increase",
          "backlog_aging_over_sla grows week over week",
          "cr_abandonment_rate increases"
        ],
        "goal": "Restore usability and accountability; prevent shadow maintenance."
      },
      {
        "level": "L3 (drift with impact)",
        "trigger_examples": [
          "downstream incidents due to master data increase",
          "replication semantic errors rise",
          "audit/control failures repeat"
        ],
        "goal": "Stabilize operations and redesign controls/ownership if needed."
      }
    ],
    "root_cause_categories": [
      {
        "category": "Ownership drift",
        "signals": [
          "approvals stall on specific roles",
          "rejects are inconsistent",
          "people say 'not my job'"
        ],
        "interventions": [
          "re-assign accountable owners per attribute group",
          "add escalation and backup approvers",
          "limit approval scope to true decision rights"
        ]
      },
      {
        "category": "Process friction drift",
        "signals": [
          "cycle time p90 rising",
          "abandonment rising",
          "rework loops rising"
        ],
        "interventions": [
          "introduce risk-tier routing",
          "downgrade low-impact rules to warnings",
          "improve UI guidance and pre-checks"
        ]
      },
      {
        "category": "Rule sprawl drift",
        "signals": [
          "more and more validations added reactively",
          "high blocking error rate",
          "exceptions concentrate on a few rules"
        ],
        "interventions": [
          "rule lifecycle review (keep/kill/simplify)",
          "tie each rule to business definition + owner",
          "add derivations to reduce manual entry"
        ]
      },
      {
        "category": "Integration/ops drift",
        "signals": [
          "replication errors increase",
          "manual fixes after replication rise",
          "monitoring alerts ignored"
        ],
        "interventions": [
          "separate semantic vs technical errors",
          "define queue ownership and runbooks",
          "move business validations to MDG (single enforcement point)"
        ]
      }
    ],
    "preferred_option_rules": [
      {
        "if": [
          "Any leading indicator trends up for 2+ cycles",
          "Team begins discussing bypass as normal"
        ],
        "then": "Choose Option A: run drift diagnosis and apply targeted intervention based on root cause category."
      },
      {
        "if": [
          "Reaction is to add more blocking rules",
          "Bypass rate is already rising"
        ],
        "then": "Avoid Option B; focus on reducing friction and fixing ownership first."
      }
    ],
    "anti_patterns_to_avoid": [
      "Tightening controls without measuring bypass",
      "Treating drift as a technical problem only",
      "No cadence: drift is noticed only during crises",
      "Fixing symptoms (exceptions) without addressing root causes"
    ]
  },
  "expected_outcomes": {
    "positive": [
      "Early detection prevents crisis-driven changes",
      "Governance remains usable and trusted",
      "Lower bypass and exception repetition over time",
      "Stable operating model after go-live"
    ],
    "possible_negative": [
      "Requires ongoing governance cadence (weekly/biweekly)",
      "Interventions may expose ownership conflicts"
    ]
  },
  "controls_enforcement": {
    "policies": [
      {
        "id": "pol_drift_is_measured_and_managed_v0_1",
        "statement": "Governance drift must be measured via leading indicators and managed with defined response levels."
      }
    ],
    "standards": [
      {
        "id": "std_drift_dashboard_minimum_v0_1",
        "statement": "Maintain a drift dashboard with at least: bypass rate, exception rate, approval p90, backlog aging, and rework loops."
      },
      {
        "id": "std_drift_response_cadence_v0_1",
        "statement": "Review drift indicators on a fixed cadence (weekly/biweekly) with assigned action owners."
