01
Where should critical data be mastered?
Define authoritative sources and the boundaries between operational systems, master data, analytical repositories and downstream products.
Data Management
Data Architecture
Create a coherent data structure across systems, business domains and analytical use cases.
NoèmeX designs data architectures that connect business meaning to technical structure. The work covers how core data is modeled, where it is mastered, how it moves between systems, where it is stored and how analytical and AI workloads consume it.
The objective is a data estate that can scale without reproducing the same inconsistencies, duplications and reconciliation problems every time a new system or use case is introduced.
NoèmeX perspectiveMake the system intelligible first: reveal the structure, constraints and variables that determine where change can produce the intended outcome.
Related leadership priorities
Data priorities change with the decision the organization needs to trust.
Use these pathways to connect data foundations to governance, technology, AI readiness and executive decision-making.
Questions we address
The decisions that shape the data architecture work
We frame the engagement around the business, governance and technical questions that must be resolved for the capability to work reliably at enterprise scale.
02
How should business concepts be modeled?
Establish common definitions and reusable core models for entities, relationships, hierarchies and measures.
03
How should data move across the estate?
Define integration patterns for batch, near-real-time, event-driven and industrial data without creating uncontrolled point-to-point dependencies.
04
How should the architecture support analytics and AI?
Ensure the target architecture can provide governed, traceable and performant data for BI, forecasting, optimization and AI use cases.
What we deliver
A practical structure that can move into implementation
The exact scope depends on the organization, but the work typically produces a combination of these outputs.
01
Current-state data architecture
A map of major sources, repositories, data flows, models, interfaces and technical constraints.
02
Target-state architecture
A future-state design covering source systems, integration, storage, master data, analytical layers and AI consumption.
03
Core data models
Shared entity, relationship, hierarchy and metric models that reduce inconsistent interpretations across teams.
04
Architecture principles and standards
Rules for data ownership, integration, reuse, security, retention and platform decisions.
05
Domain and platform boundaries
Clear responsibilities for operational, analytical and master-data platforms and the data products they serve.
06
Migration and transition roadmap
A sequenced plan for moving from fragmented current-state patterns toward the target architecture without disrupting critical operations.
Selected work
Evidence from a real enterprise data environment
This example is drawn from the documented Global Data & BI case-study portfolio and illustrates the operating conditions behind the service.
Enterprise data architecture4,000+Chemical industry · Enterprise architecture and governance
Global data governance platform
The documented global governance program operated across more than 4,000 systems with inconsistent local data structures and disconnected model definitions. The transformation standardized core data structures, mapped local data to corporate standards and established a centralized platform for model creation, certification and controlled evolution.
Read the case studyRelated data services
Continue into the adjacent data capability.
These services often intersect in the same transformation program, but each has a distinct purpose and page.
How we work
Use the engagement model that fits the data problem.
Data work can begin as focused advisory and continue into project delivery, embedded support or an ongoing managed model.
Advisory
Focused support for assessment, governance, architecture and roadmap decisions before major implementation commitments are made.
Read more →Project delivery
Structured implementation when the data design needs to move into engineering, migration, rollout and adoption.
Read more →Embedded teams
Specialists working alongside internal teams when continuity, production support and ongoing improvement matter.
Read more →Your Contact Persons
Connect directly with the people responsible for this work. Explore their experience and contact options.
Zahra Fathisalout
Founder · Eng., CSP, CMLAI
Corporate governance · ASC-C.Dir. candidate
Works across strategy, enterprise transformation, data, AI and governance, bringing operational engineering experience to complex transformation decisions.
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Brendan Young
B.Com, Director of Operations
Connects transformation design with operating discipline, drawing on logistics, fulfillment and high-throughput operational leadership.
View profile →
Data Architecture
Start with the data problem that is creating friction.
Share the business process, systems involved, current data constraints and the decision or operational outcome that needs to improve. Our teams can determine where the work should begin and how it connects to the wider data estate.