The loss of control over data quality has become a clear symptom of digital transformation initiatives that have often moved too quickly, without sufficient safeguards. Over the years, companies have multiplied their information systems, outsourced critical processes, and accelerated digitalization, creating a true technological patchwork in which data flows without real oversight. The result is tangible: obsolete data coexisting with recent data, duplicates proliferating without detection, and critical information scattered across inaccessible silos. For supply chain leaders, this drift translates into an inability to make reliable real-time decisions, an excessive dependency on IT teams for basic corrections, and growing vulnerability in the face of regulatory requirements.
In an environment where international flows are becoming increasingly complex and every decision must be made instantly, inaccurate data can block a flow, generate customs-related cost overruns, compromise product traceability, or cause a customer service disruption, with direct impacts on revenue and market share. Conversely, rigorous data governance becomes a powerful performance lever. Regaining control is no longer optional; it is a strategic necessity. It means once again taking ownership of the company’s information assets in order to regain control of operations. This effort requires structured, methodical governance that is firmly anchored in operational reality and built around four key pillars: roles, mapping, processes, and quality-driven performance management.
Breaking Down Silos: A Prerequisite for Effectiveness
In a context where organizational and system silos too often continue to hinder the flow of information, some companies are choosing to adopt a cross-functional perspective. One of the guiding principles of data governance is to avoid silos by breaking down barriers between systems and roles.
In value chain management, this approach is critical: the data produced must circulate across procurement, production, logistics, and sales without loss of information or inconsistency. A significant step forward can be achieved by structuring responsibilities around three key roles:
The Data Owner is the business reference who understands operational challenges and the consequences of poor data quality. The Data Owner defines the business rules governing data and validates its relevance, freshness, uniqueness, and completeness. In a supply chain context, the procurement manager may be the Data Owner for supplier data, ensuring its reliability across the entire chain.
The Data Administrator is the operational reference on the front line, responsible for monitoring the data lifecycle: creation, modification, and archiving. The Data Admin supports business teams in understanding the rules and facilitates the relationship between tools, users, and processes. In a multi-site company, a Data Admin may be responsible for collecting local data on behalf of all Data Owners at a given site.
The Data Officer coordinates the overall governance framework without managing each data item individually. The Data Officer creates shared reference materials such as glossaries, data dictionaries, and quality KPIs. This person facilitates governance rituals with the community of Data Officers and Data Admins, centralizes best practices, and monitors the action plan.
The golden rule is to clearly differentiate these three roles and assign responsibility for data quality to the entity that functionally produces the data.
Data Mapping as a Management Tool
Seeing clearly is the first step toward managing effectively. Data mapping consists of identifying, locating, and documenting the types of data processed by the company, their origin, usage, storage, and associated flows.
An effective data map allows companies to visualize their critical data, identify duplicates and redundancies, and focus governance efforts where they truly matter. This approach distinguishes three main types of data:
- Local data, created and used by a single department or business function.
- Enterprise data, which requires cross-functional sharing between departments, and potentially across regions or entities.
- External data, originating from third-party sources such as regulatory databases, open data, or partners.
From criticality to the “source of truth”: value-oriented governance
Good governance prioritizes the identification of critical data, whose potential impact on the business would be significant in the event of an error. Criticality is measured by combining sensitivity, usage, frequency, and regulatory exposure.
For a company, poorly managed critical data directly translates into delivery delays, additional costs, and deteriorating customer satisfaction.
Several golden rules guide this mapping exercise: only manage data that has a clear business purpose, assess the benefit-cost ratio before governing a new data set, and respect the “primary-secondary” principle, whereby a data attribute should be editable in only one place — the primary system — and then redistributed to downstream systems through interfaces.
Finally, data should only be maintained in its natural tool: management data in an ERP, and lifecycle data in a PLM, or Product Lifecycle Management system.
