01
Who has authority over which data decisions?
Define accountable owners, stewards, councils and escalation paths across corporate, regional and local levels.
Data Management
Data Governance
Turn data governance into an operating system for decisions, ownership and controlled change.
NoèmeX works with organizations to establish practical governance across data models, definitions, ownership, access, quality and change. The work is designed for organizations where global standards must coexist with local responsibilities and where governance cannot depend on spreadsheets, meetings and individual memory.
Governance is translated into roles, workflows, standards, approval paths and platform controls so teams can move faster with clearer accountability rather than adding another layer of policy that sits outside day-to-day work.
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 governance 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
Which standards must be enterprise-wide?
Separate mandatory global definitions and controls from areas where local variation is legitimate.
03
How should changes be proposed and approved?
Create traceable workflows for model, definition, access and quality changes instead of relying on meetings and spreadsheets.
04
How should governance be embedded in technology?
Translate policy into platform controls, role-based access, certification, metadata and operational workflows.
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
Governance operating model
Roles, responsibilities, forums, decision rights and escalation paths across the organization.
02
Data ownership and stewardship framework
Named accountability for domains, definitions, quality and approved use.
03
Standards and policy framework
Practical standards for modeling, naming, quality, access, metadata, retention and change.
04
Approval and certification workflows
Repeatable processes for proposing, reviewing, approving and certifying data assets and models.
05
Governance platform requirements
Functional requirements for workflow, access, auditability, model management and collaboration tooling.
06
Adoption and measurement model
Metrics and routines that show whether governance is being used, where bottlenecks remain and how the model should evolve.
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.
Global data governance70 countriesChemical industry · Enterprise governance
Global data governance platform
A multinational corporation with more than 60,000 employees and over 4,000 systems relied heavily on spreadsheets and manual approvals for data governance. The program centralized model creation and certification, simplified access control, introduced governance-driven feature prioritization and created a single platform for global consistency with local participation.
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.
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Data Governance
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.