People Domain

The People domain represents human aspects including workforce structure, roles, skills, competencies, and organizational culture. It provides a structured approach for modeling the human capital that enables organizational capabilities and drives business outcomes.

Schema Version: 2.1
Schema Location: /schemas/people.schema.json
Specification: JSON Schema Draft-07

Overview

What is the People Domain?

The People domain captures the human dimension of business architecture, connecting workforce elements to organizational capabilities and strategic objectives. The domain covers:

• Roles — Defined positions with responsibilities and authority
• Skills — Competencies and expertise needed for capability delivery
• Workforce Structure — Organization of teams and reporting relationships
• Culture — Values, behaviors, and ways of working
• Talent Management — Development, retention, and succession

The domain recognizes that people are the ultimate enablers of organizational capability—technology and process alone cannot deliver value without skilled, engaged workforce.

Purpose and Value

The People domain enables architects and planners to:

• Map skills to capabilities — Understand workforce requirements for capability delivery
• Plan workforce development — Align training and hiring with strategic needs
• Identify skill gaps — Highlight areas needing investment or recruitment
• Support transformation — Plan people aspects of organizational change
• Enable workforce planning — Connect headcount to business outcomes

For Data Engineers

The People domain maps directly to data team concepts:

• Role → Data Engineer, Analytics Engineer, Data Scientist
• Skills → SQL, Python, dbt, Spark, ML
• Workforce Structure → Data Team, Analytics Team
• Competency Level → Junior, Mid, Senior, Staff, Principal
• Talent Pipeline → Hiring plans, career paths

Core Components

The People domain uses a hierarchical structure:

1. Roles: Defined positions with responsibilities, skills, and authority
2. Skills & Competencies: Required knowledge, abilities, and expertise
3. Workforce Structure: Organization of people into teams and units
4. Culture: Shared values, behaviors, and working norms

Domain Attributes

Core Attributes

Attribute	Type	Description	Required
title	String	Name or title of the people element	✓
description	String	Detailed explanation	✓
purpose	String	Intended purpose within the Organization	✓
owner	String	Individual or team responsible	✓
orgUnitTitle	String	Organization unit(s) linked
peopleCategory	Enum	Category of people element
peopleType	Enum	Specific type
headcount	Object	Current and planned headcount
demographics	Object	Workforce demographics
skillProfile	Array[Object]	Skills and competency requirements
roleDefinitions	Array[Object]	Role specifications
teamStructure	Object	Team organization and hierarchy
culturalAttributes	Object	Cultural values and behaviors
performanceIndicators	String	Metrics for workforce effectiveness
talentPipeline	Object	Hiring and development pipeline
trainingPrograms	Array[Object]	Training and development programs
successionPlanning	Object	Succession planning for key roles
engagementMetrics	Object	Employee engagement measures
retentionMetrics	Object	Retention and turnover data
dependencies	String	Dependencies on other domains
risks	String	Workforce-related risks
riskCategories	Array[Enum]	Categories of risks
improvementOpportunities	String	Areas for enhancement
strategicAlignment	String	Alignment with strategic goals

Enumeration Values

People Category (peopleCategory)

Value	Description	Example
Workforce	General workforce elements	Team composition
Role	Position definitions	Job roles
Skill	Competency definitions	Technical skills
Culture	Cultural elements	Values, behaviors
Talent	Talent management	Pipelines, succession
Leadership	Leadership elements	Leadership roles

People Type (peopleType)

Value	Description	Example
Individual Role	Single position	Data Engineer
Role Family	Related roles	Engineering family
Team	Working group	Analytics Team
Department	Organizational unit	Data Division
Skill Category	Skill grouping	Technical skills
Competency Framework	Competency model	Engineering ladder
Cultural Value	Core value	Innovation
Behavioral Norm	Expected behavior	Collaboration

Competency Level (competencyLevel)

Value	Description	Example
Foundational	Basic understanding	Entry level
Developing	Growing proficiency	Junior level
Proficient	Solid capability	Mid level
Advanced	Deep expertise	Senior level
Expert	Industry-leading	Staff/Principal
Master	Thought leadership	Distinguished

