01
Which data should be mastered centrally?
Identify the domains where inconsistent definitions create reconciliation, reporting or operational problems across systems and business units.
Data Management
Master Data Management
Create consistent enterprise reference structures without ignoring the realities of local operations.
NoèmeX works with organizations to define, align and govern the shared data that sits underneath finance, operations, reporting and analytics. This includes customers, products, legal entities, accounts, locations, assets and other dimensions that must remain consistent across systems.
The work addresses both the technical structures and the operating model around them: who owns definitions, how local values map to corporate standards, how changes are approved and how downstream systems consume the mastered data.
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 master data management 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
Where is local variation legitimate?
Separate necessary local codes and structures from avoidable duplication, then define how local values relate to enterprise standards.
03
Who owns master-data decisions?
Establish owners, stewards, approval rights and escalation paths for new values, changes and exceptions.
04
How should master data reach downstream systems?
Define synchronization, mapping and validation patterns that keep consuming applications aligned.
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
Master-data domain assessment
A prioritized view of the enterprise dimensions most affected by inconsistency, duplication and unclear ownership.
02
Canonical definitions and hierarchies
Shared structures for core entities, relationships, rollups and business rules.
03
Local-to-corporate mapping model
A controlled method for reconciling necessary local structures with enterprise master standards.
04
Ownership and stewardship model
Named responsibilities, decision rights and approval workflows for master-data changes.
05
Master-data quality rules
Validation, duplicate detection, completeness and conformance controls for critical master records.
06
Synchronization and adoption roadmap
A staged approach for aligning systems and reports to the governed master-data model.
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.
Master-data alignment50+ P&LsChemical industry · Finance master data
Global financial consolidation
A global chemical finance organization was consolidating more than 50 P&L files while local master data differed from group standards. The transformation mapped local structures to corporate master data and created a single governed reporting environment, reducing manual reconciliation and improving trust in consolidated financial information.
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.
View profile →
Brendan Young
B.Com, Director of Operations
Connects transformation design with operating discipline, drawing on logistics, fulfillment and high-throughput operational leadership.
View profile →
Master Data Management
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.