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Interface Mapping Adoption Guide
Why Interface Mapping Matters
A CDM onboarding project can only succeed if the mapping exercise is done well. Poor mapping has cascading effects across data quality, ETL generation, analytics, compliance, timeline, and MaxAI readiness.

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Interface Mapping Adoption Guide
- Introduction
  This Adoption Guide explains, in plain language, how to perform that mapping exercise end-to-end, from understanding your source data all the way to validated, signed-off mapping rows ready for metadata loading and ETL generation.
- Why Interface Mapping Matters
  A CDM onboarding project can only succeed if the mapping exercise is done well. Poor mapping has cascading effects across data quality, ETL generation, analytics, compliance, timeline, and MaxAI readiness.
- Where It Fits in CDM Onboarding
  CDM onboarding follows a structured lifecycle. The interface mapping exercise sits at the heart of the Discovery and Design phase — after source understanding and before any ETL or DDL is generated.
- Roles and Responsibilities
  The mapping exercise requires involvement from five key roles, each bringing specific expertise and perspective to ensure the exercise is complete and correct.
- Pre-requisites Before Mapping Starts
  Do not open the MID until the following artefacts have been collected from the client. Attempting to map without these leads to guesswork and rework.
- Understanding CDM Subject Areas
  You do not need to know the full CDM model in detail before starting. Use the subject area guide to identify which family of LDZ tables your source data likely belongs to.
- Mapping Principles
  These principles must guide every mapping decision. Violating them leads to data quality problems, ETL complexity, and rework.
- Step-by-Step Mapping Method
  Follow all eight steps in sequence for each source system in scope. Do not skip steps to save time — each step produces decisions that the next step depends on.
- How to Choose the Right Target Table
  Use this decision guide when you are unsure which CDM entity to target. Work through the questions from top to bottom and stop at the first match.
- Common Mapping Patterns
  The following patterns appear in almost every CDM onboarding engagement. Recognising the pattern early saves significant time.
- Worked Examples
  The following worked examples use a fictional retail bank, "Horizon Bank", which is onboarding its core banking data into CDM.
- Data Quality and PII Guidance
  Every mapping row in the MID should have at least one data quality rule. Every source column containing personally identifiable information must be flagged in the PII column.
- Handling Gaps and Extensions
  Not every source field will have a direct target in the standard CDM model. Follow a hierarchy of options before raising an extension request.
- Review and Sign-off Process
  The mapping is not complete until all three sign-off gates have been passed. The order matters — do not request engineering sign-off before business sign-off.
- Debugging and Validation Steps
  After the first ETL run, use the following structured debugging checklist to validate that data has loaded correctly from source to LDZ. Work through each check in sequence.
- Common Errors and FAQ
  This section addresses frequently encountered issues during the mapping exercise and provides remediation approaches.
- Final Checklist
  Use this checklist to confirm that the mapping exercise is complete and the MID is ready for handoff to engineering. Every item must be checked before sign-off.
CDM Redbook

Why Interface Mapping Matters

A CDM onboarding project can only succeed if the mapping exercise is done well. Poor mapping has cascading effects across data quality, ETL generation, analytics, compliance, timeline, and MaxAI readiness.

Table 1. Impact of Poor Mapping
Impact area	What goes wrong with poor mapping
Data Quality	Incorrect fields flow into CDM, poisoning downstream 360 Views and AI models
ETL Generation	Metadata cannot drive code generation if source fields are ambiguous or missing
Analytics	Segment and audience queries return wrong results because demographic or account attributes land in the wrong entity
Compliance	PII fields not flagged result in masking/encryption gaps and regulatory exposure
MaxAI Readiness	The Segmentation Agent, Offer Agent, and Content Agent rely on clean, complete 360 View data — bad mapping breaks agentic scoring

Important: The mapping exercise is not a formality. It is the specification that drives all downstream engineering. Invest time here and the rest of the project accelerates. Cut corners here and every downstream phase pays the cost.