DAT: Data Models
Document Type
Code: DAT
Name: Data Models
Domain: Technology & Data
Abstract
This specification defines the Data Models document type within the BSpec 1.0 Universal Business Specification Standard. It establishes normative requirements, structured templates, and implementation guidance for organizations documenting data models within the technology-data domain. This specification enables systematic, machine-readable documentation that supports strategic planning, operational execution, and organizational alignment.
Purpose and Scope
The Data Models document defines systematic approaches to designing, governing, and managing data structures that support business capabilities through coherent data architecture, quality assurance, and strategic data management. It establishes data frameworks that ensure consistency, integrity, and value creation from organizational data assets.
Document Metadata Schema
yaml
---
id: DAT-{data-domain}
title: "Data Model — {Data Domain or Entity Name}"
type: DAT
status: Draft|Review|Approved|Active|Deprecated
version: 1.0.0
owner: Data-Team|Data-Architect
stakeholders: [data-team, development-team, business-stakeholders, data-governance]
domain: technology
priority: Critical|High|Medium|Low
scope: data-management
horizon: strategic
visibility: internal
depends_on: [SYS-*, ARC-*, REQ-*, GOV-*]
enables: [API-*, ANA-*, QUA-*, INT-*]
data_classification: Public|Internal|Confidential|Restricted
data_domain: Core|Supporting|Reference|Analytical
model_type: Conceptual|Logical|Physical|Dimensional
governance_level: Basic|Managed|Governed|Optimized
success_criteria:
- "Data model supports business requirements effectively"
- "Data quality meets defined standards and SLAs"
- "Data governance ensures compliance and control"
- "Data integration enables seamless data flow"
assumptions:
- "Business data requirements are clearly defined"
- "Data governance framework is established"
- "Technical infrastructure supports data model"
constraints: [Regulatory and technology constraints]
standards: [Data management and governance standards]
review_cycle: quarterly
---Content Structure Template
markdown
# Data Model — {Data Domain or Entity Name}
## Executive Summary
**Purpose:** {Brief description of data model purpose and scope}
**Domain:** {Business domain or bounded context}
**Data Classification:** {Public|Internal|Confidential|Restricted}
**Stakeholders:** {Primary stakeholders and data consumers}
## Data Architecture
### Conceptual Model
- **Business Entities:** {Key business entities and concepts}
- **Entity Relationships:** {High-level relationships between entities}
- **Business Rules:** {Business rules governing data}
- **Data Lifecycle:** {How data is created, used, and retired}
### Logical Model
```yaml
entities:
primary:
- entity: {Entity name}
description: {Business entity description}
attributes: [attribute1, attribute2, attribute3]
relationships: [relationship1, relationship2]
supporting:
- entity: {Supporting entity}
description: {Entity description}
purpose: {Why this entity exists}
```
### Physical Model
- **Storage Technology:** {Database technology and platform}
- **Schema Design:** {Physical schema structure}
- **Indexing Strategy:** {Performance optimization through indexing}
- **Partitioning:** {Data partitioning and sharding approach}
## Entity Specifications
### Core Entities
```yaml
entity_definitions:
{EntityName}:
description: {Business entity description}
attributes:
- name: {attribute_name}
type: {data_type}
nullable: {true|false}
description: {Attribute description}
business_rules: [rule1, rule2]
relationships:
- target: {Related entity}
type: {One-to-One|One-to-Many|Many-to-Many}
description: {Relationship description}
constraints:
- type: {Primary Key|Foreign Key|Unique|Check}
description: {Constraint description}
```
### Relationship Matrix
| Source Entity | Target Entity | Relationship Type | Business Rule | Cardinality |
|---------------|---------------|-------------------|---------------|-------------|
| {Entity A} | {Entity B} | {Relationship} | {Business rule} | {1:1, 1:M, M:M} |
## Data Governance
### Data Ownership
```yaml
data_ownership:
data_steward: {Primary data steward}
