AI Data
Without data that is ready, no AI can work.
We bring your data to the level required by AI systems: readiness, architecture, governance and observability, with an operational plan in 90 days.
30 min - No commitment - NDA availableWhen it is needed
The signals that indicate it is time to work on data before scaling AI
The four pillars
Governing data for AI is not an isolated IT project. It means making four areas measurable and sustainable: how ready the data is, how it is architected, how it is governed and how it is observed over time.
Data Readiness
assessment of completeness, quality, accessibility and suitability of data against priority AI use cases.
Data Architecture
design of the data architecture for AI: sources, integration, storage, feature store, pipelines and experimentation environments.
Data Governance
policies, roles and controls on quality, privacy, retention and lawful use, aligned with the EU AI Act, GDPR and ISO/IEC 42001.
Data Observability
continuous monitoring of quality, drift, lineage and metadata to keep AI models reliable over time.
What you get
A package of operational outputs that make every step towards an AI-ready data infrastructure traceable and decidable.
How it works
A structured, evidence-based process in 4 steps
Kickoff and scope
Definition of the AI use cases and data sources in scope
Data discovery
Mapping of sources, quality, ownership and regulatory constraints
Gap analysis
Comparison between available data and the requirements of AI use cases
Roadmap and workshop
Action plan, priorities and governance shared with the sponsor
How it integrates
AI Governance
Defines the decision-making and control framework within which data is governed
Discover AI GovernanceAI Shift
Brings into production the AI use cases enabled by an adequate data infrastructure
Discover AI ShiftFrequently asked questions
How is this different from a traditional data governance project?
AI Data starts from the AI use cases and derives the data requirements from them. It is not data governance as an end in itself, but a means to make AI use cases executable and reliable over time.
Do you already need a data lake or a data warehouse?
No. The framework also works in contexts where data is scattered across management software, CRM, ERP and operational spreadsheets. One of the typical outputs is a make/buy assessment on data platforms.
Is this a technical or a strategic engagement?
Both. The initial phase is strategic and focused on scoping. The technical roadmap is then set up together with your IT team or implementation partners, with no lock-in.
How does it integrate with AI Rating and AI Governance?
AI Rating measures overall AI maturity, of which data is one dimension. AI Governance defines the decision-making framework. AI Data specifically explores data readiness for execution.
How long does it typically take?
The initial assessment takes 3-6 weeks, depending on the number of data sources and use cases in scope.
Do you also cover privacy and compliance topics?
Yes, from a governance and process standpoint. For binding legal opinions we work with qualified partners; we define the control framework and the operational requirements.