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

1 Define your risk data requirements

The first step in designing a risk data warehouse is to define your risk data requirements. This means identifying the types, sources, and formats of risk data that you need to capture, store, and access for your ERM activities. You also need to consider the frequency, granularity, and quality of the risk data, as well as the security and privacy standards that apply. Your risk data requirements should align with your ERM framework, policies, and processes, as well as the expectations of your stakeholders and regulators.

2 Choose your risk data architecture

The next step is to choose your risk data architecture, which is the way you structure, organize, and manage your risk data in the warehouse. There are different types of risk data architecture, such as relational, dimensional, or hybrid, that have different advantages and disadvantages for ERM. You should choose the one that best suits your risk data requirements, your analytical and reporting needs, and your available resources and technology. You should also design your risk data model, which is the logical representation of your risk data entities, attributes, and relationships.

3 Implement your risk data integration

The third step is to implement your risk data integration, which is the process of extracting, transforming, and loading (ETL) your risk data from various sources and systems into the warehouse. You should use appropriate tools and techniques to automate and streamline your risk data integration, such as batch or real-time processing, data quality checks, data reconciliation, and data governance. You should also establish clear roles and responsibilities for your risk data integration team, as well as monitor and audit your risk data integration activities.

4 Optimize your risk data performance

The fourth step is to optimize your risk data performance, which is the ability of your risk data warehouse to deliver fast, accurate, and consistent results for your ERM activities. You should use various methods and measures to optimize your risk data performance, such as indexing, partitioning, caching, compression, and tuning. You should also test and benchmark your risk data performance regularly, as well as identify and resolve any issues or bottlenecks that may arise.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?