TDTrusted Datafor Trusted Decisions

Start Here

Start with the decision, data problem or change that needs clarity.

These three entry points organise an initial conversation around the problem you need to solve. They are not fixed packages: scope, outputs and the next useful step depend on the decision, evidence and delivery context.

Three ways to start

Choose the question closest to the current risk.

Decision & Data Trust Diagnostic

For an important decision, report, migration or AI use case that depends on data the organisation cannot confidently explain or reconcile.

Best when
The decision is important, but the source of the trust problem is unclear.
Steven's role
Clarify the problem, make the risk visible and help the team identify priorities.
Typical focus
  • decision and risk context
  • critical data, definitions and identity conflicts
  • quality, lineage and ownership gaps
  • prioritised intervention options
Data Governance, Quality & Transformation Delivery

For teams that need governance and quality controls to work inside real transformation delivery rather than remain separate policy documents.

Best when
The problem is understood and the organisation needs controls and change to work in practice.
Steven's role
Design and help drive implementation across governance, data and delivery.
Typical focus
  • ownership and stewardship
  • data-quality rules and exception management
  • metadata, lineage and reconciliation
  • governed transformation backlog
AI-Ready Data & Governance Review

For a defined AI, RAG or Agent use case that needs a clearer view of whether its enterprise information can be trusted and used responsibly.

Best when
A defined AI, RAG or Agent use case needs a trustworthy data and decision-control foundation.
Steven's role
Review readiness, expose control gaps and define the next intervention.
Typical focus
  • source authority and provenance
  • permissions and semantic consistency
  • conflicting evidence and data quality
  • accountability, human review and escalation
Specialist extension

For asset-, place- and infrastructure-intensive use cases, the same Trusted Data approach can extend into spatial-temporal, digital-twin and semantic architecture. These are selective extensions of the core practice, not separate service lines that every engagement requires.

Discuss the most useful starting point

Evaluating Steven for a role or contract? Start with About. If repeated source-data preparation is the immediate problem, see the Data Kitchen explanation.

Explore supporting methods, accelerators and workbenches

Structured starting point

Which accelerator should we start with?

Three bounded prompts use a visible static rules table. The initial result is an example; change any answer to update it. No chatbot or hidden model is making the recommendation.

2. What data is available now? Select all that apply.
Recommended accelerator1 week

Trusted Data Discovery Accelerator

Answers: what does this data actually represent? Uses Reality Mapping to identify entities, duplicate representations, identity candidates, relationships, coverage, assumptions, and confidence.

Named deliverable
Reality Mapping assessment and evidence-backed discovery brief
Scope note
Demonstrated in an independent personal project; not presented as client-delivery validation.
Why this result
It directly matches the selected client problem. It directly supports the selected next decision. It fits the available data signals.
Next action

Select one bounded dataset landscape and run the Reality, Identity, Coverage and Linkage assessment.

Inspect this accelerator →Email a short brief →

How to engage

Start small, create evidence, then decide whether to go further.

The recommended entry point is a focused assessment or case analysis, followed by a thin-slice workbench and a 2-4 week MVP roadmap.

Discovery callOne-week assessmentWorkbench evidenceGovernance controlsMVP roadmapNext step

Trusted Data Accelerators

Practical methods, templates, and demonstrators for focused advisory work.

These materials help turn a discussion into a focused assessment, prototype, or roadmap. The first reference use case is geographic and digital twin oriented because it clearly shows spatial intelligence, source-to-scene delivery, and AI-ready governance in action.

Repeatable means the client gets a more predictable engagement: clear inputs, a structured method, transparent findings, and a practical recommendation instead of a one-off demo that is hard to reuse.

Faster startLower delivery riskClear deliverablesReusable evidence
Trusted Data Accelerator

Trusted Data Discovery Accelerator

Answers: what does this data actually represent? Uses Reality Mapping to identify entities, duplicate representations, identity candidates, relationships, coverage, assumptions, and confidence.

1 weekReality mapping
Trusted Data Accelerator

Semantic Readiness Accelerator

Answers: is this data ready for AI? Assesses whether metadata, identity, representation, semantic, knowledge, and governance foundations are ready for AI-enabled use.

1 weekSemantic readiness
Trusted Data Accelerator

Source-to-Scene Accelerator

Answers: how do disconnected sources become an operational scene? Maps GIS, BIM, IoT, asset, event, document, and scene metadata into a trusted scene model.

1-2 weeksDigital twin readiness
Trusted Data Accelerator

Knowledge Graph Readiness Accelerator

Answers: should we build a graph? Reviews where graph creates value and what entity, relationship, provenance, and operating controls are required.

1 weekGraph readiness
Trusted Data Accelerator

Trusted Data Roadmap Accelerator

Answers: what should we do next? Turns assessment findings into quick wins, MVP priorities, architecture moves, governance controls, and a practical delivery roadmap.

