TDTrusted Datafor Trusted Decisions

Delivery Framework

From client problem to trusted data platform delivery.

The Framework Backbone explains the architecture. The Delivery Lifecycle turns it into client work: assessment, gap analysis, roadmap, architecture, prototype, governance, operation, continuous improvement, and decision adoption.

AssessCapability Assessment

Score the current state across reality, representation, identity, data quality, semantic layer, knowledge, and governance.

DiscoverReality and Representation Discovery

Map stakeholders, entities, observations, representations, source systems, boundaries, and information flows.

DesignArchitecture and Methodology Design

Define the target architecture, identity model, semantic model, governance model, and delivery roadmap.

PrototypePilot Prototype

Validate the framework through a thin slice such as metadata discovery, entity resolution, semantic discovery, or graph construction.

GovernControls and Operating Model

Establish ownership, policies, standards, quality gates, representation rules, and AI governance checkpoints.

OperateContinuous Knowledge Operations

Run metadata harvesting, ontology evolution, graph curation, trust monitoring, AI monitoring, and semantic drift detection.

ImproveContinuous Improvement

Use operational feedback, decision evidence, quality signals, and usage analytics to refine the platform and methodology.

ScaleDecision Adoption and Scale-up

Embed trusted data into decisions, business outcomes, operating rhythms, communities, and repeatable delivery capability.

Original Methods in Delivery

The lifecycle is standard. The method layer is distinctive.

Delivery uses familiar consulting stages, but the work is shaped by original methods that connect reality, source data, semantic architecture, governance, prototype, and decision assets.

Reality Mapping

Clarifies what exists before designing representations, data products, semantic models, or AI workflows.

Source-to-Scene Pipeline

Turns heterogeneous source data into a governed operational scene for digital twin, simulation, and AI use.

The Data Preparation Layer

Explains the operating model for preparing governed data products once and serving many consumers.

Knowledge Fitness

Checks whether concepts, methods, evidence, relationships, and next actions are mature enough to reuse.

Capability Assessment

Trusted Data Readiness

A maturity assessment scores the platform from Level 1 to Level 5 across the core capabilities needed for AI-ready digital infrastructure.

Reality MappingLevel 1
RepresentationLevel 2
IdentityLevel 3
Raw DataLevel 4
Curated DataLevel 5
Data QualityLevel 1
Semantic LayerLevel 2
Knowledge GraphLevel 3
GovernanceLevel 4

Reference Architecture

Digital Twin Data Platform

RealityDigital CaptureRepresentationIdentity RegistryOperational SystemsRaw DataCurated DataSemantic LayerKnowledge GraphAIDecisionBusiness Outcome
AzureAWSSnowflakeNeo4jPostGISKafkaSparkAirflowOpenMetadataCollibradbtPower BI

Implementation Playbook

From mapping to operation

  1. Reality Mapping
  2. Representation Mapping
  3. Identity Resolution
  4. Metadata Harvesting
  5. Quality Rules
  6. Semantic Layer
  7. Knowledge Graph
  8. AI Prototype
  9. Decision Adoption
  10. Continuous Operation

Framework Backbone x Delivery Lifecycle

The operating matrix behind the Trusted Data Framework.

The matrix connects framework layers to delivery stages. Each cell should eventually return questions, checklists, artifacts, patterns, cases, prototype evidence, and decision relevance.

