Refined Digital Insight (RDI-Data)

Governed AI, delivered in stages

We help regulated organizations move from AI experiments to AI they can trust, audit, and measure. Start with a clear readiness plan, then build the governance and data foundations that keep AI running well.

Each stage stands alone, and each one prepares you for the next.

Every engagement starts with a Data Product Maturity Model (DPMM) baseline, agreed in writing. We re-score it as you progress, so you can see what changed and what it delivered.

Baseline, re-score, prove
Agreed in writing at the start, re-scored at each milestone, and tied to the outcomes you track.

1. Assess

Find out where your AI and data stand today. We inventory the systems in use, score your data products, and identify the highest-value use cases, so you know exactly what to do next and why.

2. Govern

Set clear ownership, policies, and controls for data and AI. We define roles, decision rights, and risk tiers, then implement the controls in your governance platform so stewardship becomes part of everyday work across teams.

3. Ground

Connect AI to governed, traceable data. We build the pipelines and data products AI depends on, with business definitions, lineage, and access permissions carried, so every AI answer can be traced back to its source.

4. Assure

Keep governance current with ongoing monitoring and reviews. Our managed service runs your governance platform and AI inventory, with quarterly re-scoring, audit support, and annual plan revisions, so governance keeps pace with every AI deployment.

RDI is a Collibra partner and delivers implementation and managed services on that platform. We also work across other governance and cloud tools to fit your environment.

Platform-neutral delivery
The stages stay the same whichever governance and cloud platform you use.

Governed context for AI, so answers trace back to your business definitions, lineage, and access rules.

Answers you can trace
Governed definitions and permissions carried into AI responses.

AI Data Governance Keyword Strategy by Intent

Keyword terms grouped by search intent, with answers to the problem-led and regulatory questions drawn from RDI’s published content. Search volumes are not included; verify them in Search Console or a keyword tool before committing budget.

Keyword list by intent

2. Problem-led questions (informational, GEO and AEO)

How do I prepare my data for AI?
Why do AI projects fail, and how does data quality cause it?
What is AI data governance?
What is a data product maturity model?
How do I build an AI model inventory?
How do I set up a data governance program for AI?
How do I govern shadow AI in my organization?

3. Regulatory and compliance

EU AI Act data governance requirements
EU AI Act compliance checklist
GDPR and AI training data
PIPEDA AI compliance
Bill C-27 AI
AI risk classification
AI audit trail
ISO/IEC 42001 implementation
NIST AI RMF implementation
Responsible AI governance framework

4. Technical and semantic

Semantic layer for AI
Semantic models for generative AI
Business glossary for AI
Data lineage for AI
Metadata for LLMs
Governed data for LLMs
RAG data governance
RAG data quality
Retrieval-augmented generation governance
Agentic AI governance
AI agent data access controls
Knowledge graph for enterprise AI
Hybrid search governance
Model Context Protocol (MCP) governance

5. Comparison and alternatives (high intent)

Collibra alternatives
Collibra vs Alation
Collibra vs Informatica
Best AI governance platforms 2026
Best data governance software 2026
Microsoft Purview AI governance
Informatica AI governance
Data governance tool comparison
AI governance platform comparison

6. Business outcomes

Accelerate AI deployment
Reduce AI model approval time
Data monetization strategy
Data monetization framework
Trusted data for AI
Audit-ready AI
Reduce data risk
Data product ROI
Measuring data product value

7. Services and delivery

Collibra implementation partner
Collibra managed services
Collibra consultant
Data governance health check
Data governance framework development
Data warehouse modernization for AI
BI and analytics modernization
Oracle to Google Cloud migration
BigQuery migration services
Semantic layer implementation services

8. Canadian and public sector

Data governance consultant Toronto
AI governance consultant Canada
PIPEDA compliance consultant Ontario
AI governance for Canadian government
Data governance services for public sector
Government AI readiness

9. Brand and entity (essential for GEO)

Refined Digital Insight
RDI Data
RDI Data ClarityCast
RDI Data Command
RDI Data Intelligence LaunchPad
Data Product Maturity Model (DPMM)
RDI AI Reference Architecture

Problem-led questions

How do I prepare my data for AI?

Start with an assessment of your data and technology landscape against a shared reference. RDI’s AI Readiness Plan & Enablement Service follows a business-led path: goals are agreed and recorded in writing, the AI ecosystem is evaluated against the RDI AI Reference Architecture, and a value assessment report is delivered. The engagement ends with an AI readiness framework and roadmap, typically delivered in about nine weeks.

Why do AI projects fail, and how does data quality cause it?

AI initiatives often stall because the data beneath them is fragmented, inconsistent, and poorly governed. RDI’s framework names four recurring gaps: fragmentation and silos, where critical data is trapped in departmental systems; quality inconsistencies, where varying accuracy undermines model reliability; governance gaps, where unclear ownership creates compliance risk; and context deficiency, where raw data lacks the business meaning AI needs to interpret it.

What is AI data governance?

