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In our ongoing series, our CEO Michael Olschimke addresses a question from the audience about modelling daily exchange rates within the DV2.0 framework for a non-banking industry. The query highlights a common challenge faced by many organizations: integrating and managing exchange rate data effectively.
The question posed was, “How would you model daily exchange rates in DV2.0 for a non-banking industry? We are already using a reference table for the list of currencies (I guess we would have currency as a hub in the banking industry, but that is not our case). Now we also need daily exchange rates for currency conversions in the datamart layer. I would start with a Link for exchange rates, but do we need to create a hub for currencies? How about existing references to currency in the existing model (currently in SAT, because we have currency as a reference table)?”
Michael’s response delves into the intricacies of data modelling in such scenarios. He suggests that even though your industry is non-banking, establishing a structured and scalable way to manage exchange rate data is crucial. Using a Link for exchange rates is a good starting point, but creating a hub for currencies could provide additional benefits. This hub would act as a central repository for all currency-related information, ensuring consistency and ease of access across different layers of the data architecture. Additionally, integrating existing references to currencies within the model can streamline operations and enhance the accuracy of financial data analytics.
In conclusion, Michael emphasizes the importance of a well-thought-out data architecture. By creating a dedicated hub for currencies and effectively linking exchange rate data, organizations can ensure more accurate and efficient currency conversions in their datamart layer. This approach not only aligns with best practices in data vault modelling but also supports the broader goal of maintaining data integrity and usability across the enterprise.
Meet the Speaker
Michael Olschimke
Michael has more than 15 years of experience in Information Technology. During the last eight years he has specialized in Business Intelligence topics such as OLAP, Dimensional Modelling, and Data Mining. Challenge him with your questions!
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