Master Your Data to Accelerate Performance

Duplicate data, inconsistent master data, poor decision-making—the cost of ineffective data management is real. Master Data Management (MDM) ensures the reliability of your core data, while Master Data Governance (MDG) defines ownership and how it is managed. Citwell supports you across both dimensions to turn your data into a true competitive advantage.

At a time when data is a strategic asset, organizations must ensure its quality and reliability. Master Data Management (MDM) encompasses the tools and methodologies used to centralize and manage master data, while Master Data Governance (MDG) defines the rules, processes, and roles that ensure its quality and consistency. Together, they establish a single source of truth—essential for performance and effective decision-making.

Our convictions

How to Optimize Your Data Governance and Data Management

Data quality is first and foremost the responsibility of functional Data Owners, that is the departments that enter or use the data on a daily basis.

By assigning them direct ownership of specific data sets, organizations strengthen their accountability and engagement in managing the information they produce.

Each business function is accountable for its data, supported by a central Data Office.

Ensuring the quality of critical data is a shared responsibility.

This approach balances operational efficiency with sustainable data quality.

Concrete metrics are essential to effectively manage data.

Data governance requires clear KPIs to measure completeness, freshness, uniqueness, and relevance.

We implement dashboards that track both data quality and the performance of governance processes.

Organizations must move beyond internal silos to adopt an end-to-end digital perspective.

Data should reside where it is used and adds value, with clear distinction between local and corporate data.

We recommend prioritizing data that delivers clear business value.

It is essential to assess the cost-benefit ratio before managing new data sets.

This pragmatic approach ensures efforts are focused on what truly creates value.

Our approach to data architecture is built on three core principles:

A single master system per data domain to ensure a reliable and shared source of truth
Standardized interfaces to feed downstream systems, ensuring quality, traceability, and scalability
A target architecture exclusively driven by this data design, to avoid unnecessary complexity and stay focused on business value

Our Expertise

Our Solutions to Implement Data Governance

Maturity Assessment and Audit

Assessment of your data governance maturity across four key pillars: roles and responsibilities, data mapping, security, and management processes.

Definition of Group Data Standards

Establishment of a structured data governance framework, including the definition of ambitions, roles and responsibilities, and target data architecture.

Data Mapping and Architecture Design

Definition of the data architecture by identifying where each data set should reside and establishing parent-child relationships.

MDG Organization Setup

Definition of the data governance organizational model (MDG: Master Data Governance), including sizing, role descriptions, and responsibilities.
Support for implementation and deployment.

Definition of Your MDM Processes

Definition of processes for data creation, enrichment, and archiving/deletion (MDM: Master Data Management).
Development of data quality KPIs (completeness, consistency, freshness, etc.).

Have a question? A project in mind?

Get in touch with our Data Governance and Data Management consultants

Saad Kadioui

Information Systems Transformation Expert

Saad is a recognized expert in ERP projects, with over 17 years of experience in IT transformation and business process digitalization, particularly across SAP, Infor, and Microsoft environments.

A Partner at Citwell, he supports organizations in ERP transformation programs from the early ideation and business case stages, combining strong methodological expertise, deep functional knowledge, and a results-driven approach.