Information Domain

The Information domain encompasses data assets, knowledge resources, and information flows that enable decision-making and value creation across the enterprise. This domain integrates information assets into business architecture, connecting them with capabilities, processes, stakeholders, and strategic objectives.

Schema Version: 2.2
Schema Location: /schemas/information.schema.json
Specification: JSON Schema Draft-07

Overview

What is Information?

Information represents any data, knowledge, or intellectual asset that provides value to the organization through decision support, operational efficiency, compliance, or competitive advantage. Information can range from:

• Structured databases — Relational data, transactional records
• Analytical reports — Business intelligence, dashboards
• Unstructured documents — Procedures, contracts, knowledge articles
• Real-time streams — IoT sensor data, event streams
• Tacit knowledge — Organizational expertise and insights

Information is fundamentally about knowledge power—how to capture it, organize it, access it, analyze it, and leverage it for strategic advantage.

Purpose and Value

The Information domain enables architects and planners to:

• Catalog information assets with consistent structure and metadata
• Define data governance through policies, ownership, and quality rules
• Map data lineage by documenting sources, transformations, and consumers
• Ensure compliance by linking information to regulatory requirements
• Support analytics by connecting information to business outcomes

For Data Engineers

The Information domain maps directly to your data catalog:

• Information Asset → Data Asset /Dataset
• Information Category → Domain /Collection
• Data Classification → Sensitivity tier
• Data Quality → Quality score /dimensions
• Information Elements → Tables, fields, schemas
• Lineage → Data pipeline flow

Core Components

The Information domain includes essential elements that work together:

1. Information Elements: Specific data structures, datasets, or knowledge components
2. Information Architecture: Technical and logical structure of how information is organized
3. Data Governance: Policies, procedures, and controls for quality, security, and compliance
4. Information Flow: Processes through which information moves across the organization
5. Knowledge Management: Capabilities for capturing and sharing organizational knowledge

Domain Attributes

Core Attributes

Attribute	Type	Description	Required
title	String	Name or title of the Information asset	✓
description	String	Detailed explanation of what the asset contains	✓
purpose	String	Intended purpose or function within the Organization	✓
owner	String	Individual or team responsible for the Information asset	✓
orgUnitTitle	String	Organization unit(s) to which the asset is linked
informationCategory	Enum	Broad categorization of information type
informationType	Enum	Specific type of information
dataClassification	Enum	Security and sensitivity classification
dataQuality	Enum	Quality level of the information
format	String	Technical format or structure
volume	String	Size or scale of the information asset
updateFrequency	Enum	How often the information is updated
retention	String	Data retention policies and schedules
sources	String	Origin systems or processes that generate the information
consumers	String	Systems, processes, or users that utilize the information
dependencies	String	Other information assets or systems this depends on
interfaces	String	Technical interfaces and access methods
governance	String	Data governance policies and procedures
compliance	Array[Enum]	Regulatory and compliance requirements
security	String	Security measures and access controls
privacy	String	Privacy protection measures and policies
quality	String	Data quality measures and monitoring
lineage	String	Data lineage and transformation history
metadata	String	Descriptive information about the data
lifecycle	Enum	Current stage in information lifecycle
businessValue	String	Business value and impact of the information
costs	String	Costs associated with maintaining the asset
risks	String	Potential risks associated with the information
riskCategories	Array[Enum]	Categories of risks
improvementOpportunities	String	Areas for enhancement
strategicAlignment	String	Alignment with organizational goals
informationElements	Array[Object]	Specific elements within the information asset

Enumeration Values

Information Category (informationCategory)

Value	Description	Example
Operational Data	Day-to-day transactional data	Transaction records, operational logs
Analytical Data	Data prepared for analysis	Data warehouse, analytics marts
Reference Data	Standard codes and lookups	Country codes, product categories
Master Data	Core business entity data	Customer master, product master
Metadata	Data about data	Data dictionaries, schemas
Content	Unstructured content	Documents, images, videos
Knowledge	Organizational knowledge	Procedures, best practices
Intelligence	Strategic insights	Market research, competitive analysis

Information Type (informationType)

Value	Description	Example
Database	Structured database systems	PostgreSQL, Oracle, SQL Server
Dataset	Curated data collections	Analytics datasets, training data
Report	Analytical reports	Business reports, dashboards
Document	Text documents	Procedures, contracts, policies
Knowledge Base	Organized knowledge repository	Wiki, knowledge articles
API Data	Data accessed via APIs	REST endpoints, GraphQL
Real-time Stream	Live data streams	Event streams, sensor feeds
Archive	Historical archived data	Compliance archives, backups
Model	Analytical models	ML models, statistical models
Dashboard	Interactive visualizations	BI dashboards, monitoring views

Data Classification (dataClassification)

