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01 / Meet the model

Understand what makes data and AI trustworthy.

Data Quality Intelligence connects the outcomes you want, the people accountable for them and the evidence that governance is working. This tour shows you how the pieces fit together.

12 connected domains. Select a segment to explore it.

Selected domainAll domains

The wheel shows areas of governance, not a score. Each domain supports the others.

Read the model in three layers

12 domains · what to govern
Connected areas such as ownership, data quality, AI readiness and security. A domain is one part of the whole wheel.
112 measures · what to look for
Specific practices within each domain. They turn broad questions into things you can examine and evidence.
0–5 maturity · how well it works
A scale from absent to optimised. The level depends on the practice and its evidence, not confidence alone.

One example, all the way through

Imagine introducing an AI customer-support assistant. We’ll use it to show why every domain matters, from useful answers to responsible operation.

Next, choose a standards filter to see which parts of DQI connect to your priorities. You can also keep the whole model in view.

The 12 DQI domains at a glance

A reference to the same domains explored in the tour. Open a domain to read its purpose and examples of supporting evidence.

D1 · Data Value & Alignment

Start with the decision or service you want to improve. Connect data work to that outcome so effort goes where it matters.

Evidence to look for: A use-case register, agreed business definitions and success measures.

D2 · Data Ownership & Stewardship

Assign owners with decision-making authority and stewards who can resolve day-to-day issues. Everyone should know where to take a problem.

Evidence to look for: Named owners, responsibility records and an issue-escalation process.

D3 · Core Data Quality

Check accuracy, completeness, consistency and the other quality characteristics against the needs of the use case. A quality threshold needs a reason and evidence.

Evidence to look for: Quality rules, test results and a record of how failed checks were resolved.

D4 · Pipeline Quality & Operations

Ingestion, transformation and delivery can introduce errors. Make those steps observable and controlled so failures are detected and handled.

Evidence to look for: Pipeline tests, monitoring records, change approvals and incident runbooks.

D5 · AI Readiness & Provenance

Know where training and input data came from, how it changed and whether it is suitable. Consider bias, drift and the link between a model and its data.

Evidence to look for: Data provenance, version records, bias checks and input-validation results.

D6 · Governance & Compliance

Turn governance expectations into rules, responsibilities and records. Make it possible to trace a decision back to the policy and evidence that informed it.

Evidence to look for: Approved policies, control records, lineage and an audit trail.

D7 · Security & Privacy

Control access, protect sensitive information and prepare for incidents. Include threats specific to AI as well as familiar security and privacy risks.

Evidence to look for: Access reviews, security tests, privacy assessments and incident-response records.

D8 · Tooling & Technology

Choose and connect tools that support the controls you need. The test is whether teams can use them to operate and evidence governance consistently.

Evidence to look for: Tool inventories, integration records, automation coverage and usability feedback.

D9 · Transparency & Access

Make ownership, quality, access conditions and limitations visible. People need enough context to judge whether data or an AI output is suitable.

Evidence to look for: Catalogue entries, quality reports, output guidance and change communications.

D10 · Lifecycle & Retention

Manage information from creation through retention, archival and disposal. Keep a reason for retaining it and a controlled way to retire it.

Evidence to look for: Retention schedules, deletion records, archival tests and retirement approvals.

D11 · Organisational Readiness

Governance depends on skills, leadership and everyday behaviour. Give people the understanding, support and authority their roles require.

Evidence to look for: Role-based training, leadership sponsorship, adoption feedback and a maturity roadmap.

D12 · Sustainable & Maintainable

Keep documentation, versions, knowledge and improvement work under control. Governance needs to survive staff changes and evolving systems.

Evidence to look for: Version history, current runbooks, knowledge-transfer records and an improvement backlog.

Questions about DQI

Understand the framework and choose your next step.

What is DQI?

DQI stands for Data Quality Intelligence. It is an evidence-led framework for trustworthy data and AI, organised into 12 connected governance domains and 112 measures. Training, consulting and software help teams understand and apply it.

How does DQI assess data and AI governance?

DQI considers practices and their supporting evidence against a 0–5 maturity scale. An assessment helps identify gaps and prioritise improvements. Exploring the framework or selecting a standards filter does not produce an organisational score.

Where should I start: Explore DQI, DQI Intro or Foundation?

Explore DQI is an open guided tour with no sign-in. DQI Intro is optional, self-paced orientation to help you decide whether to invest more time in DQI. DQIA Foundation is the first professional certification stage; you can enter it directly.

Is DQI Intro a professional certification?

No. DQI Intro has six short topics, takes about 60 minutes and saves progress through email verification. It has no examination or professional credential. Optional software development practice provides additional examples without being a prerequisite for Foundation.

Does DQI replace ISO, NIST or ETSI requirements?

No. The tour shows relevant practices and evidence across standards perspectives, including ISO 8000, ISO/IEC 42001, NIST AI RMF and ETSI AI security. These connections do not confer certification or establish full coverage. Teams must check the source requirements and their applicability.

How can my organisation get help applying DQI?

Consulting can help you scope an assessment, examine evidence and create an improvement roadmap. Training builds internal capability, while DQI software supports assessment and operational governance. Contact DQI to discuss your context and the support you need.