Customer records drift apart in the same way in a trading company as in a financial group: each system keeps its own version, and nobody is responsible for reconciling them.

Governance is the standing discipline that keeps data quality in place once data strategy has set the direction. Strategy decides what needs fixing and in what order. Governance stops quality from degrading again after it has been fixed.

Data ownership and stewardship

Every major data domain (customer, product, employee, financial) needs a named owner accountable for its quality and a steward responsible for day-to-day maintenance. Without them, quality issues are everyone's problem and therefore nobody's, which lets duplicate customer records and inconsistent product coding persist for years.

Quality standards and enforcement

Governance defines what "correct" means for each data field (a valid VAT registration number format, a complete customer address, a properly categorized expense) and builds validation into the systems that capture data. Enforcement at the point of entry costs far less than correction afterwards.

Access and security

Governance also covers who may see and change what. This connects to broader control design and to AI governance where models process the governed data. For businesses in Riyadh it includes PDPL compliance for personal data, which needs a named owner and not an assumption that someone handles it implicitly.

A hypothetical example

Consider a distribution group that finds, during a BI project, that its largest customers by revenue in the CRM do not match those by receivables in the ERP. The same legal entities are recorded under slightly different names. Governance would assign customer master ownership to a specific role, set one numbering scheme and require a check for existing matches before a new customer is created.

Making governance stick

A framework that is documented once and never revisited becomes irrelevant as the business changes. We build in periodic review, measurable quality metrics reported to a named owner and a lightweight change process, so governance evolves with the business and does not become a document nobody consults.

Starting small and proving value

A program launched across every data domain at once tends to stall under its own scope. We start with the domain causing the most visible pain, usually customer or financial master data, show that the framework improves quality in a measurable way, and then extend it domain by domain.

Connecting governance to Zakat and regulatory reporting

Master data quality has a direct compliance side. An inconsistent customer VAT number or a duplicated supplier record can lead to an incorrect VAT return or Zakat calculation, and not only a confusing report. Treating governance as a compliance matter as well as a reporting one makes it easier to resource properly.

Measuring quality with numbers

Instead of relying on a general impression that data has improved, we track specific measures: the share of complete customer records, the number of duplicates found each month and the share of transactions that fail validation at entry. These show whether governance is working or has remained a policy document that changes nothing.