CJA derived field validator

OVERVIEW]
An AI-assisted workflow to ensure parity in derived field components across data views/connections

[IMPACT]
Eliminate variance in derived field components across data views/connections.

[LESSONS]
Not included

[TRYABLE]
CJA derived fields require logic to define desired metrics and dimensions. This logic must be translated from business requirements and small variance in logic can result in unexpected variances in data.

Additionally, derived fields are specific to each connection. This means they must be recreated in other connections. For example, a derived field in a prod connection is completely separate from one in a dev connection. This can easily lead to variances in the derived field definition and/or component settings which can also cause variance in data.

Ensure Derived Field/Component Meets Business Requirements

1. Build out the derived field
2. Add to a data view and configure appropriately
3. Use the Auto CJA SDR tool to download the components JSON definition
4. Use a company-approved AI tool to validate that the derived field logic matches the business requirements.
5. If updates are needed, use the updated JSON and AI tools again to ensure accuracy.

Ensure Cross Data View/Connection Consistency

Note: Shared Metrics & Dimensions may be a better solution for data views in the same connection.

1. Build out the derived fields in both connections
2. Add to data views and configure appropriate in both connections
3. Use Brian Au's CJA Auto SDR tool to download the components' JSON definition from each data view
4. Use a company approved AI tool to compare both JSON definitions looking for any variances.
5. Update any derived field logic or component settings to eliminate variance, using updated JSON definitions and AI tool validation to ensure consistency.

[SETUP]
- CJA Auto SDR
- Company-approved AI tool

[SAMPLE_OUTPUT]
Not included