Employment Type (employmentType)

Value	Description	Example
Full-Time	Full-time employee	Regular staff
Part-Time	Part-time employee	Reduced hours
Contract	Fixed-term contract	Project-based
Consultant	External consultant	Advisory
Vendor	Vendor staff	Outsourced
Intern	Internship	Training program

Risk Categories (riskCategories)

Value	Description	Example
Talent Risk	Difficulty attracting/retaining	Skill shortage
Succession Risk	Key person dependency	Single points of failure
Skill Gap Risk	Missing capabilities	Technology shifts
Engagement Risk	Low engagement	Cultural issues
Compliance Risk	Labor compliance	Regulatory issues
Capacity Risk	Insufficient headcount	Resource constraints

Role Definition Elements

Attribute	Type	Description
roleTitle	String	Title of the role
roleDescription	String	Detailed description
roleCategory	Enum	Category: Technical, Management, Executive, Specialist, Support
roleLevel	Enum	Level: Entry, Mid, Senior, Lead, Manager, Director, Executive
responsibilities	Array[String]	Key responsibilities
requiredSkills	Array[Object]	Skills needed with proficiency levels
qualifications	Array[String]	Required qualifications
experience	String	Experience requirements
reportingTo	String	Reporting relationship
directReports	Integer	Number of direct reports
capabilities	Array[String]	Capabilities this role enables
compensation	Object	Compensation range
careerPath	Object	Career progression options

Skill Definition Elements

Attribute	Type	Description
skillTitle	String	Name of the skill
skillDescription	String	Detailed description
skillCategory	Enum	Category: Technical, Business, Leadership, Interpersonal
skillType	Enum	Type: Hard Skill, Soft Skill, Domain Knowledge
proficiencyLevels	Array[Object]	Proficiency level definitions
assessmentMethod	String	How skill is assessed
developmentPath	Array[Object]	How to develop the skill
relatedSkills	Array[String]	Related skills
marketDemand	Enum	Market demand: High, Medium, Low
strategicImportance	Enum	Strategic importance

Domain Relationships

The People domain integrates with other metamodel domains:

Target Domain	Relationship Type	Description
Organization	Structure	People organized within org units
Capabilities	Enablement	People enable capability delivery
Strategy	Alignment	Workforce aligned to strategic goals
Technology	Usage	People use and manage technology
Services	Delivery	People deliver services
Performance	Measurement	Workforce performance tracked
Initiatives	Participation	People participate in initiatives
Policy	Compliance	People comply with policies
Stakeholder	Overlap	Workforce are internal stakeholders
Information	Knowledge	People hold organizational knowledge