AI data governance applies governance to the data that AI systems use, not only to reports and warehouses. It covers ownership, quality, lineage, and business definitions, so that models and AI agents draw only on trusted, traceable data. RDI’s reference architecture is built on principles such as govern once and enforce everywhere, a single source of truth for every entity, lineage throughout, and continuous compliance.

What is a data product maturity model?

A data product maturity model is a framework that shows how an organization’s data practices grow from reactive to strategic. RDI’s Data Product Maturity Model (DPMM) assesses four domains, which are data foundations and governance, data engineering and infrastructure, analytics and intelligent insights, and security, privacy and ethics. It places organizations on four maturity levels, from reactive data management to an integrated data strategy in which data drives innovation and new revenue.

How do I build an AI model inventory?

Begin by cataloging the data assets, sources, and lineage that each AI use case depends on, so every model can be traced back to its inputs. RDI’s AI reference architecture includes a model registry alongside a semantic metadata registry, a feedback database, and a prompt library, which gives you one place to record what each model uses. The AI ecosystem assessment identifies which models and data sources are in use today and where they are not yet governed.

How do I set up a data governance program for AI?

RDI’s Data Intelligence Startup & Health Check establishes governance in a structured way. The engagement assesses your current data management, delivers a value assessment report, and produces a governance framework your team reviews with RDI. Quarterly health checks then monitor plan adherence, and an end-of-year revision keeps the program current as your needs grow.

How do I govern shadow AI in my organization?

Start with visibility and an interim rule set. Issue an acceptable-use policy that covers approved tools, permitted data types, and required review steps, and communicate it through managers. Then build an inventory of AI use cases and apply dual control, where human oversight and automated policy enforcement work together. This keeps staff productive while protecting sensitive data until a full framework is in place.

Regulatory and compliance

Regulatory answers below draw on RDI’s published content where it exists. Where it does not, the answer says so, and the page should not claim a service RDI has not defined.

EU AI Act data governance requirements

The EU AI Act expects organizations to document and govern the data sources used in AI systems, including data processed by vendors. RDI’s automated governance approach aligns with GDPR and the EU AI Act, using tagging, classification, and policy enforcement to make data sources and their use visible. Confirm the specific obligations for your systems with legal counsel, since RDI’s service supports compliance but does not provide legal advice.

EU AI Act compliance checklist

A practical checklist covers four areas: an inventory of AI systems and their data sources, a risk classification for each use case, documented lineage and ownership for the data behind them, and evidence of human oversight. RDI’s AI readiness engagement produces the inventory, assessment, and roadmap that support this work. A formal compliance determination requires legal review of your specific systems.

GDPR and AI training data

Personal data used to train or ground AI systems must be governed under GDPR like any other personal data. Lineage and cataloging show where that data came from and where it flows, which is what an organization needs to respond to access and deletion requests. RDI’s governance approach supports this traceability, and legal counsel should confirm the lawful basis and retention rules for each use case.

PIPEDA AI compliance

PIPEDA governs personal information used in commercial activity in Canada, including when that information feeds AI systems. Organizations need to know what personal information each system uses, why it is used, and how it is protected. RDI’s assessment and catalog approach helps map that information, and your privacy officer should confirm obligations for specific systems.

Bill C-27 AI

Bill C-27 proposed Canada’s Artificial Intelligence and Data Act, and it has not become law as of this page’s writing. Organizations should monitor its status and design governance that can adapt to new AI rules. RDI’s governance framework is built to evolve with regulation, and the maturity roadmap is designed to accommodate new obligations as they are confirmed.

AI risk classification

AI risk classification sorts each AI use case by the potential harm it could cause, which then sets the level of controls it needs. RDI’s AI readiness framework includes an assessment of each use case against business goals and governance requirements, and the outcome is a prioritized roadmap. Specific risk tiers should follow the regulation that applies to your organization.

AI audit trail

An AI audit trail records which data, model, and policy produced each output, so decisions can be reviewed later. RDI’s governance principles emphasize lineage throughout and continuous compliance, which together create the traceability an audit needs. Implementation details depend on the platforms in your environment, and RDI can scope this as part of the governance program.

ISO/IEC 42001 implementation

ISO/IEC 42001 is an international standard for AI management systems. RDI’s current service materials do not describe ISO/IEC 42001 certification or implementation support, so this page should not present RDI as offering it. Organizations interested in this standard should confirm the scope of any RDI engagement directly with the RDI team.

NIST AI RMF implementation

The NIST AI Risk Management Framework is a voluntary US framework for managing AI risk. RDI’s current service materials do not describe NIST AI RMF implementation as a defined offering, so this page should not claim it. RDI’s AI readiness and governance work can be mapped to the framework’s functions, and the RDI team can confirm how the two relate for a specific client.

Responsible AI governance framework

A responsible AI governance framework sets the principles, roles, and review steps that keep AI use fair, transparent, and accountable. RDI’s AI readiness framework defines business-first AI design, integrated technology readiness, and innovation foresight as its core considerations, supported by the governance principles in the reference architecture. Responsibilities and approval paths are agreed with your stakeholders during the engagement.