Data Architecture Strategy & Governance
Data architecture defines how information flows, resolves, and governs across the enterprise. Build strategy for MDM, analytics, AI readiness, and regulatory compliance.
Executive Summary. Data architecture ensures information assets support decisions, operations, and compliance with clear ownership, lineage, and quality standards. As analytics, AI, and regulatory scrutiny intensify, ad hoc data pipelines and tribal knowledge become enterprise liabilities. This guide covers conceptual, logical, and physical data architecture layers, master data management, governance councils, and platform strategy. Larkinized LLC helps CIOs and CDOs align data investments with capability priorities while avoiding duplicate lakes, conflicting golden records, and audit findings from undocumented flows.
Layers of Data Architecture
Conceptual data architecture defines business entities and relationships independent of systems—customer, product, agreement, location.
Logical architecture adds attributes, keys, normalization rules, and domain boundaries aligned to capabilities.
Physical architecture maps to databases, lakes, warehouses, streams, APIs, and retention technologies.
Traceability across layers supports impact analysis when systems change or regulations emerge.
Data Governance Operating Model
Data governance councils set policies; data stewards execute within domains; data custodians operate platforms. RACI clarity prevents “everyone’s data, nobody’s job.”
Policies cover classification, privacy, retention, quality SLAs, and access provisioning.
Escalation paths resolve cross-domain disputes—customer data spanning sales, service, and finance.
Connect governance to architecture ARB for projects impacting authoritative sources.
Data Governance RACI
Council sets policy, domain stewards approve definitions, custodians implement controls, architects ensure project alignment.
Master Data Management Strategy
MDM identifies golden records for core entities with match/merge, hierarchy management, and synchronization to consuming systems.
Choose consolidation, coexistence, or registry styles based on ERP centrality and acquisition complexity.
MDM fails without business ownership of data quality KPIs and funded remediation workflows.
Start with one domain—customer or product—before enterprise hub ambitions.
Analytics and Data Platform Architecture
Modern platforms blend warehouse, lakehouse, streaming, and BI layers with governed access. Avoid duplicate lakes per BU without federation strategy.
Define zone architecture: raw, curated, published with promotion criteria and ownership.
Self-service analytics requires certified datasets and data literacy programs—not open lake dumping.
Finops for data platforms tracks storage, compute, and query cost by domain.
Lineage, Catalog, and Metadata
Data catalogs (Collibra, Alation, Purview, Informatica) document assets, lineage, and business glossary linkage.
Automated lineage from ETL/ELT tools reduces manual diagrams; critical paths need validation.
Business glossary terms map to logical entities for consistent reporting definitions.
Catalog adoption metrics mirror EA repository—usage beats completeness scores.
Quality, Observability, and SLAs
Define quality dimensions: completeness, accuracy, timeliness, consistency. Measure at domain level with thresholds triggering remediation tickets.
Data observability tools monitor pipeline failures and schema drift proactively.
SLAs align to capability KPIs—inventory accuracy for supply chain, balance accuracy for finance.
Quality debt backlog prioritized like technical debt with executive visibility.
Privacy, Security, and Regulatory Architecture
Embed privacy by design: purpose limitation, consent, minimization, cross-border transfer controls in architecture standards.
Map regulations (GDPR, HIPAA, SOX, BCBS 239) to data classes and required controls.
Tokenization, masking, and encryption standards vary by classification tier.
Architecture supports audit evidence: who accessed what, when, and lawful basis.
AI and Advanced Analytics Readiness
AI initiatives require trusted features, bias monitoring, and model lineage tied to source data architecture.
Establish feature stores and model registries integrated with governance policies.
Risk reviews for high-impact models include architecture sign-off on data suitability.
Avoid AI on polluted data—architecture remediation precedes model scale.
Integration with Enterprise Architecture
Data architecture inputs application integration design and vice versa. API payloads reflect logical entity definitions.
Retire applications only after data migration and archival architecture approved.
Joint roadmap prioritization prevents data platform build ahead of source stabilization.
Larkinized LLC facilitates CDO-CIO-EA alignment workshops.
Implementation Roadmap
Phase 1: Governance charter, glossary, top five entities, catalog MVP. Phase 2: MDM pilot, quality SLAs, lineage automation. Phase 3: Federated analytics zones, AI governance integration.
Contact Larkinized LLC for data architecture assessments and operating model design.
Key Takeaways
- Data architecture spans conceptual, logical, and physical layers.
- Governance requires council, stewards, custodians, and clear RACI.
- MDM succeeds with domain focus and business-owned quality KPIs.
- Platform strategy avoids duplicate lakes without federation.
- Catalogs and lineage enable trust, audits, and impact analysis.
- Quality and observability SLAs tie data to business outcomes.
- Privacy and regulatory controls are embedded in standards.
- AI readiness depends on governed, lineage-tracked data assets.
Need Expert Guidance?
Larkinized LLC helps organizations design, govern, and execute enterprise architecture programs that deliver measurable business outcomes.