A Comprehensive Guide to Data Validation and Model Health Checks
Introduction to Data Quality
Greetings, Business Intelligence enthusiasts! Welcome to another episode of “BI in 60 Seconds.” Today, we will delve into the critical topic of data quality, specifically when using BI4Dynamics. We will discuss the importance of data validation and model health checks to ensure the accuracy and trustworthiness of your data.
The Complexity of BI Models
Modern BI models are intricate and continually evolving in complexity. For instance, consider the customer view, an essential aspect of customer dimensions. Numerous lines of code and contributing tables are required to create the dimension customer. However, when a variable, such as a time-dependent address, is added to the mix, the BI model may become unstable and result in incorrect data.
Maintaining Data Quality
To keep data quality under control, BI4Dynamics employs a series of SQL scripts to monitor everything that goes into the model. These scripts ensure that the row count in the staging area matches the final count in the data warehouse after transformations have been applied. This process is performed for both dimensional and transactional tables (including operational, financial, and retail tables).
Inventory Payables and Receivables
At the end of the data validation process, a check is performed to confirm that the sum of all transactions for inventory payables and receivables equals the Last Child Value. This step is crucial to ensuring data accuracy, particularly when undertaking significant development efforts. It is recommended to run these tests before starting any development work and upon its completion.
Robotic Testing and Model Health Checks
In addition to manual data validation, BI4Dynamics also incorporates robotic testing to ensure data quality. This process involves comparing static and dynamic results within the working framework. Model health checks are performed as part of these comparisons, ensuring that no discrepancies are found in the data.
If the results of the robotic testing are zero, it indicates that the executed revision has passed this phase of the test successfully. This outcome is essential in confirming that the BI model is accurate and reliable before it is distributed to users.
Conclusion
In conclusion, ensuring data quality in your BI project is of paramount importance, especially when using BI4Dynamics. By implementing a combination of manual data validation, model health checks, and robotic testing, you can be confident in the accuracy and trustworthiness of your data. These rigorous processes help prevent issues that can arise due to the increasing complexity of BI models and ensure that users maintain their trust in the data they rely on for critical business decisions.
Thank you for joining us in this episode, and we look forward to seeing you in our next instalment!
