MDG Governance Maturity Model: assess current level and choose the next upgrade (Level 1–5)

{
  "id": "db_governance_maturity_model_mdg_v0_1",
  "type": "decision_block",
  "title": "MDG Governance Maturity Model: assess current level and choose the next upgrade (Level 1–5)",
  "dama_alignment": {
    "knowledge_areas": [
      "Data Governance",
      "Data Quality Management",
      "Metadata Management",
      "Reference & Master Data",
      "Data Integration & Interoperability"
    ],
    "principles": [
      "Maturity is measured by behavior and outcomes, not documents",
      "Each maturity level has a specific failure mode",
      "Next upgrades should target the bottleneck of the current level"
    ]
  },
  "scope": {
    "domains": [
      "Business Partner",
      "Material",
      "Reference Data"
    ],
    "processes": [
      "Governance Assessment",
      "Operating Model Improvement",
      "Controls & Workflow Evolution",
      "Metrics and Drift Management"
    ],
    "systems": [
      "MDG",
      "S/4HANA",
      "Downstream Consumers",
      "Reporting/Monitoring"
    ]
  },
  "tags": [
    "maturity_model",
    "assessment",
    "roadmap",
    "mdg",
    "governance",
    "continuous_improvement"
  ],
  "decision_question": "What is our current MDG governance maturity level, what is the main bottleneck, and what exact upgrades should we implement next to move one level up without over-engineering?",
  "context": {
    "when_to_use": [
      "Post go-live (Day-2/Day-100) and governance feels unstable",
      "Stakeholders argue about 'how mature we are'",
      "You need a roadmap for the next quarter(s)",
      "Drift indicators are rising and you need structured improvement"
    ],
    "preconditions": [
      "At least minimal governance scope exists (some workflows/rules)",
      "You can observe basic process signals (cycle times, exceptions, bypass, incidents)"
    ]
  },
  "inputs": {
    "signals_observable": [
      "Bypass and manual fixes occur",
      "Rules exist but are inconsistent or undocumented",
      "Approvals are unpredictable or depend on individuals",
      "Exceptions are common and not time-bound",
      "Incidents recur and root causes are not addressed"
    ],
    "constraints": [
      "Do not jump multiple maturity levels at once",
      "Maturity improvements require ownership and cadence, not only IT changes",
      "Different domains may be at different levels"
    ],
    "stakeholders": [
      "Data Governance Lead",
      "Data Owners",
      "Data Stewards",
      "MDG/IT Ops",
      "Business Process Owners",
      "Architecture/Integration"
    ]
  },
  "options": [
    {
      "id": "A",
      "name": "Use maturity model to pick next-level upgrades (recommended)",
      "summary": "Assess current level using observable criteria; implement the smallest set of upgrades that move you one level up.",
      "tradeoffs": [
        "Requires honest measurement and transparency",
        "Some stakeholders resist being labeled 'low maturity'"
      ]
    },
    {
      "id": "B",
      "name": "Treat maturity as documentation (avoid)",
      "summary": "Create policies and decks but do not change operations and metrics.",
      "tradeoffs": [
        "Looks good",
        "Does not reduce bypass or incidents"
      ]
    },
    {
      "id": "C",
      "name": "Over-engineer to Level 5 immediately (avoid)",
      "summary": "Implement complex councils, heavy tooling, and many rules at once.",
      "tradeoffs": [
        "Feels comprehensive",
        "Creates friction and governance collapse"
      ]
    }
  ],
  "decision_logic": {
    "assessment_rules": [
      {
        "rule": "Assess per domain and per Tier 1 attribute groups",
        "details": "Do not average everything; maturity differs by domain and critical attributes."
      },
      {
        "rule": "Use observable evidence",
        "details": "Cycle times, exceptions, bypass, incident repeat rates, ownership coverage, rule registry coverage."
