Methodology
The D.A.T.A. Confidence Framework
A practical methodology for helping leadership teams discover, assess, transform, and assure their data and AI readiness.
Framework
Discover, Assess, Transform, Assure
Every engagement follows these phases so boards and technical teams share the same picture of intent, evidence, and outcomes.
Discover
Purpose
Identify critical data, systems, vendors, AI usage, stakeholders, and business goals.
Key activities
- Stakeholder interviews
- Business objective review
- System and data landscape mapping
- Sensitive data identification
- AI use-case identification
- Vendor and SaaS discovery
Assess
Purpose
Evaluate sensitive data exposure, governance maturity, access controls, architecture, vendor risk, and AI readiness.
Key activities
- Risk scoring
- Access and permissions review
- Governance maturity review
- Data quality and trust review
- Architecture review
- Vendor exposure assessment
- AI readiness assessment
Transform
Purpose
Build the roadmap, control uplift, governance model, data ownership model, architecture improvements, and operating cadence.
Key activities
- 90-day action plan
- 12-month roadmap
- Governance operating model
- Data ownership model
- Policy and control recommendations
- Technology and architecture uplift plan
Assure
Purpose
Track progress, provide executive reporting, support board visibility, and maintain governance over time.
Key activities
- Monthly advisory cadence
- Board or ELT reporting
- Risk register updates
- Roadmap tracking
- Governance meeting support
- Vendor and AI risk review
Assessment
What we assess
Every engagement is structured around the domains that determine whether data can be trusted, protected, governed, and safely used for AI.
Data visibility
Do you know what data you have and where it lives?
Data sensitivity
Can you identify which data would cause the greatest harm if exposed or misused?
Access control
Are access rules clear, enforced, and proportionate to sensitivity?
Data governance
Do ownership, policies, and approvals hold up when teams move quickly?
Data quality
Is data accurate and consistent enough for reporting and automation?
AI readiness
Are AI use cases, models, prompts, and training data governed before scale?
Vendor exposure
Do you know which vendors touch sensitive data and where subprocessors sit?
Architecture
Does your architecture contain sensitive data flows and integration risk?
Resilience
Can you respond and recover when systems, vendors, or AI workflows fail?
Executive reporting
Can leadership see risk, progress, and trade-offs without drowning in detail?
Outputs
Practical outputs, not generic maturity reports.
Deliverables are designed for executive decisions — prioritised, traceable, and usable in board or customer conversations.
- Executive summary
- Scorecard
- Risk register
- Data, system, and vendor map
- Prioritised recommendations
- 90-day action plan
- 12-month roadmap
- Board or ELT decision pack
Next step
Discuss how this methodology applies to your organisation
Book a Data & AI Risk Discovery Call to align on context, priorities, and the right engagement entry point.