Catalog
concept#Data#Architecture#Governance#Integration

Data Architecture

Conceptual organization of data, interfaces, and governance to ensure consistent data quality and efficient data usage across an organization.

Data architecture defines the structural organization, models, and integration principles for data across an organization.
Established
High

Classification

  • High
  • Organizational
  • Architectural
  • Advanced

Technical context

Data warehouse / lakehouseETL/ELT tools and orchestrationAPI gateways and streaming platforms

Principles & goals

Treat data as a productClear responsibilities for data (data ownership)Explicit metadata and lineage maintenance
Build
Enterprise, Domain

Use cases & scenarios

Compromises

  • Silos and conflicting models without enforcement
  • Excessive centralization stifles innovation
  • Governance drag if implemented too bureaucratically
  • Introduce incrementally with clear pilots
  • Define visible metrics and SLAs
  • Treat metadata and catalog maintenance as continuous work

I/O & resources

  • Source systems and schema definitions
  • Business requirements and KPIs
  • Compliance and privacy requirements
  • Target data models and architecture diagrams
  • Data catalog and lineage documentation
  • Governance policies and SLAs

Description

Data architecture defines the structural organization, models, and integration principles for data across an organization. It specifies storage, access patterns, and governance policies as well as interfaces between systems. The goal is consistent data quality, scalability, and efficient data use for analytics, operations, and product features. It also covers security and metadata management.

✔

  • Improved data quality and consistency across systems
  • Better scalability and performance for data applications
  • Faster time-to-insight through clear integration paths

✖

  • High initial effort for modeling and governance
  • Requires cross-departmental alignment
  • Not all legacy systems can be fully harmonized

  • Data quality score

    Metric measuring completeness, accuracy and consistency.

  • Time-to-insight

    Time from data availability to actionable analysis.

  • Data availability / SLA

    Measurement of availability for critical data products and interfaces.

Consolidated data warehouse for e-commerce

Unification of order, customer and logistics data to improve personalization and reporting.

Real-time event architecture in FinTech

Use of event streams for immediate risk analysis and fraud prevention.

Metadata-driven analytics at an insurer

Metadata catalog improves reuse and traceability of data pipelines.

1

Identify stakeholders and define goals

2

Perform as-is analysis of the data landscape

3

Design target architecture, models and governance

4

Pilot implementation and iterative rollout

⚠️ Technical debt & bottlenecks

  • Unclear data ownership in legacy systems
  • Ad-hoc schemas without versioning
  • Fragmented data stores without a central catalog
Data silosLegacy system integrationLack of metadata
  • Rigid central standards ignoring local needs
  • Migration without data quality assurance
  • Incomplete metadata capture causing missing lineage
  • Models too generic to cover operational cases
  • Underestimating change management effort
  • Missing monitoring and alerting design
Data modeling and domain knowledgeData integration and pipeline developmentData governance and compliance understanding
Scalability of storage and queriesData quality and trustworthinessSecurity, privacy and compliance
  • • Existing regulatory requirements
  • • Budget and resource constraints
  • • Technological dependencies on legacy