Value	Description	Example
Public	Can be freely shared	Product descriptions, company news
Internal	For internal organizational use	Employee directories, procedures
Confidential	Sensitive, requires protection	Customer data, financial details
Restricted	Highly sensitive, limited access	PII, payment data, trade secrets
Top Secret	Highest classification level	Strategic plans, M&A data

Data Quality (dataQuality)

Value	Description	Example
High Quality	Excellent quality, well-governed	Master data with validation
Good Quality	Above average quality	Curated analytical data
Acceptable Quality	Meets minimum standards	Operational data with known issues
Poor Quality	Below standards, needs improvement	Legacy data, ungoverned sources
Unknown Quality	Quality not assessed	New or ungoverned data

Update Frequency (updateFrequency)

Value	Description	Example
Real-time	Continuous updates	Event streams, live feeds
Hourly	Updated every hour	Near real-time analytics
Daily	Updated daily	Daily batch loads
Weekly	Updated weekly	Weekly reports
Monthly	Updated monthly	Monthly aggregates
Quarterly	Updated quarterly	Quarterly reports
Annually	Updated annually	Annual summaries
Ad-hoc	Updated as needed	On-demand refreshes
Static	Not regularly updated	Reference data, archives

Information Lifecycle (lifecycle)

Value	Description	Example
Creation	Initial data creation	New data capture
Active Use	Actively used in operations	Production data
Maintenance	Being maintained and updated	Active master data
Archive	Archived for retention	Historical records
Disposal	Being disposed or deleted	End of retention
Migration	Being migrated to new systems	System transitions

Compliance Requirements (compliance)

Value	Description
GDPR	EU General Data Protection Regulation
HIPAA	US Health Insurance Portability and Accountability
SOX	US Sarbanes-Oxley Act
PCI DSS	Payment Card Industry Data Security Standard
Industry Standards	Industry-specific standards
Internal Policies	Organization-specific policies

Information Element Components

Attribute	Type	Description
title	String	Name/title of the element
description	String	Detailed explanation
elementType	Enum	Type: Table, Field, File, Stream, Object, Document, Image, Report Section
dataType	String	Technical data type
constraints	String	Data constraints and rules
businessRules	String	Business rules applied
sensitivity	Enum	Sensitivity level: Public, Internal, Confidential, Restricted
quality	String	Element-specific quality measures
source	String	Source of the element
transformation	String	Any transformations applied
performance	String	Performance characteristics
monitoring	String	Monitoring and alerting
documentation	String	Documentation and metadata

Domain Relationships

The Information domain integrates with other metamodel domains:

Target Domain	Relationship Type	Description
Organization	Ownership	Organization units own and manage information assets
Stakeholder	Consumption	Stakeholders consume and interact with information
Capabilities	Enablement	Information enables organizational capabilities
Strategy	Support	Information supports strategic objectives
Products	Integration	Information is integrated into products and services
Services	Utilization	Services consume and generate information
Performance	Measurement	Information provides metrics for performance monitoring
Initiatives	Development	Information assets developed through programs and projects
Policy	Governance	Information governed by organizational policies
Value Stream	Flow	Information flows through value streams

Examples

Example 1: Operational Database

{
  "title": "Customer Transaction Database",
  "description": "Comprehensive database containing all customer transaction records, payment details, and purchase history for operational and analytical purposes",
  "purpose": "Support real-time transaction processing and enable customer analytics and business intelligence",
  "owner": "Chief Data Officer",
  "orgUnitTitle": "Data and Analytics Division",
  "informationCategory": "Operational Data",
  "informationType": "Database",
  "dataClassification": "Confidential",
  "dataQuality": "High Quality",
  "format": "PostgreSQL relational database with JSON fields for flexible attributes",
  "volume": "2.5TB database with 50M transaction records, growing by 100K records daily",
  "updateFrequency": "Real-time",
  "retention": "Active transactions for 7 years, archived for additional 10 years",
  "sources": "POS systems, e-commerce platform, mobile app, customer service systems",
  "consumers": "Analytics platform, fraud detection system, customer service tools, executive dashboards",
  "compliance": ["GDPR", "PCI DSS", "Internal Policies"],
  "security": "AES-256 encryption, role-based access control, audit logging, network isolation",
  "privacy": "Data masking in non-production, anonymization for analytics, consent management",
  "quality": "99.8% accuracy with real-time validation, automated quality monitoring",
  "lineage": "Source → ETL → Data warehouse → Analytics mart → Reports",
  "businessValue": "Enables $3M annual revenue optimization through customer insights and fraud prevention",
  "informationElements": [
    {
      "title": "Transaction Amount",
      "description": "Monetary value of individual customer transactions",
      "elementType": "Field",
      "dataType": "DECIMAL(10,2)",
      "constraints": "NOT NULL, positive value only",
      "sensitivity": "Confidential"
    }
  ]
}