      }
    ],
    "levels": [
      {
        "level": 1,
        "name": "Ad-hoc / firefighting",
        "signature": [
          "Data is fixed wherever it breaks (often downstream)",
          "No stable ownership; approvals are informal",
          "Incidents repeat; manual corrections are normal"
        ],
        "dominant_failure_mode": "Shadow maintenance becomes the real system of record.",
        "key_indicators": [
          "High bypass_rate",
          "High manual_post_replication_fix_rate",
          "No SLA/cadence",
          "No rule registry"
        ],
        "next_upgrades_to_level_2": [
          "Define RACI + minimum decision rights for Tier 1 attribute groups",
          "Introduce exception mechanism with expiry (even simple)",
          "Assign queue ownership and basic runbooks",
          "Start tracking minimum leading indicators (bypass, exceptions, approval p90)"
        ]
      },
      {
        "level": 2,
        "name": "Defined basics (still fragile)",
        "signature": [
          "Workflows exist, but inconsistent and person-dependent",
          "Rules exist, but many are copied and cause friction",
          "Exceptions exist but may be overused"
        ],
        "dominant_failure_mode": "Rules and approvals create friction → bypass rises.",
        "key_indicators": [
          "approval_cycle_time_p90 unstable",
          "exception_rate rising",
          "rework_loops_per_cr high",
          "ownership exists on paper"
        ],
        "next_upgrades_to_level_3": [
          "Attribute-group ownership (beyond domain RACI)",
          "Risk-tier routing for approvals and rule severities",
          "Minimal glossary linked to critical rules",
          "Introduce rule lifecycle reviews (keep/kill/simplify)"
        ]
      },
      {
        "level": 3,
        "name": "Operationally managed",
        "signature": [
          "Governance has cadence and a backlog (Run + Improve)",
          "Owners are active for critical attribute groups",
          "Rules are justified and monitored"
        ],
        "dominant_failure_mode": "Slow drift over months if monitoring and lifecycle are weak.",
        "key_indicators": [
          "Stable or improving bypass_rate",
          "Exceptions time-bound and reviewed",
          "Backlog burn rate exists",
          "Rule registry coverage good for Tier 1"
        ],
        "next_upgrades_to_level_4": [
          "Governance drift detection with thresholds and response playbook",
          "Metrics framework with response rules",
          "Replication error taxonomy and ownership enforced",
          "Domain/attribute prioritization to prevent scope creep"
        ]
      },
      {
        "level": 4,
        "name": "Measured & resilient",
        "signature": [
          "Leading indicators prevent crises",
          "Drift response is controlled, not reactive",
          "Bulk changes follow risk-tier controls",
          "Definitions and rules evolve predictably"
        ],
        "dominant_failure_mode": "Complexity growth (too many consumers, rules, and edge cases).",
        "key_indicators": [
          "Drift alerts trigger interventions and improvements",
          "Repeat exceptions and repeat incidents trend down",
          "Duplicate rule count decreases",
          "High Tier 1 stability with low Tier 3 friction"
        ],
        "next_upgrades_to_level_5": [
          "Consumer-driven governance (data products mindset)",
          "Automation readiness: recommendations + human-in-the-loop",
          "Continuous improvement loops across domains (standardized playbooks)",
          "Formal measurement of decision quality and consistency"
        ]
      },
      {
        "level": 5,
        "name": "Adaptive / optimized",
        "signature": [
          "Governance is embedded in operations and product thinking",
          "Decisions are consistent across regions and changes",
          "Automation supports stewards/owners (recommendations, detection)"
        ],
        "dominant_failure_mode": "Over-automation or optimization that ignores human context.",
        "key_indicators": [
          "Very low bypass and manual fixes",
          "High decision consistency rate",
          "Strong consumer satisfaction and predictable change throughput"
        ],
        "next_upgrades": [
          "Keep optimizing based on consumer outcomes and risk changes",
          "Maintain human oversight for high-risk decisions"
        ]
      }
    ],
    "preferred_option_rules": [
      {
        "if": [
          "Stakeholders want a roadmap and alignment",
          "You see drift or repeated incidents"
        ],
        "then": "Choose Option A: assess honestly per domain and implement only next-level upgrades."
