Attributes for Conceptual Data Objects

Mon, 01 Jan 0001 00:00:00 +0000

Tip: Not every CDO needs every attribute — focus on what’s essential for business clarity, governance, and compliance. Keep definitions short, practical, and easy to update.

Each attribute below carries a second purpose: it shapes how AI agents, MCP servers, and retrieval pipelines can use the CDO directly. Read the italic commentary alongside each row.

Core (Functional)

Attribute	Guideline	Example
Name	Short, business-friendly label for the CDO. Used as the canonical token in MCP tool names, RAG indexes, and agent prompts — eliminates synonym drift ("Client" vs "Customer" vs "Account") that derails AI retrieval.	`Customer`
Business Meaning & Definition	One-sentence summary in plain English. Becomes the `description` field exposed to LLMs via MCP servers and tool schemas — models match user intent to the right concept by meaning, not by guessing from column names.	"A person or organisation that has an active relationship with the enterprise and may transact with it."
Type	Category (External Standard, Canonical, Bespoke). Signals to AI agents how much trust to place in the concept — autonomous actions can be permitted on External/Canonical CDOs and gated for human review on Bespoke ones.	Canonical
Relationships	List inbound and outbound links to other CDOs. Lets AI agents traverse the enterprise data graph deterministically (Customer → Order → Invoice) instead of hallucinating joins or inventing foreign keys.	Customer → places → Order; Customer → owns → Account
Mandatory Attributes	Key business fields that must be present. Defines the required argument shape for MCP tool calls — the LLM knows what it must collect from the user before invoking an action.	CustomerID, LegalName, ContactEmail, Country
Validation Rules	Constraints (uniqueness, mandatory fields, valid ranges). Machine-readable guardrails that automatically reject malformed AI-generated payloads before they hit downstream systems — turns "hope" into enforcement.	CustomerID unique; ContactEmail per RFC 5322; Country in ISO 3166-1
Interoperability	Ability to map to external standards or internal canonical models. Enables AI agents to translate between vendor schemas and the enterprise concept on the fly, so the same prompt works whether the data lives in Workday, SAP, or a CSV.	Maps to ISO 20022 "Party"; SAP Business Partner; Salesforce Account
Usage Scenarios	Where and how the CDO is used (reporting, analytics, integration, compliance). Used by AI tool-routers to decide which agent or skill should handle a given query — improves tool selection accuracy in multi-agent workflows.	CRM segmentation, billing, KYC reporting, churn analytics
Effective Date & Version	When the current definition applies; track changes. AI agents and embeddings must pin to a version — without this, a model can quote a definition that was retired last quarter and confidently mislead the user.	v2.3, effective 2025-07-01
Authoritative source	Who owns or governs the CDO (internal or external). Gives AI agents a citation target and a human escalation path — every AI-generated answer about this concept can attribute back to a known steward.	Customer Data Office — Steward: J. Doe
Usage Notes	Consuming domains, reports, APIs, integrations. Tells AI orchestrators which downstream systems will be touched by a tool call — essential for impact assessment, blast-radius control, and "are you sure?" confirmations.	Consumed by Billing, Analytics Warehouse, Marketing Automation

Supporting (Non-Functional)

Attribute	Guideline	Example
Performance	Response time targets for queries and API calls. Determines whether the CDO can be called inline during a chat turn or must be cached/pre-fetched — directly shapes AI agent latency budgets.	Lookup ≤ 200 ms p95; search ≤ 500 ms p95
Scalability	Ability to handle growth in data volume. AI workloads are bursty (one prompt fans out into many tool calls) — capacity must flex without throttling agents mid-task.	10M records today; 30% YoY growth; burst 500 req/s
Availability	Uptime targets (e.g., 99.9% for critical CDOs). When AI sits in the critical path, a CDO outage cascades into a visible AI failure — availability is now a user-experience metric, not just an infrastructure one.	99.95% uptime, 24×7
Resiliency	Disaster recovery and failover capabilities (RPO/RTO). AI agents that fail silently are dangerous; failover behaviour must be explicit so the agent either retries cleanly or refuses, never fabricates.	RPO 15 min; RTO 1 hour; multi-region active-passive
Quality rules	Checks for accuracy, completeness, uniqueness, and reference integrity. Poor data quality is amplified by AI — bad inputs become confident wrong outputs at scale. Quality rules are the upstream defence against hallucination.	<0.1% duplicate rate; 100% mandatory fields populated; FK integrity to Country
Lifecycle	Creation, update, archival, and review cadence. RAG corpora and fine-tuning datasets must respect the CDO lifecycle — otherwise AI keeps surfacing deprecated definitions long after the business has moved on.	Created on first transaction; reviewed annually; archived 7 years after last activity
Security	Encryption, access controls (RBAC, MFA) for sensitive data. AI agents inherit the caller's identity; RBAC enforced at the CDO prevents oversharing through prompt injection, summarisation, or careless context-window leakage.	RBAC; PII fields AES-256 at rest; TLS 1.3 in transit; MFA for write
Compliance	Privacy, retention, and regulatory constraints. AI inference can re-expose redacted data through paraphrase — compliance must be enforced at the CDO so sensitive content never enters the model's context window in the first place.	GDPR Art. 17 (right to erasure); 7-year tax retention; APP (Australia)
Auditability	Full change history and version control. Every AI-driven decision needs a reproducible trail: which definition, which version, which rules were in force at the moment of inference.	Every field change logged with user, timestamp, before/after; immutable audit store
Maintainability	Ease of updating definitions and propagating changes. AI tooling subscribed to the CDO (vector stores, MCP schemas, prompt templates) needs predictable change events to refresh embeddings and tool definitions automatically.	Schema changes published via change-event topic; semver versioning
Portability	Technology-independent representation (e.g., JSON, XML, UML). Directly consumable by MCP servers, OpenAPI tool schemas, and vector embeddings — a portable CDO is an AI-ready CDO with no glue code.	JSON Schema + OpenAPI 3.1 definition; UML class diagram

AI-Native Attributes

Recommended additions for AI / agentic deployments. Treating CDOs as first-class AI resources requires a small set of additional attributes. These make a CDO directly usable by MCP servers, agent frameworks, and retrieval pipelines — without bolt-on glue code or one-off integration projects.

Rationale & Classification

Mon, 01 Jan 0001 00:00:00 +0000

CDOs fall into three classification tiers. Architecture stability depends on a strong base of standardised CDOs — the wider that base, the more resilient the enterprise.

Stable — wide base of standards

✓ grounded ✓ resilient ✓ scalable

Unstable — flipped pyramid

! top-heavy ! coupling risk ! drift

A healthy enterprise has a wide base of External Standard and Enterprise Canonical CDOs and a small, controlled set of Bespoke CDOs. If the pyramid flips — too many bespoke concepts at the top — architecture becomes unstable.

Conceptual Data Object on Orchestrated Technology Transition

Attributes for Conceptual Data Objects

Core (Functional)

Supporting (Non-Functional)

AI-Native Attributes

Rationale & Classification