LayerAssessDiscoverDesignPrototypeGovernOperateImproveScale
Reality
Reality Mapping
Reality Mapping
Reality Mapping
To map
To map
To map
To map
To map
Representation
Capture and Handling
Capture and Handling
To map
To map
To map
To map
To map
To map
Identity
To map
Identity Resolution Pattern
Identity Resolution Pattern
Identity Resolution Pattern
Identity Resolution Pattern
To map
To map
To map
Raw Data
Source IngredientsMarket and Store
Source IngredientsMarket and Store
To map
To map
Market and Store
To map
To map
To map
Curated Data
AI-Ready Data Foundation AssessmentData Readiness Levels Evidence
AI-Ready Data Foundation AssessmentData Readiness Levels Evidence
Professional KitchenAI-Ready Data Foundation Assessment
Professional Kitchen
Professional Kitchen
Professional Kitchen
To map
To map
Semantic Layer
Digital Twin 2.0 Semantic Governance AssessmentSemantic Layer Assessment
Recipe and MenuDigital Twin 2.0 Semantic Governance Assessment
Recipe and MenuDigital Twin 2.0 Semantic Governance Assessment
Semantic Discovery Pattern
Recipe and Menu
To map
Semantic Discovery Pattern
To map
Knowledge
Knowledge Graph Readiness Assessment
Knowledge Graph Readiness AssessmentSource-to-Scene Pipeline
Knowledge Graph Readiness AssessmentSource-to-Scene Pipeline
Source-to-Scene Pipeline
Source-to-Scene Pipeline
To map
To map
To map
Intelligence
To map
To map
To map
To map
To map
To map
To map
To map
Decision
From Data Readiness to Decision Readiness
To map
From Data Readiness to Decision Readiness
Meal and Service
To map
To map
Meal and ServiceDecision Adoption Pack
Meal and ServiceDecision Adoption Pack
Business Outcome
To map
To map
To map
To map
To map
To map
To map
To map
Governance
Spatial Intelligence & Digital Twin Governance ExtensionGDS Responsible AI Advisory Panel Evidence
Spatial Intelligence & Digital Twin Governance Extension
Spatial Intelligence & Digital Twin Governance Extension
To map
Spatial Intelligence & Digital Twin Governance ExtensionGDS Responsible AI Advisory Panel Evidence
Spatial Intelligence & Digital Twin Governance Extension
GDS Responsible AI Advisory Panel Evidence
To map
Lineage / Provenance
To map
To map
To map
To map
To map
To map
To map
To map
Observability
To map
To map
To map
To map
To map
To map
To map
To map
Quality
To map
To map
To map
To map
To map
To map
To map
To map
Security
To map
To map
To map
To map
To map
To map
To map
To map
Compliance
To map
To map
To map
To map
To map
To map
To map
To map
Trust
To map
To map
To map
To map
To map
To map
To map
To map

Deliverables

How the thinking becomes practical work.

These are the working assets that connect discovery, architecture, governance, prototype, and delivery.

Assessment ReportGap AnalysisRoadmapArchitecture BlueprintSemantic ModelOntologyKnowledge GraphData Product CatalogueMetadata CatalogueIdentity ModelIntegration MapGovernance ModelImplementation BacklogPilot PrototypeOperating ModelTraining PlanDecision Adoption Plan

Decision Assets

From data platform outputs to business decisions.

Architecture deliverables are necessary, but decision deliverables show how trusted data becomes executive action, operational judgement, investment choice, risk control, and policy.

Executive DashboardScenario ReportInvestment RecommendationRisk AssessmentSimulation ReportPolicy RecommendationAI AssistantOperational Cockpit

Stage Artifacts

Each stage produces reusable consulting assets.

Delivery should leave the client with artifacts they can govern, maintain, reuse, and improve after the initial project.

Assess
  • Readiness Scorecard
  • Capability Baseline
  • Maturity Radar
  • Risk Register
  • Assessment Brief
Discover
  • Entity Inventory
  • Representation Inventory
  • Stakeholder Map
  • Reality Map
  • Boundary Diagram
  • Observation Catalogue
  • Identity Matrix
Design
  • Semantic Model
  • Ontology
  • Reference Architecture
  • Target State
  • Capability Matrix
  • Governance Model
Prototype
  • Working Demo
  • Neo4j Graph
  • Metadata Catalogue
  • Graph API
  • Vector Index
  • Prompt Library
Govern
  • Governance Model
  • Ownership Matrix
  • Policy Map
  • Quality Gates
  • AI Governance Checkpoints
Operate
  • Curation Workflow
  • Ontology Change Log
  • Graph Quality Report
  • AI Monitoring Dashboard
  • Trust Metrics
Improve
  • Improvement Backlog
  • Drift Report
  • Usage Analytics
  • Decision Feedback Loop
  • Methodology Updates
Scale
  • Scale Roadmap
  • Training Plan
  • Community Model
  • Reusable Patterns
  • Decision Adoption Pack

Method Toolkits

Concepts become practical tools.

Each core concept should eventually connect to checklists, templates, patterns, assessment prompts, and implementation methods.

Reality Mapping
  • Observation Checklist
  • Stakeholder Mapping
  • Entity Inventory
  • Representation Inventory
  • Boundary Analysis
Identity
  • Persistent Identifier Design
  • Master Entity Mapping
  • Entity Resolution Matrix
  • Identifier Policy
Curated Data
  • Quality Rules
  • Metadata Template
  • Lineage Checklist
  • Data Product Canvas
  • Data Contract
Semantic Layer
  • Ontology Pattern
  • Naming Convention
  • Vocabulary
  • SKOS
  • OWL
  • RDF
Knowledge Graph
  • Graph Schema
  • Relationship Pattern
  • Evidence Model
  • Graph Prompt Template

Capability Enablement

Help the client maintain the capability.