Example 2: Business Intelligence Repository

{
  "title": "Market Intelligence Repository",
  "description": "Centralized repository of market research, competitive intelligence, industry trends, and strategic analysis",
  "purpose": "Provide comprehensive market insights to support strategic planning and competitive positioning",
  "owner": "Director of Strategic Intelligence",
  "orgUnitTitle": "Strategy and Planning Division",
  "informationCategory": "Intelligence",
  "informationType": "Knowledge Base",
  "dataClassification": "Confidential",
  "dataQuality": "Good Quality",
  "format": "Mixed format repository: documents, spreadsheets, databases, web content",
  "volume": "500GB of documents, reports, and datasets updated weekly",
  "updateFrequency": "Weekly",
  "retention": "Current intelligence for 3 years, archived for historical reference",
  "sources": "Market research firms, industry reports, competitive analysis, internal research",
  "consumers": "Executive team, product managers, strategic planning team, business development",
  "compliance": ["Internal Policies", "Industry Standards"],
  "security": "Document-level access controls, watermarking, secure sharing protocols",
  "quality": "Expert review process, source verification, regular content updates",
  "businessValue": "Supports strategic decisions worth $10M+ annually in market opportunities",
  "strategicAlignment": "Directly supports market expansion and competitive strategy initiatives",
  "informationElements": [
    {
      "title": "Competitive Analysis Reports",
      "description": "Detailed analysis of competitor strategies, strengths, and market positioning",
      "elementType": "Document",
      "sensitivity": "Restricted",
      "businessRules": "Quarterly updates required, executive approval for sharing"
    }
  ]
}

Example 3: Real-time Data Stream

{
  "title": "IoT Sensor Data Stream",
  "description": "Real-time data stream from IoT sensors monitoring infrastructure conditions, equipment performance, and environmental factors",
  "purpose": "Enable predictive maintenance, real-time monitoring, and operational optimization",
  "owner": "IoT Platform Manager",
  "orgUnitTitle": "Technology Operations Division",
  "informationCategory": "Operational Data",
  "informationType": "Real-time Stream",
  "dataClassification": "Internal",
  "dataQuality": "High Quality",
  "format": "Apache Kafka streams with Avro schema",
  "volume": "10M events per day, 500GB daily storage",
  "updateFrequency": "Real-time",
  "sources": "Temperature sensors, vibration monitors, GPS trackers, environmental sensors",
  "consumers": "Predictive maintenance system, operations dashboard, alerting platform",
  "interfaces": "Kafka Consumer API, REST API for historical queries",
  "quality": "99.9% data completeness, automated anomaly detection"
}

Implementation Guidelines

Information Modeling Best Practices

1. Start with business context — Understand how information supports business outcomes
2. Define clear ownership — Assign accountable owners for each information asset
3. Classify consistently — Apply classification standards across all assets
4. Document lineage — Track data from source to consumption
5. Measure quality — Establish and monitor quality metrics

Data Governance Framework

OpenMetadata Integration

For Data Platform Teams

The Information domain maps directly to OpenMetadata entities:

Orthogramic Element	OpenMetadata Entity	Notes
Information Asset	Table /Topic /Dashboard	Core data asset
Information Category	Domain	Business domain grouping
Data Classification	Tier	Sensitivity classification
Data Quality	Test Suite	Quality test results
Information Element	Column /Field	Schema elements
Lineage	Lineage	Data flow tracking
Owner	Owner	Ownership assignment
Compliance	Tag /Classification	Compliance tags

# Example: Map Orthogramic Information to OpenMetadata Table
def create_om_table(info_asset):
    """
    Map Orthogramic Information asset to OpenMetadata Table entity
    """
    return {
        "name": info_asset["title"].lower().replace(" ", "_"),
        "displayName": info_asset["title"],
        "description": info_asset["description"],
        "tableType": "Regular",
        "columns": [
            {
                "name": elem["title"].lower().replace(" ", "_"),
                "displayName": elem["title"],
                "dataType": elem.get("dataType", "STRING"),
                "description": elem["description"],
                "tags": [
                    {"tagFQN": f"Sensitivity.{elem.get('sensitivity', 'Internal')}"}
                ]
            }
            for elem in info_asset.get("informationElements", [])
        ],
        "owner": {
            "name": info_asset["owner"],
            "type": "user"
        },
        "tags": [
            {"tagFQN": f"DataClassification.{info_asset['dataClassification']}"},
            {"tagFQN": f"InformationCategory.{info_asset['informationCategory']}"}
        ],
        "extension": {
            "updateFrequency": info_asset.get("updateFrequency"),
            "dataQuality": info_asset.get("dataQuality"),
            "businessValue": info_asset.get("businessValue"),
            "retention": info_asset.get("retention")
        }
    }

Schema Reference

• Repository: Orthogramic/Orthogramic_Metamodel
• Schema Location: /schemas/information.schema.json
• Version: 2.2
• Specification: JSON Schema Draft-07
• License: Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA 4.0)

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