A TO BE blueprint that connects meaning, operation, technology and consumption. We do not pick technologies in isolation: we define how the data ecosystem must be structured so it can be governed, automated, published and evolved without generating operational debt.
The signal is not always technical. Sometimes it is organizational, sometimes economic, almost always the feeling that every new use case forces redesigning what already existed.
Hand-crafted pipelines, duplications between layers, low performance and rising costs that grow faster than the value delivered to the business.
Multiple technologies without common governance, inconsistent quality, metrics that do not match across areas and heterogeneous security applied case by case.
Each new domain or use case forces redesigning part of the platform. The organization can no longer grow on what it has without taking on structural debt.
It is about deciding how the ecosystem must be structured.
Data Architecture defines how data flows, transforms, is governed, published and protected within the organization. Its purpose is to ensure that the data platform and processes are coherent, traceable, sustainable and aligned with the business.
A well-defined architecture connects four dimensions: meaning, operation, technology and consumption. That is why it should not be understood only as a technical schema, but as a corporate capability that articulates data layers, metadata, DataOps, security, data model, publication and exploitation.
Metadata governs structure, meaning and automation. Architecture is not designed on top of code, it is designed on top of the data model.
Flows must be designed to be repeatable, versionable, traceable and industrializable. Zero hand-crafted pipelines.
Business, governance, platform and infrastructure must have clear boundaries. Each one is accountable for its own.
Business keys, traceability, historization, change rules. The model is the semantic contract, not a technical map.
Value is exposed to the business through governed layers and mechanisms, not by indiscriminate access to internal operations.
Architecture must scale without generating structural debt. What is designed today must be sustainable in five years.
Six work blocks delivered as a complete blueprint, not as an à la carte menu.
Design of the TO BE ecosystem: layers, zones, components, responsibilities and flows.
Execution patterns, DataOps, versioning, monitoring, change management and lifecycle control.
Conceptual, functional and physical modelling criteria. Rules for business keys, historization, partitioning and security.
Separation between functional, platform and infrastructure custody. Access controls, traceability and usage principles.
Ingestion and integration patterns: batch, incremental, CDC, streaming, files, databases and APIs.
Serving model, data products, KPIs, dashboards and relationship with exploitation tools.
A short, intensive and verifiable methodology. We do not produce a voluminous document: we produce an architectural decision base.
Analysis of the AS IS, technologies, pain points, constraints, costs, risks and target ambition.
Definition of architectural principles, modelling, security, layer and publication criteria.
Design of the TO BE model: components, layers, flows, custodies and operating mechanisms.
Definition of the DataOps model, execution patterns, versioning, observability and change management.
Implementation sequencing, initial domains, quick wins, dependencies and risks.
Final document, executive summary, validation with stakeholders and implementation recommendation.
not just to be presented.
Principles, layers, components, flows and custodies. The executive piece that defends the model before business, technology and security.
DataOps, versioning, monitoring and change management. How data is operated day-to-day, not how it was designed on a whiteboard.
Rules applicable to entities, attributes and data products. What any domain coming later must respect.
Integration patterns by source type: batch, incremental, CDC, streaming, files, APIs.
How value is exposed to the business, KPIs and data products. What each audience sees and how they consume it.
Priorities, domains, success conditions. What comes after the document.
Clear and sustainable before business, technology and security. Decisions argued upfront, not defended in hindsight.
Ready to evolve into a governed platform without redoing the design. What you build next will not break what existed before.
On how data is structured and operated. Each team knows what is theirs and what is not.
For domains, teams and projects. The same reference for the next 24 months.
For governance, publication, BI and AI. Architecture is not an end, it is what enables what comes next.
| Indicative duration | 6 to 10 weeks, depending on technological complexity, number of domains and depth of the blueprint. |
|---|---|
| Modality | Closed consulting service with working sessions, document review and structured deliverables. |
| Payment terms | 50% at start and 50% at final delivery. By milestones if the scope includes deep analysis of several alternative architectures. |
| Natural continuity | Platform assessment, data governance, environment implementation, DataOps or domain onboarding. |
Architecture, governance, metadata, security, quality and exploitation thought of as one piece. We do not produce orphan technical designs.
Oriented to real industrialization, not to a pretty presentation. Architecture is measured by what it automates, not by what it documents.
Architecture is designed to be implementable. If it cannot be executed with the client's resources, it is not useful architecture.
As a structural principle, not as a footnote. Business governs, architecture custodies, IT operates.
Architecture is also evaluated by adoption, value delivered and sustainability. Not just by technical elegance.
Hand-crafted pipelines, duplications between layers, costs that grow faster than the value.
Multi-country governed analytical architecture
No client names — results that speak for themselves.
Design of the global data ecosystem architecture, legacy platform integration and analytical modernisation for a financial institution operating across multiple countries.
Definition of the corporate data strategy, implementation of a federated governance model and design of a modern architecture based on Databricks, Confluent Cloud and Snowflake to industrialise data exploitation and optimise platform costs.
Definition of the data strategy and a Data-Driven architecture to activate precision oncology analytics, clinical data governance under HL7 FHIR and mCODE standards, and interoperability between heterogeneous systems.
Representative · pending authorised publication
A solid data architecture is the foundation. What you build on top requires a clear assessment and a product ready to scale.
Start with a 30-minute conversation to understand the real scope of an architecture assessment tailored to your context.