Integrations Inventory and Attributes - Data and Information attributes for the Integrations Inventory
Integrations Inventory and Attributes
Chapter 19. Data and Information attributes for the Integrations Inventory
Executive Summary: Chapter Overview
IF4ITThe Bottom Line
Core Concepts
| Concept | Definition & Strategic Role |
|---|---|
| Data Lineage | Source Data Object and Target Data Object identify where data originates and where it lands, enabling precise lineage and impact analysis. |
| Transformation Logic | Transformation Required captures whether payloads are modified and where that logic resides, exposing hidden complexity and key-person dependency risk. |
Quick Q&A
Question: Why is transformation documentation important?
Read More Below
Data and Information attributes capture the specific data objects, formats, and transformations involved in this Integration — connecting the integration map to the enterprise data architecture.
| Attribute Name | Maturity | Description and Notes |
|---|---|---|
| Source Data Object | Walk | Description — The specific entity, table, schema, object, file, queue, or topic in the source system that provides the data for this integration. Benefit(s) — Enables precise data lineage tracing. When a source system is decommissioned or its schema changes, Source Data Object identifies exactly which integrations are affected and what must be remapped. Source — Manual. Examples — CUSTOMERS table in CRM_PROD database, /orders/confirmed endpoint, customer-events Kafka topic, VENDOR_INVOICES_OUT SFTP directory |
| Target Data Object | Walk | Description — The specific entity, table, schema, object, file, queue, or topic in the target system that receives the data from this integration. Benefit(s) — Completes the data lineage picture from source to target. Enables impact analysis when a target system’s receiving schema changes. Source — Manual. Examples — CUSTOMER_MASTER table in ERP_PROD database, /intake/customers endpoint, customer-profiles S3 bucket, PAYMENT_IN queue |
| Transformation Required | Walk | Description — Whether data is transformed between source format and target format as part of this integration, and where the transformation logic resides. Benefit(s) — Surfaces hidden complexity in the integration. A transformation that lives in undocumented custom code is a key person dependency and a fragility risk. Explicit transformation documentation is the first step to reducing that risk. Source — Manual. Examples — Yes — transformation logic in Informatica mapping; Yes — transformation in Python script; Yes — field mapping in MuleSoft DataWeave; No transformation — pass-through |
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