Centre · Data & AI · Frameworks

20 flagship frameworks for governing data and adopting AI.

Data Governance Framework

Establish enterprise ownership, policy and accountability for data as a managed asset.

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Data Quality Framework

Measure, improve and sustain the fitness of data for its intended use.

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Data Management Framework

Provide the end-to-end disciplines to manage data across its lifecycle.

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Master Data Framework

Create single, trusted versions of core business entities.

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Business Intelligence Framework

Turn governed data into trusted, self-service insight and reporting.

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Analytics Maturity Framework

Advance analytics capability from descriptive to predictive and prescriptive.

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AI Governance Framework

Govern the responsible, accountable development and use of AI.

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AI Risk Framework

Identify, assess and treat the distinctive risks of AI systems.

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Responsible AI Framework

Ensure AI is fair, transparent, accountable and aligned with values and law.

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AI Strategy Framework

Align AI investment with strategy and measurable value.

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AI Operating Model Framework

Define how AI capability is organised, delivered and run at scale.

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Machine Learning Lifecycle Framework

Deliver and operate ML models reliably from problem to production.

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Data Ethics Framework

Embed ethical principles into how data is collected and used.

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Data Privacy Framework

Protect personal data and meet privacy obligations (NDPA-aligned).

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Data Security Framework

Protect data confidentiality, integrity and availability.

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AI Transformation Framework

Scale AI from pilots to enterprise value.

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AI Readiness Framework

Assess and build the foundations needed before scaling AI.

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Data Culture Framework

Build an organisation that values and uses data and AI well.

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Decision Intelligence Framework

Connect data, analytics and AI to better decisions.

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Enterprise AI Framework

Provide the master architecture integrating all data and AI capability.

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