Data Platform Audit & Review –  Checklist

1. Program Governance & Scope Validation

•               Clear definition of Data Track scope (ingestion → transformation → reporting)

•               Alignment of audit scope with business objectives and success criteria

•               Stakeholder identification (Business, Data Engineering, Reporting, Platform, Governance)

•               RACI matrix defined and agreed

•               Availability of program roadmap and milestone tracking

•               Defined SLAs/OLAs for data pipelines and reporting

2. Technology & Architecture Assessment

2.1 Azure Data Platform & Fabric

•               Azure services inventory documented (ADF/Fabric, ADLS, Synapse, Databricks, etc.)

•               Justification for technology choices (cost, scalability, maintainability)

•               Environment strategy (Dev / QA / Prod isolation)

•               Infrastructure as Code (ARM/Bicep/Terraform) implemented

•               Naming conventions and resource organization standards followed

•               Cost optimization mechanisms (auto-scaling, pause/resume, storage tiering)

2.2 Medallion Architecture (Bronze / Silver / Gold)

•               Clear implementation of Bronze, Silver, Gold layers

•               Data movement and transformation logic defined per layer

•               Schema evolution and change handling strategy

•               Data lineage across layers documented

•               Data quality checks enforced at each stage

•               Partitioning, indexing, and performance optimization strategies applied

2.3 Integration & Data Movement

•               Source system onboarding pattern standardized

•               Incremental vs full load strategies defined

•               CDC (Change Data Capture) implementation where required

•               API/streaming ingestion patterns validated (if applicable)

•               Dependency management across pipelines

3. Data Engineering & Pipeline Quality

•               Reusable pipeline frameworks/patterns used

•               Parameterization and modularization of pipelines

•               Error handling and retry mechanisms implemented

•               Logging and observability integrated (Log Analytics, App Insights)

•               Pipeline performance benchmarks defined and met

•               Data validation frameworks in place (Great Expectations / custom rules)

•               Backfill and reprocessing strategy documented

4. Data Governance & Compliance

•               Data catalog and metadata management implemented (Purview / equivalent)

•               Data classification (PII, sensitive data) defined and enforced

•               Role-based access control (RBAC) implemented

•               Data retention and archival policies defined

•               Audit trails and access logs maintained

•               Compliance with organizational and regulatory standards

5. Reporting & Consumption Layer

•               Reporting architecture defined (Power BI / Fabric / external tools)

•               Semantic layer / data model design validated

•               KPIs and metrics definitions standardized

•               Data refresh strategies optimized

•               Performance tuning for dashboards and reports

•               Row-level security (RLS) implemented where needed

•               Self-service BI enablement supported with governance controls

6. Quality & Deliverables Across Lifecycle

6.1 Design Phase

•               High-level and detailed architecture documents available

•               Design review approvals completed

•               Non-functional requirements (NFRs) defined

6.2 Build Phase

•               Code versioning (Git) and branching strategy followed

•               CI/CD pipelines implemented

•               Unit and integration testing completed

6.3 Testing Phase

•               Test cases covering functional and non-functional scenarios

•               Data reconciliation and validation completed

•               Performance and volume testing executed

6.4 Deployment Phase

•               Release management process defined

•               Rollback strategy documented

•               Environment promotion process standardized

6.5 Support & Operations

•               •  L1/L2/L3 support model established

•               •  Monitoring dashboards available

•               •  Incident management and RCA processes defined

7. Standards, Best Practices & Alignment

•               Adherence to enterprise architecture standards

•               Coding standards and review processes followed

•               Documentation completeness (runbooks, SOPs, architecture diagrams)

•               Reusability and standard frameworks leveraged

•               Alignment with DataOps / MLOps (if applicable)

8. Engineering Maturity Assessment

•               Automation level across pipelines (low → high maturity)

•               Observability maturity (logs, alerts, predictive monitoring)

•               CI/CD maturity (manual → fully automated)

•               Data quality maturity (reactive vs proactive)

•               Self-service vs centralized data model balance

•               Use of advanced patterns (metadata-driven pipelines, orchestration frameworks)

9. Risk Identification & Gap Analysis

•               Single points of failure identified

•               Scalability risks evaluated (data volume growth, concurrency)

•               Performance bottlenecks identified

•               Data quality risks highlighted

•               Security vulnerabilities assessed

•               Dependency risks (external systems / teams)

10. Recommendations & Improvement Plan

•               Prioritized list of findings (High / Medium / Low)

•               Quick wins vs strategic improvements identified

•               Roadmap for remediation actions

•               Ownership and timelines assigned

•               Governance model enhancements proposed

Kick-off Call Checklist

Scope & Approach

•               Confirm audit scope (technology, lifecycle, governance, reporting)

•               Agree audit methodology (interviews, artifact review, walkthroughs)

•               Define audit timeline and milestones

Stakeholders & Roles

•               Confirm stakeholders across Data Track

•               Identify SMEs for each domain (ingestion, transformation, reporting)

•               Establish communication and escalation channels

Artifacts & Inputs

•               Architecture diagrams (HLD, LLD)

•               Pipeline and code repositories

•               Data models and reporting layer documents

•               Governance and policy documents

•               Test cases and release documentation

Expectations from Teams

•               Availability for walkthroughs and Q&A sessions

•               Timely sharing of artifacts

•               Transparency on risks and issues

Dependencies & Risks

•               Identify access dependencies (tools, environments)

•               Highlight ongoing releases that may impact audit

•               Call out immediate risks impacting timeline

Output Deliverables (from Audit)

•               Executive Summary (for leadership)

•               Detailed Assessment Report

•               Maturity Scorecard

•               Risk & Issue Register

•               Actionable Recommendations & Roadmap