1 weekRoadmap

Featured Workbench

Trusted Data Demonstration Workbench

The workbench demonstrates trusted data in action. The first reference scenario focuses on digital twin and spatial data, then follows the same pattern for other domains: scenario, data, method, workbench, deliverable, and decision asset.

Open workbench
Data InventoryEntity & Relationship DiscoverySemantic Layer CandidateKnowledge Graph PreviewGovernance Control PlaneReadiness Report

Assessment Patterns

Reusable ways to diagnose readiness.

These patterns help consultants move quickly from client context to structured findings, control points, and practical next steps.

Assessment pattern

AI-Ready Data Foundation Assessment

Assesses whether data inventory, metadata, quality, governance, lineage, and access controls can support enterprise AI, RAG, and decision support.

AssessData foundation
Assessment pattern

Semantic Layer Assessment

Reviews whether shared meaning, controlled vocabulary, mappings, ownership, and change control are strong enough for analytics, APIs, and AI.

Assess / DesignSemantic architecture
Assessment pattern

Knowledge Graph Readiness Assessment

Checks entity readiness, identity resolution, relationship quality, ontology maturity, provenance, GraphRAG readiness, and graph operating model.

Assess / PrototypeKnowledge graph
Assessment pattern

Digital Twin 2.0 Semantic Governance Assessment

Connects spatial, asset, sensor, event, document, semantic, graph, and governance concerns into a practical digital twin readiness review.

Assess / DiscoverDigital twin readiness

Governance Delivery Packs

Governance as delivery capability.

This approach treats governance as operational controls that make data, semantic assets, spatial layers, graph knowledge, and AI consumption reusable and trustworthy.

Governance pack

Enterprise Data Governance Delivery Pack

A delivery-oriented governance pack covering ownership, stewardship, quality, metadata, lineage, policy, controls, and operating rhythm.

Govern / OperateGovernance delivery
Governance pack

Spatial Intelligence & Digital Twin Governance Extension

Extends governance to multi-source spatial-temporal data, scene databases, scene file formats, spatial intelligence, and AI consumption controls.

Design / GovernSpatial intelligence
Governance pack

Spatial Layer Intelligence Governance

Turns spatial layers from passive map backgrounds into governed, matchable, feature-ready spatial knowledge assets.

Discover / DesignLayer intelligence
Governance pack

Data Middle Platform Governance

Frames the middle platform as reusable capability: identity, metadata, quality rules, lineage, data products, semantic services, APIs, and AI controls.

Design / OperateMiddle platform

PoC and MVP Accelerators

From conversation to a testable delivery path.

These accelerators keep scope tight: discover the decision use case, assess the data foundation, demonstrate a thin slice, and turn findings into a roadmap.

Accelerator

One-day Digital Twin Semantic Governance PoC

A focused discovery, data review, semantic workshop, governance control analysis, and MVP roadmap session.

DiscoveryPoC accelerator
Accelerator

One-week Spatial Data Governance Case Analysis

A short case analysis using anonymised architecture, metadata, spatial data, governance, and delivery material.

AssessmentCase analysis
Accelerator

Two-week Digital Twin Semantic Readiness MVP

A compact end-to-end workbench showing profiling, relationship discovery, semantic candidates, graph preview, governance readiness controls, and report output.

PrototypeMVP roadmap

One-week case analysis

One-week Spatial Data Governance & AI-readiness Case Analysis

A low-risk, time-boxed consulting analysis using anonymised architecture, metadata, spatial data, or governance material. The goal is to help the client judge practical fit through a real case rather than another interview.

This does not replace existing platform capability. It makes delivery controls, source-to-scene lineage, semantic reuse, governance readiness, and AI-ready consumption more explicit.

Delivery storyline: Source-to-Scene PipelineThe case analysis follows data from heterogeneous sources toward a trusted operational scene that can support AI, simulation, and decision support.
Input
  • Anonymised architecture diagram
  • Metadata / data catalogue / data model sample
  • Spatial layer or digital twin data description
  • Governance process or role description
  • One priority delivery question
Outputs
  1. Current-state governance and AI-readiness assessment
  2. Spatial / semantic data governance recommendations
  3. 2-4 week MVP roadmap with priorities
Non-goals
  • No production system access
  • No sensitive data required
  • No full platform implementation
  • No claim to replace existing platform capabilities
Multiple sourcesReality mappingIdentity and curationSemantic layerKnowledge graphTrusted operational sceneAI / simulation / decision support

How to Use These Assets

Discovery -> Assessment -> Prototype -> Roadmap -> Delivery

The asset library is intentionally lightweight. Its job is to show the consulting system behind the Trusted Data Framework: a reusable delivery architecture that connects client questions, assessment patterns, governance templates, prototype evidence, and delivery recommendations.

Explore next

Where this page can take you.

Shows how reusable assessment patterns, governance packs, and delivery accelerators attach to the concept network.

Browse the common language
Raw DataCurated DataSemantic LayerKnowledgeDecision

Delivery connection

AssessDiscoverDesignPrototypeGovern
Cross-cutting
GovernanceQualityLineage