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Single version of the truth

Single version of the truth is a fundamental concept in data warehousing and specifically in Kimball's dimensional modelling methodology.

The idea behind this concept is to create a consistent, reliable, and unified view of an organization's data, ensuring that all users can obtain consistent and accurate information from a single source of data.

In the context of Kimball's dimensional modelling, which is a popular methodology for designing data warehouses, the "single version of the truth" principle emphasizes:

1. Consistency

All data within the data warehouse should be consistent across different reports, dashboards, and analyses. There should be no discrepancies or conflicting information.

2. Integration

Data from various source systems is integrated and transformed into a standardized format within the data warehouse. This ensures that users across the organization are working with the same set of data definitions and business rules.

3. Accuracy

The data warehouse should contain accurate and up-to-date information that reflects the true state of the business. Maintaining data integrity requires regular data cleansing, validation, and reconciliation processes.

4. Accessibility

By having a single version of the truth, organizations can provide users with a centralized and reliable source of data for making informed business decisions. This eliminates the need for users to reconcile data from multiple sources or deal with inconsistencies.

5. Usability

Data is organized and structured in a dimensional model (e.g., star schema or snowflake schema) that aligns with business processes and user requirements. This allows for efficient querying, reporting, and analysis capabilities, enabling users to derive valuable insights from the data.


Overall, the "single version of the truth" principle in data warehousing ensures that organizations have a trustworthy and unified data repository that supports informed decision-making, fosters collaboration, and drives business value.

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