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Interface Mapping Adoption Guide
Mapping Principles
These principles must guide every mapping decision. Violating them leads to data quality problems, ETL complexity, and rework.

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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

Mapping Principles

These principles must guide every mapping decision. Violating them leads to data quality problems, ETL complexity, and rework.

Table 1. Core Mapping Principles
Principle	Explanation
Map by business meaning first	Never map by column name similarity alone. A source column called "phone" might be a contact number that maps to ldz_party_contact — or it might be a branch phone that maps to ldz_branch_dtl. Always confirm the business meaning.
Identify the grain of the source table first	Before mapping a single column, determine: what does one row represent? One customer? One account? One daily balance? Mapping fails when grain is misunderstood.
One source table may produce multiple CDM entities	A "Customer Master" table typically produces rows in party_demographic, party_identity, and party_contact. Plan for this upfront — it requires separate mapping rows for each target.
Multiple source tables may populate one CDM entity	A bank may have separate KYC and CIF tables that both contribute to ldz_party_identity. This requires a union or join strategy — document it explicitly.
Distinguish data type by category	Before mapping each table, classify it: Is it master data? Reference data? Relationship data? Transactional or event data? The category determines the target entity family.
Separate attribute mapping from relationship mapping	Do not bury relationship data (who owns which account) inside a demographic target table. Relationship data goes into dedicated relationship entities like ldz_account_party.
Always preserve the source business key	The source natural key (e.g., CUST_ID from the source CRM) must flow into CDM. This becomes the natural key in the LDZ and drives hash key generation for the RDV layer.
Document transformation logic explicitly	Do not leave transformations as "implied". Every derivation, date format conversion, code mapping, or null handling rule must be written in the Transformation column of the MID.
Do not force-fit unclear fields	If you cannot confidently determine the business meaning of a field, do not guess. Mark it as PENDING CLARIFICATION and seek input from the client data SME.
Use attrib_* columns sparingly	LDZ tables contain flexible attribute columns (attrib_1 through attrib_n) for short-term extension. These are for controlled, approved use cases — not a dumping ground for fields that do not fit anywhere else.