A data governance framework is the comprehensive set of policies, roles, decision rights, and accountability mechanisms that govern how data is created, defined, managed, used, and protected across an organisation’s systems. In a finance context, it answers the questions that determine whether financial data can be trusted: Who has the authority to add a new cost centre to the chart of accounts? Who approves changes to dimension hierarchies in the EPM? What is the retention policy for transactional data in the ERP? Who owns the definition of EBITDA as it is calculated in the BI platform, and who can change it? Without a governance framework, these decisions are made ad hoc by whoever has system access — producing data environments where the same metric means different things in different reports, where master data changes are made without impact assessment, and where regulatory data retention requirements are not systematically enforced.
The Three Layers of Finance Data Governance
An effective finance data governance framework operates across three layers, each with distinct ownership and accountability.
| Layer | Components | Typical Owner |
|---|---|---|
| Policy layer | Data classification policy; retention policy; access control policy; data quality standards | CFO / CIO jointly |
| Stewardship layer | Data stewards by domain (finance, HR, commercial); change control processes; issue escalation | Head of Finance Systems / Data Management |
| Technical layer | Data catalogue; metadata management; access controls; audit logging; lineage tracking | IT / Enterprise Architecture |
GCC and Egypt-Specific Regulatory Dimensions
Data governance in GCC and Egyptian enterprises now carries explicit regulatory implications that did not exist five years ago. Saudi Arabia’s Personal Data Protection Law (PDPL), effective 2023, establishes data subject rights and data controller obligations that affect how customer and employee data stored in ERP systems is managed, retained, and shared. The UAE’s Federal Data Protection Law imposes equivalent requirements for UAE-based entities. Egypt’s Data Protection Law No. 151 of 2020 applies to personal data processed by Egyptian entities. Finance technology leaders must ensure that the data governance framework addresses personal data handling in ERP systems — particularly customer master data, employee payroll records, and vendor contact information — not only financial transaction data.
Data Governance and EPM Close Quality
The most direct impact of data governance quality on the finance function is the period-end close. A finance team that operates without a dimension change control process discovers mid-close that a cost centre was renamed in the ERP without the EPM mapping being updated — producing unallocated variances that require manual investigation. A finance team with a governed dimension change process has the EPM mapping updated before the period closes, because the change control workflow requires EPM impact assessment before any ERP change is approved. Data governance is not an abstract IT discipline; it is the operational control that prevents close cycle disruption.
What Goes Wrong in Practice
The most common data governance implementation failure is designing a governance framework that is too bureaucratic for the pace of the business. When every vendor master addition requires three levels of approval and a five-day turnaround, procurement teams bypass the process and create vendors directly — exactly the uncontrolled behaviour the governance was designed to prevent. Effective data governance is calibrated to the risk profile of the data change: low-risk changes (adding a vendor contact email) should have a lightweight process; high-risk changes (restructuring the entity hierarchy in the EPM) should have a multi-stakeholder governance process. One-size-fits-all governance produces compliance theatre rather than actual data control.
How Loop Wise Solutions Designs Data Governance
We design data governance frameworks from the finance function’s specific risk profile — identifying the data domains where governance gaps produce the highest operational or compliance cost, and designing proportionate processes for those domains first. Governance frameworks designed from a theoretical completeness perspective but not calibrated to operational reality are not used; governance frameworks designed to solve the specific problems the finance team experiences are adopted and maintained.