Digital infrastructure is not finished at handover. The client needs the people, governance, operating model, and community to curate knowledge continuously.

TrainingPlaybookGovernanceCentre of ExcellenceOperating ModelCommunityKnowledge Transfer

Typical Client Questions

Consulting starts with better questions.

Reality Mapping
  • What actually exists?
  • What assets are missing?
  • What should be represented?
  • Which entities matter?
Representation
  • Which systems already represent it?
  • Where are duplicates?
  • Who owns the representation?
  • Which representation is trusted?
Identity
  • How do we know two records are the same object?
  • Which identifier is persistent?
  • Who owns identity policy?
Knowledge Graph
  • What relationships matter?
  • Which decisions require connected evidence?
  • What must AI be able to explain?

Kitchen Methodology

A memorable model for trusted data delivery.

The kitchen metaphor makes the operating model tangible: reality is harvested, raw data becomes prepared ingredients, semantics becomes the recipe, AI becomes the chef, and decisions become customer value.

RealityHarvest
ObservationIngredients
Raw DataDelivery Truck
Curated DataWashing and Storage
Data ProductPrepared Ingredients
Semantic LayerRecipe
Knowledge GraphCooking
AI AgentChef
DecisionMeal
Customer ValueOutcome

Registry-backed consulting patterns

Reusable methods are becoming consulting assets.

These patterns are rendered from a shared method library, so delivery pages can show reusable consulting assets without duplicating pattern data in the UI.

AI-Ready Data Foundation Assessment

A reusable consulting pattern for assessing whether an enterprise data foundation can support GenAI, RAG, GraphRAG, semantic governance, and AI-assisted decisions.

Curated DataAssessDiscoverDesignLevel 1
Source: docs/patterns/ai-ready-data-foundation-assessment.md
Digital Twin 2.0 Semantic Governance Assessment

A one-day PoC pattern for assessing whether a digital twin data foundation is ready for semantic layer, knowledge graph, GraphRAG, AI agents, and decision support.

Semantic LayerAssessDiscoverDesignLevel 1
Source: docs/patterns/digital-twin-semantic-governance-assessment.md
Knowledge Graph Readiness Assessment

A reusable consulting pattern for assessing whether an organisation is ready to build and operate a knowledge graph for enterprise AI, GraphRAG, digital twin, or semantic governance use cases.

KnowledgeAssessDiscoverDesignLevel 1
Source: docs/patterns/knowledge-graph-readiness-assessment.md
Semantic Layer Assessment

A reusable consulting pattern for assessing whether an organisation has a usable semantic layer connecting business meaning, metadata, data products, ontology, knowledge graph, RAG, and decision support.

Semantic LayerAssessDiscoverDesignLevel 1
Source: docs/patterns/semantic-layer-assessment.md
Spatial Intelligence & Digital Twin Governance Extension

A Freedo-style governance extension for spatial-temporal digital twin platforms, 3DT-style scene databases, scene file formats, spatial intelligence models, and data middle platform capabilities.

GovernanceAssessDiscoverDesignGovernOperateLevel 1
Source: docs/governance/spatial-intelligence-digital-twin-governance-extension.md
Source-to-Scene Pipeline

An emerging method for transforming heterogeneous source data into a coherent, governed, semantically connected operational scene for AI, simulation, and decision support.

KnowledgeDiscoverDesignPrototypeGovernLevel 1
Source: docs/internal/SOURCE_TO_SCENE_PIPELINE.md

Reference Framework Mapping

Standards alignment

ISO 19650ISO 8000ISO 11179ISO 55000TOGAFNIST AI RMFDAMADCATW3COGCbuildingSMART IFCGS1FAIRLinked DataDigital Twin Consortium

Capability Matrix

Who needs to be involved?

Reality MappingBusinessArchitectureDigital Twin
IdentityDataPlatformGovernance
Curated DataDataPlatformAI
Semantic LayerBusinessArchitectureAI
Knowledge GraphPlatformAIDigital Twin
GovernanceBusinessDataGovernance

Consulting Operating System

Every concept should connect to delivery.

The next evolution is to connect each concept to standards, methods, assessments, templates, architecture patterns, prototypes, case studies, deliverables, and tools.

Explore the concept network

Connected to the concept network

This page in the Trusted Data Framework.

Links the delivery lifecycle to concepts, assessment patterns, reusable artifacts, and governance control points.

Explore concepts
RealityRepresentationIdentityCurated DataSemantic LayerKnowledgeDecision

Delivery connection

AssessDiscoverDesignPrototypeGovernOperateImproveScale
Cross-cutting
GovernanceTrustObservability