      }
    ],
    "technical_controls": [
      "Dashboards/alerts for leading indicators (trend-based, not single spikes)",
      "Exception analytics by rule/attribute group",
      "Approval backlog monitoring by role",
      "Replication error categorization and ownership"
    ],
    "operating_cadence": {
      "frequency": "weekly_or_biweekly",
      "agenda": [
        "Review leading indicators trends",
        "Identify top 3 drift drivers",
        "Assign interventions with owners and deadlines",
        "Track whether bypass/exceptions reduce after intervention"
      ],
      "roles_present": [
        "Data Owner representative",
        "Data Steward lead",
        "MDG/IT Ops",
        "Integration/Architecture (as needed)"
      ]
    }
  },
  "owner_rights_raci": {
    "accountable": [
      "Data Governance Lead / Data Owner Council (depending on org)"
    ],
    "responsible": [
      "Data Steward Lead",
      "MDG/IT Ops Lead"
    ],
    "consulted": [
      "Business Process Owners",
      "Architecture/Integration",
      "Compliance/Audit (if L3)"
    ],
    "informed": [
      "Requesters",
      "Downstream System Owners",
      "Support Teams"
    ]
  },
  "metrics": {
    "leading_indicators": [
      {
        "name": "bypass_rate",
        "definition": "Changes made outside governance / total changes"
      },
      {
        "name": "exception_rate",
        "definition": "Exceptions per 100 CRs"
      },
      {
        "name": "approval_cycle_time_p90",
        "definition": "90th percentile approval cycle time"
      },
      {
        "name": "backlog_aging_over_sla",
        "definition": "Approvals older than SLA"
      },
      {
        "name": "rework_loops_per_cr",
        "definition": "Average reject/resubmit loops"
      },
      {
        "name": "cr_abandonment_rate",
        "definition": "CRs started but not completed"
      }
    ],
    "lagging_indicators": [
      {
        "name": "dq_defect_incident_rate",
        "definition": "Incidents caused by master data defects"
      },
      {
        "name": "manual_post_replication_fix_rate",
        "definition": "Manual fixes after replication"
      },
      {
        "name": "replication_error_rate_semantic",
        "definition": "Semantic data errors during replication"
      }
    ],
    "target_hint": "Use trends and thresholds per domain; focus on reducing bypass and repeat exceptions as primary health signals."
  },
  "examples_generic": [
    {
      "scenario": "After go-live, approval p90 grows from 2 days to 9 days; exceptions double; teams start updating S/4 directly.",
      "application": "Declare drift level L2; apply risk-tier routing, reduce low-impact blocking rules, add escalation and backup approvers."
    },
    {
      "scenario": "Exception rate concentrates on two rules and repeats weekly; stewards approve them routinely.",
      "application": "Diagnose rule sprawl drift; redesign those rules, add derivations, tie to glossary, and enforce expiry with post-review."
    },
    {
      "scenario": "Replication semantic errors rise though MDG approvals succeed; support team applies manual fixes.",
      "application": "Diagnose integration/ops drift; consolidate enforcement point, categorize errors, assign queue ownership and runbooks."
    }
  ],
  "version": "0.1",
  "status": "draft",
  "meta": {
    "schema": "dkharlanau.dataset.byte",
    "schema_version": "1.1",
    "dataset": "DAMA",
    "source_project": "cv-ai",
    "source_path": "DAMA/db_governance_drift_detection_response_v0_1.json",
    "generated_at_utc": "2026-02-03T14:33:32+00:00",
    "creator": {
      "name": "Dzmitryi Kharlanau",
      "role": "SAP Lead",
      "website": "https://dkharlanau.github.io",
      "linkedin": "https://www.linkedin.com/in/dkharlanau"
    },
    "attribution": {
      "attribution_required": true,
      "preferred_citation": "Dzmitryi Kharlanau (SAP Lead). Dataset bytes: https://dkharlanau.github.io"
    },
    "license": {
      "name": "",
      "spdx": "",
      "url": ""
    },
    "links": {
      "website": "https://dkharlanau.github.io",
      "linkedin": "https://www.linkedin.com/in/dkharlanau"
    },
    "contact": {
      "preferred": "linkedin",
      "linkedin": "https://www.linkedin.com/in/dkharlanau"
    },
    "canonical_url": "https://dkharlanau.github.io/datasets/DAMA/db_governance_drift_detection_response_v0_1.json",
    "created_at_utc": "2026-02-03T14:33:32+00:00",
    "updated_at_utc": "2026-02-03T15:29:02+00:00",
    "provenance": {
      "source_type": "chat_export_extraction",
      "note": "Extracted and curated by Dzmitryi Kharlanau; enriched for attribution and crawler indexing."
    },
    "entity_type": "data_governance_byte",
    "entity_subtype": "version:0.1",
    "summary": "Governance Drift: How to detect degradation early and respond without panic fixes"
  }
}