How do you design a risk data warehouse that supports ERM objectives?

On possible approach to defining an organization's Risk Data requirements is to assess the organizations current and future risk reporting needs and retracing your steps back from the end node to assess the underlying data requirements (e.g. understanding the granularity and frequency of data needed, possible data transformation requirements etc.)

In my experience Risk Data requirements well defined in every organization. Particularly we work in BFSI space where Risk data Requirements are well defined and documented framework, policies and processes. We have developed surveys and metadata as per ERM stakeholders and regulations

Designing a risk data warehouse begins with defining risk data requirements like types, sources, formats, frequency, and quality. Consider alignment with ERM framework, policies, stakeholder expectations, and regulatory standards.

Using modern data lake and cloud technologies relational vs dimensional is irrelevant. Since data captured in layered /Zonal approach in data lake retained as Raw, Sanitized, Confirmed and curated layers enables risk architecture robust.

Select the appropriate risk data architecture for your ERM by considering types like relational, dimensional, or hybrid. Choose based on risk data requirements, analytical needs, resources, and technology. Design a risk data model representing data entities, attributes, and relationships.

Best practice to use ETL/ELT native tools in cloud or on Prem technologies, for example Glue/AWS and Azure Data Factory covers data governance, data lineage, data dictionary, and data quality built in function. No need to buy, develop and implement.

Implementing risk data integration involves ETL processes to extract, transform, and load data into a warehouse. Automation tools, batch or real-time processing, data quality checks, and governance are essential. Clear roles, monitoring, and auditing ensure effective risk data integration.

Modern data lake architecture provides performance. All optimization techniques are default as part of design and architecture

Optimizing risk data performance is the fourth step, focusing on enhancing the risk data warehouse's speed, accuracy, and consistency for ERM tasks. Methods like indexing, partitioning, caching, compression, and tuning should be employed for performance optimization. Regular testing, benchmarking, issue identification, and resolution are essential to maintain efficient risk data performance.

Updating the risk data warehouse is crucial in ERM, involving adding, modifying, or deleting risk data to align with changes in the risk environment, ERM goals, or regulations. A well-defined procedure for updating the warehouse, along with a backup plan, is essential. Communication with stakeholders and users, along with training and support on utilizing the updated risk data, is also important.

In the final step, evaluating the risk data warehouse is crucial to assess its effectiveness and efficiency in supporting ERM objectives. Utilizing criteria like data quality, availability, usability, security, compliance, and user satisfaction for evaluation is essential. Gathering feedback from stakeholders and users, and implementing improvements based on the evaluation results are key actions to enhance the risk data warehouse.