1. Stick to the Flow Chart as the Operational Blueprint

In an ELT process, the flowchart serves as the foundational blueprint for operations. It’s crucial because it dictates the sequence and dependencies of data movements and transformations. For example, if the flowchart does not handle cases where an item is returned and accepted, it might lead to discrepancies such as excess or missing POs. This highlights why every potential scenario must be accounted for in the flowchart to avoid operational inefficiencies or errors.

The flowchart is the source of truth in ELT processes. Both manual and automated processes are merely implementations of this blueprint. Ensuring that the flowchart comprehensively captures all aspects of operations is essential to prevent issues.

2. Use Test Cases to Sense Check Outputs

Implementing test cases is a critical step to validate that the ELT process is performing as intended. By using provided test cases to check the outputs, you can ensure that the data transformation aligns with expected results. This is particularly important in complex systems where multiple data sources and transformations can lead to unexpected outcomes.

Test cases help in verifying that the ELT outputs make sense and match the expected data scenarios outlined in the flowchart. They act as a practical checkpoint to catch errors or misalignments early in the process.

3. Relevant Naming and Description of Models

When creating models within an ELT framework, it’s crucial to use meaningful names and descriptions. This helps in several ways:

• Clarity: Clear, descriptive names and explanations ensure that anyone who works with or reviews the models can easily understand their purpose and how they fit into the larger data ecosystem.
• Maintenance: Well-named and well-meaning models are easier to locate and maintain, especially as systems grow and become more complex. They have to capture the generality.
• Documentation: Proper descriptions serve as part of the documentation, making it easier to onboard new team members and provide context for audits or revisions.

The naming and description of models should not only reflect their functionality but also align with the conventions and terminologies used across the business. This ensures consistency and ease of understanding.