data_owner: {Business data owner}
technical_contact: {Technical contact}
responsibilities:
quality: {Who ensures data quality}
privacy: {Who manages privacy compliance}
access: {Who controls data access}
```
### Data Quality Framework
- **Quality Dimensions:** {Accuracy, completeness, consistency, timeliness}
- **Quality Rules:** {Specific data quality validation rules}
- **Quality Monitoring:** {How data quality is measured and monitored}
- **Quality Remediation:** {Process for addressing quality issues}
### Data Privacy and Security
```yaml
privacy_controls:
classification: {Data classification level}
pii_handling: {Personal data handling procedures}
retention: {Data retention policies}
access_controls: {Who can access what data}
compliance:
gdpr: {GDPR compliance measures}
ccpa: {CCPA compliance measures}
industry: {Industry-specific compliance}
```
## Data Integration
### Source Systems
```yaml
data_sources:
primary:
- system: {Source system name}
type: {OLTP|OLAP|File|API}
frequency: {Real-time|Batch|Scheduled}
quality: {Data quality assessment}
secondary:
- system: {Secondary source}
purpose: {Enrichment|Validation|Backup}
integration: {Integration method}
```
### Data Flow
- **Ingestion Patterns:** {How data enters the system}
- **Transformation Rules:** {Data transformation and cleansing}
- **Distribution Patterns:** {How data is distributed to consumers}
- **Synchronization:** {How data consistency is maintained}
### Master Data Management
- **Golden Records:** {Master data definition and management}
- **Data Deduplication:** {Duplicate detection and resolution}
- **Data Lineage:** {Tracking data origin and transformations}
- **Reference Data:** {Shared reference data management}
## Data Storage and Performance
### Storage Strategy
```yaml
storage_design:
database_type: {Relational|NoSQL|Graph|Time-series}
storage_engine: {Engine choice and rationale}
data_distribution: {Sharding|Replication strategy}
performance_optimization:
indexing: [index1, index2, index3]
partitioning: {Partitioning strategy}
caching: {Caching approach}
```
### Data Archival
- **Archival Strategy:** {When and how data is archived}
- **Retention Policies:** {Data retention rules and compliance}
- **Retrieval Procedures:** {How to access archived data}
- **Disposal Procedures:** {Secure data disposal}
## Analytics and Reporting
### Analytical Model
```yaml
analytics:
fact_tables:
- table: {Fact table name}
granularity: {Level of detail}
measures: [measure1, measure2]
dimension_tables:
- table: {Dimension table name}
attributes: [attr1, attr2]
hierarchy: {Dimensional hierarchy}
```
### Reporting Requirements
- **Standard Reports:** {Regular business reports}
- **Ad-hoc Analysis:** {Self-service analytics capabilities}
- **Real-time Dashboards:** {Live monitoring and dashboards}
- **Data Export:** {Data export and integration capabilities}
## Data Operations
### Data Lifecycle Management
```yaml
lifecycle:
creation:
process: {How data is created}
validation: {Creation validation rules}
maintenance:
updates: {Update procedures and governance}
quality_checks: {Ongoing quality monitoring}
retirement:
criteria: {When data is retired}
process: {Retirement procedures}
```
### Backup and Recovery
- **Backup Strategy:** {Frequency and scope of backups}
- **Recovery Procedures:** {Data recovery processes}
- **Recovery Objectives:** {RTO and RPO targets}
- **Testing:** {Backup and recovery testing}
### Change Management
- **Schema Evolution:** {How schema changes are managed}
- **Version Control:** {Data model versioning}
- **Impact Analysis:** {Change impact assessment}
- **Migration Procedures:** {Data migration processes}
## Monitoring and Metrics
### Data Quality Metrics
```yaml
quality_metrics:
accuracy:
- metric: {Accuracy measure}
target: {Target percentage}
measurement: {How measured}
completeness:
- metric: {Completeness measure}
target: {Target percentage}
critical_fields: [field1, field2]
```
### Performance Metrics
- **Query Performance:** {Query response time monitoring}
- **Storage Utilization:** {Storage capacity and growth monitoring}
- **Data Freshness:** {Data currency and staleness tracking}
- **Usage Analytics:** {Data access and usage patterns}
