Data — Practices & Playbooks

AR-D01 · Decision-to-Data Dependency Mapping

Expose the data, knowledge, rules and context on which a priority decision or AI-enabled workflow depends, establish what must be kept out of it, and connect each dependency to its business consequence.

Data

AR-D02 · Critical Asset and Ownership Mapping

Use when critical assets are orphaned, ownership is system-based, issue resolution stalls or multiple domains claim authority.

Data

AR-D03 · Semantic Context Mapping

Define the meanings, relationships, temporal conditions and context signals an AI-supported decision must understand.

Data

AR-D04 · Data Fitness-for-Decision

Set and test decision-specific quality, freshness and latency requirements linked to business impact.

Data

AR-D05 · Accessibility and Interoperability Assessment

Determine whether authorised human and machine consumers can obtain and interpret required information with acceptable effort.

Data

AR-D06 · Event and Operational Readiness

Validate that event-driven or real-time information can support safe operational action under normal and failure conditions.

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AR-D07 · Accountability and Decision Rights Design

Clarify who decides, who advises, who executes and who provides independent challenge for critical data-use decisions.

Data

AR-D08 · Privacy and Data-Purpose Boundary

Trace purpose, legal basis or consent, minimisation, retention and individual rights through the AI data lifecycle.

Data

AR-D09 · Contextual Permission Design

Design runtime permissions that account for identity, purpose, task, risk, customer relationship and transaction context.

Data

AR-D10 · AI Data Lifecycle Boundary

Separate and govern training, evaluation, inference and operational feedback data to prevent leakage, contamination and uncontrolled reuse.

Data

AR-D11 · Retrieval and Context Evaluation

Demonstrate that the right authorised and current context reaches the AI system, including cases where retrieval should return nothing.

Data

AR-D12 · Enterprise Memory Design

Define what an AI system may remember, why it may remember it, who can correct it and when it must expire.

Data

AR-D13 · Agent Data and Tool Contract

Expose enterprise data and actions to agents through bounded, interpretable and auditable business-capability interfaces.

Data

AR-D14 · End-to-End AI Decision Lineage

Create proportionate evidence linking user or system intent, context, policies, models, tools, actions and business outcomes.

Data

AR-D15 · Data Trust Monitoring and Remediation

Detect material changes in quality, freshness, access, retrieval, memory and population conditions, and in the behaviour of the services that consume them, and resolve them through an owned operational process.

Data

AR-D16 · Outcome-to-Data Improvement and Risk Integration

Convert AI outcomes, overrides and incidents into prioritised data improvements and visible enterprise risk treatment.

Data

AR-D17 · Content and Knowledge Quality Assessment

Establish whether the documents, procedures and recorded material an AI service draws on are authoritative, current and correctly scoped to where they are used.

Data

AR-R01 · Weekly Data Readiness Triage

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AR-R02 · Biweekly Context & Retrieval Evaluation

Review evaluation regressions, authority/freshness failures, permission tests and context backlog.

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AR-R03 · Release Data & Context Gate

Confirm lifecycle boundaries, data contracts, permissions, reproducibility, lineage and operational readiness.

Data

AR-R04 · Monthly Data Trust Conversation

Connect data and context signals to AI behaviour, incidents, overrides and business outcomes.

Data

AR-R05 · Quarterly Dependency & Portfolio Risk Conversation

Review shared data dependencies, concentration risk, ownership, capability investment and roadmap outcomes.

Data

Introduction and source information

AIDIVE AI Maturıty Framework™ - Architecture & Rhythm - DATA PRACTICES & PLAYBOOK

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Source headers, footers and notes

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