Examples

Example 1: Data Engineering Role

{
  "title": "Data Engineering Team",
  "description": "Team responsible for building and maintaining data infrastructure, pipelines, and platform capabilities",
  "purpose": "Enable data-driven decision making through reliable, scalable data infrastructure",
  "owner": "Head of Data Engineering",
  "orgUnitTitle": "Data and Analytics Division",
  "peopleCategory": "Workforce",
  "peopleType": "Team",
  "headcount": {
    "current": 15,
    "planned": 20,
    "planningHorizon": "2025",
    "breakdown": {
      "fullTime": 12,
      "contract": 3
    }
  },
  "skillProfile": [
    {
      "skillTitle": "SQL",
      "proficiencyRequired": "Advanced",
      "coverage": "100%"
    },
    {
      "skillTitle": "Python",
      "proficiencyRequired": "Proficient",
      "coverage": "90%"
    },
    {
      "skillTitle": "Spark",
      "proficiencyRequired": "Proficient",
      "coverage": "60%"
    },
    {
      "skillTitle": "dbt",
      "proficiencyRequired": "Advanced",
      "coverage": "80%"
    },
    {
      "skillTitle": "Cloud Platforms (AWS/Azure)",
      "proficiencyRequired": "Proficient",
      "coverage": "85%"
    }
  ],
  "roleDefinitions": [
    {
      "roleTitle": "Senior Data Engineer",
      "roleLevel": "Senior",
      "roleCategory": "Technical",
      "headcount": 6,
      "responsibilities": [
        "Design and implement data pipelines",
        "Mentor junior engineers",
        "Lead technical initiatives",
        "Ensure data quality and reliability"
      ],
      "requiredSkills": [
        {"skill": "SQL", "level": "Expert"},
        {"skill": "Python", "level": "Advanced"},
        {"skill": "Data Modeling", "level": "Advanced"}
      ],
      "experience": "5+ years in data engineering",
      "capabilities": ["Data Integration", "Data Pipeline Development", "Data Quality"]
    },
    {
      "roleTitle": "Data Engineer",
      "roleLevel": "Mid",
      "roleCategory": "Technical",
      "headcount": 6,
      "responsibilities": [
        "Build and maintain data pipelines",
        "Implement data quality checks",
        "Support analytics teams"
      ],
      "experience": "2-5 years in data engineering"
    },
    {
      "roleTitle": "Junior Data Engineer",
      "roleLevel": "Entry",
      "roleCategory": "Technical",
      "headcount": 3,
      "responsibilities": [
        "Assist in pipeline development",
        "Monitor pipeline execution",
        "Learn from senior engineers"
      ],
      "experience": "0-2 years"
    }
  ],
  "teamStructure": {
    "manager": "Head of Data Engineering",
    "techLead": "Principal Data Engineer",
    "teamSize": 15,
    "subTeams": [
      {"name": "Platform Team", "size": 5, "focus": "Infrastructure"},
      {"name": "Integration Team", "size": 6, "focus": "Data Pipelines"},
      {"name": "Quality Team", "size": 4, "focus": "Data Quality"}
    ]
  },
  "performanceIndicators": "Pipeline reliability: 99.5%, Data freshness SLA: 98%, Team velocity: 85 points/sprint",
  "talentPipeline": {
    "openPositions": 5,
    "hiringTimeline": "Q2 2025",
    "sourcingChannels": ["LinkedIn", "Referrals", "Tech Events"],
    "interviewProcess": "Technical screen, coding challenge, system design, culture fit"
  },
  "trainingPrograms": [
    {
      "programTitle": "Cloud Certification Program",
      "description": "AWS/Azure certification for all engineers",
      "participants": 15,
      "duration": "3 months"
    },
    {
      "programTitle": "Data Modeling Excellence",
      "description": "Advanced data modeling techniques",
      "participants": 10,
      "duration": "2 weeks"
    }
  ],
  "engagementMetrics": {
    "engagementScore": 4.2,
    "maxScore": 5,
    "lastSurvey": "Q4 2024"
  },
  "retentionMetrics": {
    "turnoverRate": "8%",
    "averageTenure": "3.2 years",
    "regrettableLosses": 1
  },
  "risks": "Talent competition for data engineering skills, key person dependency on principal engineer",
  "riskCategories": ["Talent Risk", "Succession Risk"],
  "strategicAlignment": "Core enabler for data-driven strategy and digital transformation"
}

Example 2: Competency Framework

{
  "title": "Data & Analytics Competency Framework",
  "description": "Comprehensive framework defining skills and competencies for data and analytics roles",
  "purpose": "Enable consistent skill assessment, career development, and hiring across data teams",
  "owner": "Chief Data Officer",
  "orgUnitTitle": "Data and Analytics Division",
  "peopleCategory": "Skill",
  "peopleType": "Competency Framework",
  "skillProfile": [
    {
      "skillTitle": "Data Engineering",
      "skillCategory": "Technical",
      "skillType": "Hard Skill",
      "proficiencyLevels": [
        {
          "level": "Foundational",
          "description": "Understands data pipeline concepts, can write basic SQL",
          "indicators": ["Write simple queries", "Understand ETL concepts"]
        },
        {
          "level": "Proficient",
          "description": "Can build production pipelines independently",
          "indicators": ["Design data models", "Implement complex transformations"]
        },
        {
          "level": "Advanced",
          "description": "Architects scalable data solutions",
          "indicators": ["Design distributed systems", "Optimize performance"]
        },
        {
          "level": "Expert",
          "description": "Industry-recognized expertise, sets direction",
          "indicators": ["Define standards", "Mentor others", "Innovation"]
        }
      ],
      "assessmentMethod": "Technical assessment, code review, project evaluation",
      "developmentPath": [
        {"method": "Training", "resource": "Data Engineering Bootcamp"},
        {"method": "Certification", "resource": "Cloud certifications"},
        {"method": "Project", "resource": "Lead a pipeline project"}
      ],
      "marketDemand": "High",
      "strategicImportance": "Critical"
    },
    {
      "skillTitle": "Data Analysis",
      "skillCategory": "Business",
      "skillType": "Hard Skill",
      "marketDemand": "High",
      "strategicImportance": "High"
    },
    {
      "skillTitle": "Communication",
      "skillCategory": "Interpersonal",
      "skillType": "Soft Skill",
      "marketDemand": "Medium",
      "strategicImportance": "High"
    }
  ],
  "strategicAlignment": "Supports talent strategy for building world-class data organization"
}

