Implementation Readiness & Deployment Assurance Checklist
The implementation checklist serves as a rigorous readiness framework designed to ensure that the canonical data model, associated metadata structures, and automated code-generation pipelines are deployed consistently and accurately across client environments.
It provides a structured, end-to-end view of all prerequisites—spanning source-system understanding, metadata preparation, data quality governance, lineage instrumentation, and automation configuration—required before initiating full-scale ETL development.
Given that the solution operates on an insert-only, metadata-driven architecture using a supported tech stack, this checklist acts as the formal validation mechanism that all upstream dependencies are understood, documented, and operational before code execution begins.
| ☑ | Checklist Item |
|---|---|
| ▢ | Source system documentation collected and analyzed |
| ▢ | All source entities and attributes identified |
| ▢ | Interface Mapping Document created and reviewed |
| ▢ | Data quality rules defined and documented |
| ▢ | PII classification completed for every onboarded attribute (not just identified — fully classified with category, tokenization status, and exposure flag) |
| ▢ | Business sign-off obtained on mappings |
| ▢ | Metadata tables created (all 10+ tables) |
| ▢ | All metadata tables populated with complete data |
| ▢ | Metadata completeness validated (no referential integrity errors) |
| ▢ | Code generator configured and tested |
| ▢ | LDZ ingestion code generated |
| ▢ | RDV transformation code generated |
| ▢ | Data quality validation code generated |
| ▢ | Lineage capture code generated |
| ▢ | Job orchestration generated |
| ▢ | Grandmaster DAG and dependent Master DAG orchestration validated through successful end-to-end execution |
| ▢ | Target database platform validated (Oracle / SQL Server / Snowflake) and deployment prerequisites confirmed |
| ▢ | Generated code reviewed for correctness |
| ▢ | Sample data transformation tested |
| ▢ | Data quality validations verified |
| ▢ | Lineage capture verified |
| ▢ | All documentation completed and version controlled |
| ▢ | Team trained on generated code and processes |
| ▢ | Ready to proceed to Phase 2 (ETL Development) |
| DATA QUALITY CHECKLIST (Out-of-Box Categories — definitions in Metadata Model: The Core Integration Layer) | |
| ▢ | Mandatory field checks configured in Interface document for each onboarded subject area |
| ▢ | Datatype checks and referential integrity checks defined in Interface document per attribute |
| ▢ | Duplicate handling strategy confirmed in ETL design (reject / quarantine / merge) for customer and campaign keys |
| ▢ | Consent value validity confirmed — valid code set loaded, expiry date handling logic verified |
| ▢ | Campaign date validity confirmed — start/end date logic and active campaign rules verified |
| ▢ | Contact / response chronology validated — response datetime ≥ contact datetime per campaign-customer record |
| ▢ | Audience resolution completeness confirmed — resolution status captured per record; unresolved records flagged or quarantined |
| ▢ | Tokenization completeness validated — all PII-flagged attributes confirmed tokenized before LDZ load; pipeline gate in place |
| ▢ | Aggregate reconciliation checks configured — 360 view counts and sums reconcile against RDV within defined tolerance |
| PII GOVERNANCE CHECKLIST (Expanded) | |
| ▢ | Tokenization status validated before LDZ/RDV load — no raw direct identifiers permitted in pipeline |
| ▢ | No raw direct identifiers present in 360 views (Customer, Campaign, Flowchart) |
| ▢ | Free-text fields reviewed and either excluded from CDM or controlled with documented justification |
| ▢ | Consent attributes mapped and validated (source, effective date, status, channel-level precedence) |
| ▢ | MaxAI-exposed views reviewed for PII leakage — confirmed no raw identifiers in AI serving layer |
| ▢ | Aggregation thresholds applied where needed to prevent re-identification from small cohorts |
| ▢ | Audience resolution does not expose raw account/device/customer identifiers in downstream 360 views |
| ▢ | Rejected attributes documented with reason and client sign-off obtained for approved CDM attribute exposure |
| ▢ | PII matrix template completed and linked to implementation interface document (see Appendix) |
| Campaign AI Checklist | |
| ▢ | Flowchart 360 aggregation tables verified and data populated correctly |
| ▢ | Flowchart 360 key attributes indexed (flowchart_id, campaign_code, execution timestamps) |
| ▢ | Flowchart 360 pre-aggregated metrics validated (clicks, opens, responses per execution) |
| Multi Audience Support Checklist | |
| ▢ | Audience_map table populated and validated for all audience levels (customer / account / device) |
| ▢ | Bridge table verified for Campaign → Offer → Channel → Product combinations |
| ▢ |
Audience resolution logic tested for multi-grain campaign execution scenarios |
| ML Feature Checklist | |
| ▢ | ML feature views (point-in-time consistent) created and validated from Customer 360 |
| ▢ | STO and NBC model output ingestion layer configured and tested |
| ▢ |
ML prediction write-back to Customer 360 verified (NBC / STO attributes populated correctly) |
| ▢ |
Incremental watermark-driven processing confirmed for ML feature refresh cycles |