Performance Domain

The Performance domain focuses on measurement, evaluation, and enhancement of alignment between business activities and strategic objectives. It embeds performance metrics directly into the architecture, linking them to capabilities, value streams, products, and services across the enterprise ecosystem.

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

Overview

What is Performance?

Performance represents the quantifiable measurement and evaluation of how effectively an organization delivers value and achieves its strategic objectives. Performance metrics provide insight into the alignment between business activities and desired outcomes, enabling data-driven decision making and continuous improvement.

The domain supports:

• Proactive performance management through predictive analytics
• Democratized access to performance information across roles
• Continuous feedback loops connecting strategy to execution

Purpose and Value

The Performance domain enables architects and planners to:

• Define measurable outcomes with clear KPIs and targets
• Track strategic alignment by linking metrics to objectives
• Enable data-driven decisions through performance dashboards
• Drive continuous improvement by identifying gaps and opportunities
• Support accountability through clear ownership and targets

For Data Engineers

The Performance domain maps directly to analytics and observability:

• Performance Metric → KPI /Metric in your BI tools
• KPIs → Dashboard measures and alerts
• Thresholds → Alerting rules and SLOs
• Data Sources → Source systems for metrics
• Measurement Method → Calculation logic /SQL

Core Components

The Performance domain includes essential elements:

1. Performance KPIs: Specific quantifiable metrics that measure outcomes
2. Performance Framework: Structure for organizing and relating metrics
3. Measurement Systems: Technical capabilities for collecting and processing data
4. Performance Analysis: Analytical capabilities for interpreting data
5. Improvement Actions: Mechanisms for translating insights into improvements

Domain Attributes

Core Attributes

Attribute	Type	Description	Required
title	String	Name or title of the performance metric	✓
description	String	Detailed explanation of what the metric entails	✓
purpose	String	Intended purpose or function of the metric	✓
owner	String	Individual or team responsible for the metric	✓
orgUnitTitle	String	Organization unit(s) to which the metric is linked
performanceCategory	Enum	Broad categorization of performance type
performanceType	Enum	Specific type of performance metric
measurementLevel	Enum	Level at which performance is measured
metricClass	Enum	Classification of metric characteristics
inputs	String	Resources, information required for the metric
outputs	String	Deliverables or results produced
performanceIndicators	String	Current values and trends
dependencies	String	Other metrics, processes, or systems depended on
relatedPerformanceMetrics	String	Related or linked metrics
maturityLevel	Enum	Current maturity level of the metric
toolsAndTechnologies	String	Tools and technologies used
complianceAndStandards	Array[Enum]	Regulatory requirements and standards
cost	String	Financial cost for implementing and maintaining
risks	String	Potential risks associated with the metric
riskCategories	Array[Enum]	Categories of risks
improvementOpportunities	String	Areas for enhancement
strategicAlignment	String	Alignment with strategic goals
measurementMethod	String	How the metric is calculated
dataSource	String	Primary sources of data
frequency	Enum	How often the metric is measured
targets	String	Specific targets, benchmarks, or goals
thresholds	String	Alert thresholds and ranges
automation	Enum	Level of automation in measurement
predictiveCapability	String	Predictive analytics capabilities
businessValue	String	Business value and impact
performanceKPIs	Array[Object]	Specific KPIs supporting the metric

Enumeration Values

Performance Category (performanceCategory)

Value	Description	Example
Strategic Performance	Measures strategic objective achievement	Market share, revenue growth
Operational Performance	Measures operational efficiency	Throughput, cycle time
Financial Performance	Measures financial results	Revenue, profit margin, ROI
Customer Performance	Measures customer experience	Satisfaction, retention, NPS
Process Performance	Measures process effectiveness	Quality rate, error rate
Quality Performance	Measures quality outcomes	Defect rate, compliance
Safety Performance	Measures safety outcomes	Incident rate, compliance
Innovation Performance	Measures innovation outcomes	New products, adoption rates

Performance Type (performanceType)

Value	Description	Example
KPI	Key Performance Indicator	Revenue growth rate
Metric	General measurement	Transaction count
Indicator	Directional measure	Trend indicator
Index	Composite measure	Customer satisfaction index
Ratio	Comparative measure	Cost-to-income ratio
Score	Evaluated measure	Quality score
Rate	Frequency measure	Incident rate
Benchmark	Comparative standard	Industry benchmark
Target	Goal measure	Target achievement
Threshold	Limit measure	Alert threshold

Measurement Level (measurementLevel)

Value	Description	Example
Enterprise	Organization-wide	Company-level KPIs
Division	Business division	Division performance
Department	Departmental level	Department metrics
Process	Process level	Process efficiency
Project	Project level	Project metrics
Individual	Individual level	Personal performance
System	System level	System performance
Product	Product level	Product metrics

