At Fluxym, we see it every day on client projects: data is the true fuel for any Procure-to-Pay or Source-to-Pay transformation. Without reliable, current, and properly governed data, even the best software quickly hits a ceiling.
Yet without dedicated attention, supplier and customer data quality degrades fast. On average, close to 30% of master data records become obsolete each year. Add to that errors, incomplete fields, and duplicates. The result? Strategic decisions based on questionable information.
This is precisely why rigorous data governance has become a foundational requirement for successful digital transformation.
1. What Is Data Governance?
Data governance refers to the policies and processes that ensure your data remains accessible, traceable, high-quality, and secure. It encompasses several core dimensions:
- Regulatory compliance: GDPR, SOX, anti-corruption legislation, CSRD, electronic invoicing requirements, tax reporting standards… Legal obligations continue to expand and demand greater rigor in data management.
- Internal policy management: defining procedures for data creation, modification, and deletion; assigning roles and responsibilities; establishing access and sharing rules.
- Data quality assurance: this is where data governance intersects with data quality. Governance defines the strategic framework, while data quality encompasses the operational actions required to maintain that quality over time.
2. The Risks of Poor Data Governance
Without clearly defined procedures, organizations face multiple risks that can directly impact operations:
- Operational risks: Outdated data can lead to placing orders with financially distressed suppliers or companies that have filed for bankruptcy, creating dependencies on unreliable partners and supply chain disruption risks.
- Financial risks: Duplicate records in your supplier or customer database eliminate visibility into total purchase or sales volumes. This erodes negotiating leverage and can result in failing to apply contracted rates. Reporting quality suffers as well.
- Regulatory risks: Missing required compliance data, incomplete tax identification numbers in electronic invoices, or gaps in extra-financial reporting can expose companies to significant regulatory penalties.
3. Building a Data Governance Strategy in 5 Steps
Step 1: Define internal roles and responsibilities
The goal is to ensure consistent data management and avoid errors caused by unclear accountability.
- Assign specific roles to key stakeholders: the Chief Data Officer (CDO), Data Quality Managers, Procurement leadership, Finance leadership… Each person should know precisely their scope of action and authorization levels.
- Establish clear access policies: who can view the data? Who can modify or delete it? This segmentation of rights is essential for maintaining database integrity.
- Implement segregation of duties for sensitive information to secure data and reduce fraud risk (for example, don’t assign bank account modification rights to a single person).
Step 2: Centralize third-party data
To reduce error and inconsistency risk, adopt the Single Source of Truth (SSOT) principle by creating a single master data repository for supplier and customer data. This centralization can occur in your ERP or an SRM/CRM solution. It allows all employees to access the same information, updated in real time.
Feel free to leverage your suppliers’ portals to facilitate data collection and updates.
Step 3: Establish a data management policy
This step formalizes all rules governing your data lifecycle. It includes four key actions:
- Create a data catalog containing all metadata: source, backup, usage, controls, owner… The goal is to thoroughly understand your database and identify information critical to your business.
- Define precise management rules for data creation, modification, and deletion. These rules should be documented and accessible to all relevant employees.
- Implement procedures to manage data access, sharing, backup, protection, and recovery in case of incidents.
- Organize data standardization and implement it in your business systems. This is key to facilitating the reading, use, and management of third-party data. A best practice to avoid duplicates: automatically verify the tax identification number or national business registration number whenever a supplier or customer is created in your system.
Step 4: Educate and train your teams
The best governance policy remains ineffective if your teams don’t apply it daily. Education and training are therefore indispensable.
- Involve employees in identifying strategic data for their function and other departments. This creates cross-functional visibility and reinforces that data is everyone’s responsibility.
- Educate them on concrete risks the company faces. Use real examples and practical cases drawn from your own experience or industry peers.
- Present the specific procedures to implement in their daily work and communicate regularly about any updates.
- Encourage development of a true data culture through regular communication essential for team engagement and sustained commitment.
Step 5: Monitor and control data quality continuously
Data governance isn’t a one-time project but a continuous improvement process. You must regularly verify the effectiveness and relevance of your procedures.
- Implement a data quality monitoring process using metrics: duplicate rates, percentage of missing values, consistency checks… Also define the frequency of these controls based on data criticality.
- Deploy an alert system to quickly identify anomalies: missing data, non-compliant formats, exceeded thresholds…
- Adjust policies based on changes: new regulations, new KPIs, identification of obsolete data… Involve users in regular reviews to gather their feedback and fuel continuous improvement.
Data governance isn’t optional. It’s a strategic necessity for any organization pursuing digital transformation.
It not only limits operational, financial, and regulatory risks but also optimizes process efficiency and properly feeds AI-powered applications.
Want to evaluate your supplier or customer data quality and identify priority improvement areas? Fluxym’s experts conduct a complimentary audit of your customer and supplier database to support this initiative.