Data & AI Governance
Quality, MDM, compliance
Data & AI Governance: reliable data for confident decisions
Reliable data and AI are the foundation of confident decision-making. We design effective governance frameworks, calibrated to your risk profile and integrated into your existing corporate governance.
What we do
Data operating model: data office, roles (data owners, data stewards), governance bodies and decision-making processes.
Data quality framework: rules, measurement, remediation and associated responsibilities.
Master Data Management and PIM: scoping, tool selection and deployment of critical master data.
Cataloging and documentation: shared vocabulary, lineage, access management.
AI governance: usage policy, model evaluation, explainability, risk management, trustworthy AI.
Compliance and sovereignty: GDPR, AI Act, industry-specific requirements (e.g. HDS for healthcare in France), data localization policies.
Our approach
Governance that works is governance that is proportionate.
We start from your risk profile and your existing governance rather than a theoretical model: the framework is calibrated to be sustained over time, with responsibilities owned by the business.
Our “data management by design” approach embeds governance rules into every new project rather than running them as a separate program.
What sets us apart
Governance that works, not a binder of policies:
every rule has an owner, a process and a KPI.
Joint coverage of data and AI:
a single, consistent framework, from customer master data to generative models.
Experience:
with market reference frameworks (DAMA) and leading tools (DataGalaxy, Informatica, Stibo Systems), with no vendor lock-in.
Data governance at the scale of a major automotive group
Client case study
A major European automotive group
Challenges
A strategic data estate scattered across multiple plants, brands and partners, which needed to be made reliable and leveraged across the enterprise.
Frame data exchanges between entities in terms of ownership, usage, security and GDPR compliance.
Solution
Leading governance and information architecture at group level, working with the Data Privacy Office, Legal, the CISO and IT.
Rollout of operational frameworks: policies and standards, data stewardship roles, data quality (metrics, root cause analysis), metadata, lineage and cataloging.
Reference information model and mapping of key entities to guide projects, along with team upskilling.
Benefits
Decisions backed by reliable data and GDPR compliance under control across all data flows.
Ownership, access and retention clearly governed in exchanges with plants, brands and partners.
An embedded data culture: empowered teams and governance applied day to day.
FAQ
What is data governance?
It is the set of roles, processes and rules that ensure an organization’s data is reliable, documented, accessible to the right people and compliant with regulations. It covers data quality, master data (MDM), cataloging, security and the data lifecycle.
Does the AI Act require AI governance?
Yes. The EU AI Act sets out requirements graded according to each system’s risk level: an inventory of AI systems, risk management, transparency, human oversight and documentation. Operational AI governance makes it possible to meet these requirements without slowing down adoption.
Where do you start when maturity is low?
With a rapid assessment: within a few weeks, it establishes your maturity level (against a reference framework such as DAMA), identifies priority workstreams and defines an initial, proportionate set of roles. Governance then expands step by step, driven by concrete use cases.