Example 3: Cultural Values

{
  "title": "Data-Driven Culture Initiative",
  "description": "Cultural transformation to embed data-driven decision making across the organization",
  "purpose": "Create culture where decisions are informed by data and evidence",
  "owner": "Chief Data Officer",
  "orgUnitTitle": "Enterprise",
  "peopleCategory": "Culture",
  "peopleType": "Cultural Value",
  "culturalAttributes": {
    "coreValues": [
      {
        "value": "Data-Driven Decision Making",
        "description": "Base decisions on data and evidence, not just intuition",
        "behaviors": [
          "Ask 'what does the data say?'",
          "Challenge assumptions with facts",
          "Measure outcomes"
        ]
      },
      {
        "value": "Data Quality Ownership",
        "description": "Everyone is responsible for data quality",
        "behaviors": [
          "Report data issues immediately",
          "Validate data before using",
          "Document data definitions"
        ]
      }
    ],
    "leadershipBehaviors": [
      "Role model data-driven decisions",
      "Invest in data literacy",
      "Celebrate data-driven wins"
    ],
    "adoptionMetrics": {
      "dataLiteracyScore": 3.8,
      "dashboardAdoption": "72%",
      "selfServiceUsage": "45%"
    }
  },
  "performanceIndicators": "Data literacy score: 3.8/5, Self-service adoption: 45%",
  "strategicAlignment": "Foundation for digital transformation and operational excellence"
}

Implementation Guidelines

Workforce Planning Best Practices

1. Align to capabilities — Map workforce to capability requirements
2. Assess skill gaps — Regularly evaluate skills vs. needs
3. Plan for the future — Anticipate skill needs from strategy
4. Develop talent — Invest in training and career growth
5. Manage succession — Plan for key role transitions

Skills-Capability Mapping

OpenMetadata Integration

For Data Platform Teams

When integrating with OpenMetadata, map People entities as follows:

Orthogramic Element	OpenMetadata Entity	Notes
Role	User Role	Role-based access
Team	Team	Team ownership
Person	User	Individual user
Skills	Custom Property	Skill tags
Competency Level	Custom Property	Level tagging

# Example: Create team in OpenMetadata
def create_data_team(people_element):
    """
    Map Orthogramic People element to OpenMetadata Team
    """
    return {
        "name": people_element["title"].lower().replace(" ", "_"),
        "displayName": people_element["title"],
        "description": people_element["description"],
        "teamType": "Department",
        "owner": {"name": people_element["owner"], "type": "user"},
        "users": [
            {"name": role["roleTitle"].lower().replace(" ", "_")}
            for role in people_element.get("roleDefinitions", [])
        ],
        "children": [
            {"name": sub["name"].lower().replace(" ", "_")}
            for sub in people_element.get("teamStructure", {}).get("subTeams", [])
        ],
        "defaultRoles": ["DataConsumer"],
        "policies": ["DataAccessPolicy"],
        "isJoinable": True
    }

Schema Reference

• Repository: Orthogramic/Orthogramic_Metamodel
• Schema Location: /schemas/people.schema.json
• Version: 2.1
• Specification: JSON Schema Draft-07
• License: Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA 4.0)

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