Metric Class (metricClass)

Value	Description	Example
Leading Indicator	Predictive of future performance	Pipeline metrics, engagement
Lagging Indicator	Historical performance measures	Revenue, customer retention
Coincident Indicator	Current performance measures	Real-time operational metrics
Predictive Metric	Forward-looking predictions	Forecasts, projections
Diagnostic Metric	Root cause analysis	Problem indicators

Frequency (frequency)

Value	Description	Example
Real-time	Continuous measurement	Live dashboards
Hourly	Measured every hour	Operational monitoring
Daily	Measured daily	Daily reports
Weekly	Measured weekly	Weekly reviews
Monthly	Measured monthly	Monthly reports
Quarterly	Measured quarterly	Quarterly reviews
Annually	Measured annually	Annual assessments
Ad-hoc	Measured as needed	Special analysis

Maturity Level (maturityLevel)

Value	Description	Example
Initial	Ad-hoc, inconsistent	Manual collection
Developing	Basic processes emerging	Some automation
Defined	Standardized processes	Documented methods
Managed	Quantitatively controlled	Statistical analysis
Optimizing	Continuous improvement	Predictive optimization

Automation Level (automation)

Value	Description	Example
Manual	Fully manual collection	Spreadsheet entry
Semi-Automated	Partial automation	Automated collection, manual analysis
Highly Automated	Mostly automated	Automated with exception handling
Fully Automated	End-to-end automation	No human intervention

Performance KPI Elements

Attribute	Type	Description
title	String	Name/title of the KPI
description	String	Detailed explanation
kpiType	Enum	Type: Outcome KPI, Process KPI, Input KPI, Output KPI, Leading KPI, Lagging KPI
priority	Enum	Priority: Critical, High, Medium, Low
frequency	Enum	Measurement frequency
targets	String	Target values
thresholds	String	Alert thresholds
dataSource	String	Data source
calculation	String	Calculation method
owner	String	KPI owner
visualization	String	How KPI is displayed
alerting	String	Alert configuration

Domain Relationships

The Performance domain integrates with other metamodel domains:

Target Domain	Relationship Type	Description
Organization	Ownership	Organization units own and manage performance metrics
Stakeholder	Consumption	Stakeholders consume and act on performance information
Strategy	Measurement	Performance metrics measure strategic objective achievement
Capabilities	Enablement	Performance metrics measure capability effectiveness
Products	Assessment	Performance metrics assess product success and value
Services	Evaluation	Performance metrics evaluate service delivery quality
Information	Utilization	Performance metrics utilize organizational information assets
Initiatives	Tracking	Performance metrics track initiative progress and success
Policy	Compliance	Performance metrics ensure policy compliance
Value Stream	Optimization	Performance metrics optimize value stream performance

Examples

Example 1: Safety Performance Metric

{
  "title": "Track-Related Accident Rate",
  "description": "Measurement of accidents caused by track conditions per million train-miles operated",
  "purpose": "Monitor and reduce track-related accidents through targeted interventions and continuous improvement",
  "owner": "Chief Safety Analysis Officer",
  "orgUnitTitle": "Safety Analysis Division",
  "performanceCategory": "Safety Performance",
  "performanceType": "Rate",
  "measurementLevel": "Enterprise",
  "metricClass": "Lagging Indicator",
  "inputs": "Accident reports, track inspection data, train movement logs, incident investigation reports",
  "outputs": "Monthly safety reports, trend analysis, intervention recommendations, regulatory filings",
  "performanceIndicators": "Current rate: 1.2 accidents per million train-miles, Year-over-year improvement: 15%",
  "frequency": "Monthly",
  "targets": "Target: <1.0 accidents per million train-miles, Industry benchmark: 1.5, Regulatory threshold: 2.5",
  "thresholds": "Green: <1.0, Yellow: 1.0-1.5, Red: >1.5, Emergency: >2.5 per million train-miles",
  "dataSource": "Incident management system, operational data warehouse, FRA reporting system",
  "measurementMethod": "Total track-related accidents divided by total train-miles operated (in millions)",
  "automation": "Highly Automated",
  "maturityLevel": "Optimizing",
  "complianceAndStandards": ["Federal Regulations", "Safety Standards"],
  "businessValue": "Prevented $5M in accident costs annually, 20% reduction in insurance premiums",
  "strategicAlignment": "Direct measure of safety performance and regulatory effectiveness",
  "performanceKPIs": [
    {
      "title": "Monthly Accident Rate Trend",
      "description": "Monthly tracking of accident rate trends and patterns",
      "kpiType": "Lagging KPI",
      "priority": "Critical",
      "frequency": "Monthly",
      "targets": "Consistent monthly improvement toward annual target"
    }
  ]
}

