APRA CPG 235 Managing Data Risk: A Comprehensive Guide
CPG 235 Managing Data Risk: Navigating the Landscape of Data Governance.
They say 'data is the new gold,' and indeed, its value is immeasurable. To harness this value, data must be free of quality issues, adequately protected, and governed as a valuable asset. In the realm of data governance, the Australian Prudential Regulation Authority, APRA, introduced guidelines encapsulated in CPG 235 in 2013.
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7 Key Aspects of Data Governance
1. Managing Data Risk
Data, as one of the most valuable assets for regulated entities, demands attention to information and IT risks. APRA emphasises the importance of implementing data risk management practices to apply adequate controls throughout the data lifecycle.
2. Implementation of Data Management Framework
Integral to a bank's change management initiative, the data management framework must become a business-as-usual process. Clearly defined roles and responsibilities, including chief data officers, data custodians, owners, and stewards, play a crucial role.
3. Generating Staff Awareness & Support
Firm-wide campaigns are essential to onboard non-data professionals, fostering adherence to policies and standards outlined in the data management framework.
4. Managing Risks throughout Data Lifecycle
Identifying and mitigating risks at each stage is crucial. Data lineage diagrams are invaluable, aiding in improving data quality, implementing controls, and automating processes.
5. Implementation of Controls & Validations
Transparency is key. Meta-data repositories with business glossaries, data transformation logic, and controls on data access ensure security, integrity, and fitness-for-purpose, especially in outsourcing or offshoring scenarios.
6. Managing Data Quality
A robust Data Quality (DQ) issue management framework is vital. Designated teams work towards identifying, resolving, and reporting DQ issues as part of business-as-usual processes.
7. Data Risk Assurance
In the final leg of CPG 235, APRA expects regulated entities to seek regular assurance that data quality is appropriate. Internal audit or independent functions conduct systematic assessments, ensuring effective data risk management over time.
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