## Compliance and Audit
### Regulatory Compliance
- **Data Regulations:** {Applicable data regulations}
- **Compliance Controls:** {Controls to ensure compliance}
- **Audit Trail:** {Data access and modification logging}
- **Compliance Reporting:** {Required compliance reports}
### Data Audit
```yaml
audit_framework:
access_logging:
scope: {What access is logged}
retention: {Log retention period}
change_tracking:
schema_changes: {Schema change tracking}
data_changes: {Data modification tracking}
compliance_monitoring:
controls: [control1, control2]
reporting: {Compliance reporting schedule}
```
## Validation
*Evidence that data model supports business requirements, maintains quality, and enables value creation*Quality Standards
Bronze Level (Minimum Viable)
- [ ] Basic data model with core entities and relationships
- [ ] Simple data governance and ownership definition
- [ ] Basic data quality and security measures
- [ ] All required metadata fields completed
Silver Level (Investment Ready)
- [ ] Comprehensive logical and physical data model
- [ ] Detailed data governance framework with quality controls
- [ ] Structured data integration and lifecycle management
- [ ] Privacy and compliance framework
Gold Level (Operational Excellence)
- [ ] Advanced data architecture with optimization strategies
- [ ] Sophisticated data governance with automated quality monitoring
- [ ] Comprehensive analytics and self-service capabilities
- [ ] Strategic data management with predictive governance
Common Pitfalls
- Lack of data governance: Poor data ownership and accountability
- Inconsistent data models: Different representations of same business concepts
- Poor data quality: Inadequate data validation and quality controls
- Siloed data design: Data models that don't consider integration needs
Success Patterns
- Domain-driven data design: Data models aligned with business domains with clear bounded contexts and ownership
- Data as a product: Data treated as product with defined consumers and quality SLAs
- Automated data governance: Automated quality monitoring with self-service capabilities and governance guardrails
- Lineage transparency: Clear data lineage and provenance with impact analysis capabilities
Relationship Guidelines
Typical Dependencies
- SYS (Systems): System requirements drive data model design and integration needs
- ARC (Architecture): Architecture design informs data storage and integration patterns
- REQ (Requirements): Business requirements drive entity design and data governance
- GOV (Governance): Governance framework drives data policies and compliance controls
Typical Enablements
- API (APIs): Data models enable API design and data exchange patterns
- ANA (Analytics): Data models enable analytical capabilities and reporting
- QUA (Quality Specification): Data quality standards drive overall quality outcomes
- INT (Integration): Data models enable system integration and data sharing
Document Relationships
This document type commonly relates to:
- Depends on: SYS (Systems), ARC (Architecture), REQ (Requirements), GOV (Governance)
- Enables: API (APIs), ANA (Analytics), QUA (Quality Specification), INT (Integration)
- Informs: Database design, integration strategy, analytics requirements
- Guides: Data governance, quality management, compliance procedures
Validation Checklist
- [ ] Executive summary with clear purpose, domain, classification, and stakeholders
- [ ] Data architecture with conceptual, logical, and physical models
- [ ] Entity specifications with core entities and relationship matrix
- [ ] Data governance with ownership, quality framework, and privacy controls
- [ ] Data integration with source systems, data flow, and master data management
- [ ] Data storage and performance with strategy, optimization, and archival procedures
- [ ] Analytics and reporting with analytical model and reporting requirements
- [ ] Data operations with lifecycle management, backup, and change management
- [ ] Monitoring and metrics with quality and performance measurement
- [ ] Compliance and audit with regulatory compliance and audit framework
- [ ] Validation evidence of data model effectiveness, quality achievement, and business value