Processes to Ensure Reliability from the Point of Creation
Long considered a secondary objective, the formalization of data management processes has now become essential. A sound governance process is based on clear, repeatable steps to manage data throughout its lifecycle: creation, update, verification, and archiving.
This reflects the expectations of operational teams, who are increasingly aware of the impact that poor-quality data can have on their daily activities.
- Creation and referencing: Who can create data? How? Is there a standardized model and a validation workflow?
- Updates: How often should the data be updated? Who is responsible? What control rules should be applied?
- Cleansing and correction: Are there tools to detect errors or inconsistencies? Is there a recurring “duplicate-hunting” ritual?
- Archiving and deletion: What is the retention period for each type of data?
- For effective implementation, companies are advised to create a checklist for each process and to regularly update their data dictionary and validation schemas.
Measuring to Improve: Data Quality KPIs
The development of innovations in data quality management has, over time, enabled companies to move beyond the historical tension between control and operational agility.
Data quality is measured across five essential dimensions: completeness, meaning the percentage of mandatory fields completed; uniqueness, meaning the percentage of duplicates detected; freshness, meaning the actual versus expected frequency of updates; relevance, meaning the actual usage rate of the data created; and integrity, meaning the logical consistency between related fields.
Beyond data quality itself, companies must also manage the performance of governance. This includes tracking the number of anomalies reported and corrected, the average validation and creation lead time, and the adoption rate of workflows.
These indicators must be defined according to the SMART framework: Specific, Measurable, Achievable, Realistic, and Time-bound. They must also be linked to concrete action plans.
Security: Beyond Cybersecurity
Data security is not limited to technical cybersecurity. It also includes proper data classification, access management, and regulatory compliance.
Overall, securing data makes it possible to protect sensitive information, comply with laws and regulations, prevent leaks, and identify who does what with which data.
Categorization into four levels is essential: public data, which is open without restriction; private data, which is sensitive internal information; personal data, which can identify individuals; and legal data, which is subject to strict legal obligations.
Best practices often revolve around defining RACI responsibilities for security, restricting access based strictly on business need, separating sensitive roles, and integrating security by design.
For instance, in international supply chains, the EU GDPR compliance act requires identifying all personal data collected, implementing clear and traceable consent systems, and appointing a DPO, or Data Protection Officer, to coordinate the initiative.
Data confidentiality challenges are becoming even more significant with the rise of AI. Because these tools make data exploration much easier, the probability that any confidentiality vulnerability could have a major impact is increasing dramatically.
From Data Quality to Sustainable Competitive Advantage
Data governance is a strategic investment for the supply chain of tomorrow. It is not solely a matter of tools; above all, it is a mindset, an organization, and a collective discipline. Even with limited resources, every company can initiate or strengthen its approach by appointing clearly identified owners, prioritizing high-impact data, formalizing simple and pragmatic rules, and establishing rituals that embed best practices over time.
For the supply chain, the impact is direct: reliable data helps reduce processing times, optimize inventory, secure international flows, streamline collaboration with partners, and improve customer satisfaction. The efficiency of tomorrow’s company is built today, one data point and one process at a time.
This requirement becomes even more critical with the rise of artificial intelligence, which acts as an amplifier of both risks and opportunities. Predictive algorithms and generative AI promise to dynamically optimize inventory, anticipate shortages, intelligently manage flows, and automate complex operational decisions. But these promises depend on one absolute prerequisite: the quality of both training and operational data.
AI powered by inconsistent, incomplete, or biased data will produce erroneous decisions at scale, turning a performance lever into a systemic risk factor. Conversely, organizations that have invested in rigorous governance will gain a decisive competitive advantage: more reliable models, faster deployment, and auditable systems capable of meeting growing regulatory requirements, particularly around algorithmic transparency.
Data governance is therefore not a barrier to innovation. It is the “sine qua non” for securing today’s performance and preserving the ability to innovate tomorrow.
Saad KADIOUI, Data & Supply Chain Expert, Citwell.
Pierre ABOU HAMAD, Country Manager Citwell USA.