Quality Score
92.4
Target ≥ 90
One Data Governance · Federated Model
Governance command center
Central rules with domain autonomy — standards, quality, security, and AI context governed as one stack from ontology to dataset.
Contract Fulfillment
93%
Target ≥ 90%
Security Compliance
94%
Target ≥ 95%
Labeled Assets
98%
UC coverage goal 100%
Governance Capabilities
Drill into standards, models, quality, security, and AI governance
Asset Catalog
Unified metadata directory, search, tags, and model linkage.
Enter →Data Standards
Business, technical, and management attributes — publish & reuse.
Enter →Data Contracts
ODCS lifecycle — Schema, quality, SLA, security, aiContext & breaches.
Enter →Data Models / Ontology
Metadata ingest, model list, and semantic mapping to physical tables.
Enter →Quality Management
Rules, check tasks, scoring, alerts, and quality SLA config.
Enter →Security Management
Classification, masking, DPIA, access audit, and unsafe alerts.
Enter →AI Governance
AI data contracts, model-data lineage, bias reports, AI asset catalog.
Enter →Governance Ops Monitor
Weekly trends and actionable alerts — click a KPI above to filter
Quality trend
92.4
↑ 1.1 pts
Contract rate
93%
↑ 2%
Security rate
94%
↓ 1%
Layered Execution Model
Hover each layer to see what it defines
Ontology Model Layer
L1
Defines “what it is” — semantic authority, Value Types, shared properties. Define once, reuse globally.
Data Contract Layer (Output Port)
L2
Defines “what is promised” — Schema + quality rules + SLA + security + aiContext. Rules derive from Value Types.
Data Product Layer
L3
Defines “who is responsible” — Owner / Domain / Input Ports / lineage. Dataset hangs on Output Port with contract.
Dataset Physical Layer
L4
Executes the first line of defense — DDL constraints / RLS / PII desensitization.