Example 2: Customer Performance Metric

{
  "title": "Customer Satisfaction Score",
  "description": "Comprehensive measurement of customer satisfaction across all service touchpoints",
  "purpose": "Monitor and improve customer experience to drive loyalty, retention, and business growth",
  "owner": "Chief Customer Experience Officer",
  "orgUnitTitle": "Customer Experience Division",
  "performanceCategory": "Customer Performance",
  "performanceType": "Score",
  "measurementLevel": "Enterprise",
  "metricClass": "Lagging Indicator",
  "inputs": "Customer surveys, service interaction data, complaint records, feedback forms",
  "outputs": "Monthly satisfaction reports, customer experience insights, improvement action plans",
  "frequency": "Monthly",
  "targets": "Target: 4.5/5.0, Industry benchmark: 4.2/5.0, Minimum acceptable: 4.0/5.0",
  "dataSource": "Customer survey platform, CRM system, service delivery systems",
  "measurementMethod": "Weighted average of satisfaction scores across all customer touchpoints",
  "automation": "Semi-Automated",
  "maturityLevel": "Managed",
  "businessValue": "15% increase in customer retention, $2M additional revenue from satisfied customers",
  "performanceKPIs": [
    {
      "title": "Service Quality Rating",
      "description": "Customer rating of service delivery quality",
      "kpiType": "Outcome KPI",
      "priority": "High",
      "frequency": "Weekly",
      "targets": "Maintain >4.3/5.0 weekly average"
    }
  ]
}

Example 3: Operational Performance Metric

{
  "title": "On-Time Performance Rate",
  "description": "Percentage of trains arriving at destinations within scheduled timeframes",
  "purpose": "Track and improve operational efficiency to enhance customer service",
  "owner": "Director of Operations",
  "orgUnitTitle": "Operations Division",
  "performanceCategory": "Operational Performance",
  "performanceType": "Rate",
  "measurementLevel": "Enterprise",
  "metricClass": "Coincident Indicator",
  "inputs": "Train scheduling data, actual arrival times, weather data, infrastructure status",
  "outputs": "Daily operations reports, performance dashboards, delay analysis, improvement plans",
  "frequency": "Daily",
  "targets": "Target: 95%, Industry benchmark: 90%, Minimum acceptable: 85%",
  "thresholds": "Green: >92%, Yellow: 85-92%, Red: <85%",
  "dataSource": "Train control systems, GPS tracking, station reporting systems",
  "automation": "Fully Automated",
  "maturityLevel": "Optimizing",
  "predictiveCapability": "Machine learning models predict delays, weather impact analysis, resource optimization",
  "businessValue": "Improved customer satisfaction, reduced operational costs, competitive advantage"
}

Implementation Guidelines

Performance Measurement Best Practices

1. Align to strategy — Ensure every metric links to strategic objectives
2. Balance perspectives — Include leading and lagging indicators
3. Define ownership — Assign clear accountability for each metric
4. Set meaningful targets — Base targets on benchmarks and improvement goals
5. Automate collection — Minimize manual data collection where possible

Balanced Scorecard Approach

OpenMetadata Integration

For Data Platform Teams

When integrating with OpenMetadata, map Performance entities as follows:

Orthogramic Element	OpenMetadata Entity	Notes
Performance Metric	Data Quality Test	For data-related metrics
KPIs	Dashboard Metrics	In BI dashboards
Thresholds	Test Config	Alert thresholds
Data Source	Lineage Source	Where data comes from
Targets	Test Expected Results	Pass/fail criteria

# Example: Create performance monitoring tests
def create_performance_tests(metric):
    """
    Map Orthogramic Performance metric to OpenMetadata data quality test
    """
    tests = []
    
    for kpi in metric.get("performanceKPIs", []):
        test = {
            "name": kpi["title"].lower().replace(" ", "_"),
            "displayName": kpi["title"],
            "description": kpi["description"],
            "testDefinition": {
                "name": "customMetricTest",
                "description": kpi.get("calculation", "Custom calculation")
            },
            "parameterValues": [
                {"name": "target", "value": kpi.get("targets", "")},
                {"name": "threshold", "value": kpi.get("thresholds", "")}
            ],
            "entityLink": f"<#E::table::{metric['dataSource']}>",
            "testSuite": metric["title"].replace(" ", ""),
            "owner": {
                "name": kpi.get("owner", metric["owner"]),
                "type": "user"
            }
        }
        tests.append(test)
    
    return tests

Schema Reference

• Repository: Orthogramic/Orthogramic_Metamodel
• Schema Location: /schemas/performance.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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