Nobody owns the data that decisions depend on
Critical datasets and measures have no named owner, so quality issues have no route to resolution and disagreements about definitions escalate rather than resolve.
Security & Governance
Governance that builds trust in your data and your AI.
Having policies isn’t the same as having good governance. Organisations need to know who is responsible for their data, whether it can be trusted, who has access to it and how AI is being used. As adoption of AI increases, getting those foundations right becomes even more important.
We work with organisations that need to put practical controls around their data and AI, particularly where the data is sensitive, regulated or important to the decisions they make.
You finish with named ownership, measured data quality, an access model you can evidence, a practical AI governance position, and an operating rhythm your team can run.
Common challenges
What we find when we look at governance honestly.
These patterns are common in organisations that have invested in data and are now being asked to demonstrate control. The technology is rarely the constraint. Ownership, accountability and operating discipline usually are.
AI is being adopted faster than it is being governed
Teams are already using AI tools, sometimes with company data. There is no agreed position on acceptable use, model oversight, human review or the records the organisation would need if challenged.
Governance was written but never operated
Policies, a data dictionary and a committee structure exist on paper. They are not connected to how data is produced or used daily, so behaviour has not changed.
Access has accumulated over time
Permissions were granted for specific needs and never reviewed. Sensitive data is more widely available than leadership assumes, and demonstrating who can see what takes weeks.
Regulatory questions require evidence, not intent
Auditors, regulators and enterprise customers now ask how data and AI are controlled and expect documented proof. Assembling that evidence from scratch is slow and reveals gaps at the worst moment.
Quality issues are found by the business first
Errors surface in a report or a customer interaction rather than in a control. Without measurement, ownership and remediation, trust in reporting declines while effort spent checking it grows.
Our approach
Governance designed to be operated, not filed.
We use our Discover, Design, Develop, Deploy engagement model, and our DIAlog framework to understand the behaviours and incentives that decide whether governance holds. We design controls into the platform and the delivery process so the right thing to do is also the easiest.
Data and AI governance, defined
Data and AI governance is the combination of ownership, decision rights, standards and controls that make data trustworthy and its use appropriate. It covers named owners, agreed definitions, measured quality, classification and access, retention, and oversight of models and AI enabled tools. It is judged by the behaviour it produces and the evidence it can supply, not by the documentation it generates.
Stage 01
Discover
We establish what needs protecting and what needs to be trusted: the data domains that matter commercially, where sensitive and personal data sits, current access, existing policy and controls, regulatory obligations, and where AI is already being used inside the organisation.
Stage 02
Design
We design governance that fits your size and risk appetite: ownership and stewardship roles, decision forums, agreed definitions, data quality measures, classification and access model, retention, and an AI governance position covering acceptable use, review and oversight. Proportionate beats comprehensive if comprehensive will not be operated.
Stage 03
Develop
We implement it in the places work happens. That means controls in the platform rather than in a document, automated quality tests with owners and thresholds, access aligned to role, lineage that is generated rather than maintained by hand, and templates that make the right behaviour the easiest option. Alongside that we help internal teams understand their responsibilities, clarify what being a data owner actually involves day to day, and establish appropriate ownership across the domains that matter.
Stage 04
Deploy
We embed governance into day-to-day operations: who reviews what and when, how issues are raised and resolved, how new data and new AI use cases are assessed before they go live, and the reporting that shows leadership the controls are working. Where it helps, we establish or support a data governance council so decisions about definitions, access and AI use have a clear home. Your people run it, with our support while it settles.
Business outcomes
What changes as a result.
We describe outcomes in the terms our clients use about the work.
Clear ownership of data and AI
Named owners for the domains, measures and models that matter, with a route for issues to be raised and resolved.
Measured, improving data quality
Quality expressed as tested rules with thresholds and owners, so problems are found by controls rather than by customers.
Access that reflects role and risk
A classification and access model you can explain and evidence, reducing exposure of sensitive and personal data.
Evidence ready for scrutiny
Documentation, lineage and control records that answer auditor, regulator and enterprise customer questions without a scramble.
A defensible position on AI
Agreed acceptable use, human oversight and review points, so teams can adopt AI without creating unmanaged risk.
Governance your team can operate
A proportionate model with the roles, rhythm and tooling to keep running after the engagement ends.
Relevant client work
Governance and capability work we have delivered.
The case studies set out what the organisation needed, what we did and what it produced.
They provided costed structure and solutions to create an ongoing credible data management capability.
Data Understood push to learn the nuances of the company, its overall strategy, its people and thus deliver solutions that benefit the company to its core and lay the foundation for a strong future in data.
The sense of partnership. At no point did it feel like a purely transactional relationship.
Where to go next
Related services, sectors and reading.
Governance is most effective when it is designed alongside the platform and the reporting it protects. Sector obligations shape how formal the model needs to be.
Related services
Sector context
Buying questions
Security and governance: questions enterprise buyers ask.
Written for the people accountable for control and assurance, covering how proportionate data and AI governance is actually implemented.
Let's talk about your Data & AI priorities.
Whether you're developing a data strategy, modernising your data platform, preparing for AI or tackling a specific business challenge, start with a 30-minute conversation with Inez.