      },
      {
        "if": [
          "Someone wants to jump to complex governance immediately"
        ],
        "then": "Reject Option C; enforce one-level-up rule to avoid friction and bypass."
      }
    ],
    "anti_patterns_to_avoid": [
      "Calling yourself Level 4 because policies exist",
      "Implementing Level 5 automation while Level 2 basics are broken",
      "Averaging maturity across domains (hides critical weaknesses)",
      "Improving everything at once (no bottleneck focus)"
    ]
  },
  "expected_outcomes": {
    "positive": [
      "Shared language for maturity and priorities",
      "Clear roadmap with minimal but effective upgrades",
      "Reduced drift and fewer repeated incidents over time",
      "Better justification for governance investments"
    ],
    "possible_negative": [
      "Political resistance to maturity assessment",
      "Requires evidence and transparency"
    ]
  },
  "controls_enforcement": {
    "policies": [
      {
        "id": "pol_maturity_is_evidence_based_v0_1",
        "statement": "Governance maturity must be assessed using observable outcomes and operational evidence, not documentation volume."
      }
    ],
    "standards": [
      {
        "id": "std_one_level_up_upgrade_v0_1",
        "statement": "Improvements must target the next maturity level bottleneck; avoid multi-level jumps."
      }
    ],
    "technical_controls": [
      "Minimum evidence dashboard for assessment (leading + lagging indicators)",
      "Rule registry and glossary coverage reports",
      "Operating cadence records and backlog tracking",
      "Drift playbook execution log"
    ]
  },
  "owner_rights_raci": {
    "accountable": [
      "Data Governance Lead / Sponsor"
    ],
    "responsible": [
      "Data Steward Lead",
      "MDG/IT Ops Lead"
    ],
    "consulted": [
      "Data Owners",
      "Business Process Owners",
      "Architecture/Integration"
    ],
    "informed": [
      "Requesters",
      "Support Teams",
      "Downstream System Owners"
    ]
  },
  "metrics": {
    "assessment_core": [
      {
        "name": "bypass_rate",
        "definition": "Changes outside governance / total changes"
      },
      {
        "name": "exception_rate",
        "definition": "Exceptions per 100 CRs"
      },
      {
        "name": "approval_cycle_time_p90",
        "definition": "90th percentile approval time"
      },
      {
        "name": "manual_post_replication_fix_rate",
        "definition": "Manual fixes after replication"
      },
      {
        "name": "repeat_incident_rate",
        "definition": "Recurring incident categories within 30/60 days"
      },
      {
        "name": "rule_registry_coverage_tier1",
        "definition": "% of Tier 1 rules with registry entries and owners"
      },
      {
        "name": "glossary_coverage_tier1",
        "definition": "% of Tier 1 attribute groups with definitions and owners"
      }
    ],
    "target_hint": "Use trends and thresholds; assess per domain and Tier 1 attribute groups rather than global averages."
  },
  "examples_generic": [
    {
      "scenario": "BP domain has workflows but approvals are inconsistent; exceptions are common; users bypass for urgent orders.",
      "application": "Assess as Level 2; upgrade to Level 3 by implementing attribute-group ownership, risk-tier routing, and minimal glossary links."
    },
    {
      "scenario": "Material domain is stable; drift is detected early via leading indicators; bulk changes are controlled.",
      "application": "Assess as Level 4; move toward Level 5 by adding consumer-driven governance and automation readiness with human oversight."
    }
  ],
  "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_maturity_model_mdg_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_maturity_model_mdg_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": "MDG Governance Maturity Model: assess current level and choose the next upgrade (Level 1–5)"
  }
}