---
title: Data Analytics and Reporting Training for Staff
---

**SPECIALIZED TRAINING FOR DATA AND ANALYTICS COORDINATORS**

## 1. INTRODUCTION AND SCOPE

### 1.1 Purpose and Strategic Importance

This training provides comprehensive guidance for staff members serving as Data and Analytics Coordinators within our community ecosystem. Data analytics plays a crucial role in understanding community health, measuring progress toward our social justice goals, and making evidence-based decisions that better serve marginalized community members.

### 1.2 Data and Analytics Coordinator Responsibilities

**Primary Areas of Focus:**
- **Community Health Analytics**: Measuring and analysing community engagement, satisfaction, and well-being
- **Equity and Inclusion Metrics**: Tracking progress on diversity, equity, and inclusion goals
- **Platform Performance Analysis**: Analysing the effectiveness and accessibility of community platforms
- **Programme Outcome Measurement**: Evaluating the effectiveness of community programmes and initiatives
- **Predictive Analytics**: Using data to identify trends and potential issues before they become problems
- **Privacy-Compliant Data Management**: Ensuring all data collection and analysis respects user privacy and consent

### 1.3 Integration with Community Values

**Data for Social Justice:**
- **Equity-Centred Analytics**: Using data to identify and address systemic inequalities within the community
- **Community Empowerment**: Making data accessible and actionable for community members
- **Transparency and Accountability**: Using data to hold leadership accountable and demonstrate progress
- **Privacy Protection**: Protecting community member privacy and data rights in all analytics work
- **Inclusive Measurement**: Ensuring data collection and analysis includes and represents marginalized voices

## 2. FUNDAMENTALS OF COMMUNITY DATA ANALYTICS

### 2.1 Understanding Community Data

#### 2.1.1 Types of Community Data

**Engagement and Participation Data:**
- **Platform Activity**: Message volume, participation rates, active users across platforms
- **Event Participation**: Attendance, engagement, and satisfaction at community events
- **Content Interaction**: Views, shares, comments, and engagement with community content
- **Contribution Patterns**: Contributions to projects, discussions, and community initiatives

**Demographic and Identity Data:**
- **Voluntary Identity Disclosure**: Self-reported demographic information when voluntarily provided
- **Geographic Distribution**: General geographic distribution of community members
- **Professional Background**: Career stages, industries, and professional development interests
- **Community Tenure**: Length of community membership and engagement patterns

**Community Health Indicators:**
- **Satisfaction Metrics**: Community member satisfaction with platforms, services, and experiences
- **Retention Rates**: Member retention and factors affecting long-term engagement
- **Support Utilization**: Usage of community support services and resources
- **Conflict and Resolution**: Frequency and types of conflicts, and resolution effectiveness

#### 2.1.2 Data Collection Ethics

**Consent and Transparency:**
- **Informed Consent**: Clear, understandable consent processes for all data collection
- **Purpose Specification**: Clear communication about why data is being collected and how it will be used
- **Opt-Out Options**: Easy opt-out mechanisms for all non-essential data collection
- **Regular Consent Review**: Periodic review and renewal of data collection consent

**Privacy by Design:**
- **Data Minimization**: Collect only the minimum data necessary for stated purposes
- **Anonymization**: Proper anonymization techniques to protect individual identity
- **Secure Storage**: Secure, encrypted storage for all community data
- **Access Controls**: Strict access controls limiting data access to authorized personnel only

### 2.2 Data Quality and Integrity

#### 2.2.1 Data Validation and Cleaning

**Quality Assurance Processes:**
- **Data Validation**: Systematic validation of data accuracy and completeness
- **Error Detection**: Automated and manual processes for detecting data errors and inconsistencies
- **Cleaning Procedures**: Standardized procedures for cleaning and correcting data issues
- **Quality Metrics**: Regular tracking of data quality metrics and improvement goals

**Bias Recognition and Mitigation:**
- **Sampling Bias**: Understanding and correcting for sampling biases in data collection
- **Selection Bias**: Recognizing when certain community members may be over or under-represented
- **Measurement Bias**: Identifying biases in how data is collected and measured
- **Historical Bias**: Understanding how past discrimination may affect current data patterns

#### 2.2.2 Data Integration and Management

**Multi-Platform Data Integration:**
- **Cross-Platform Tracking**: Safely linking data across different community platforms
- **Data Standardization**: Standardizing data formats and definitions across systems
- **Version Control**: Maintaining version control for data sets and analysis methodologies
- **Documentation**: Comprehensive documentation of data sources, processing, and transformations

**Data Lifecycle Management:**
- **Retention Policies**: Clear policies for how long different types of data are retained
- **Deletion Procedures**: Secure deletion of data when retention periods expire
- **Archive Management**: Long-term archiving of important historical data
- **Legal Compliance**: Ensuring data management complies with applicable privacy laws

## 3. EQUITY AND INCLUSION ANALYTICS

### 3.1 Measuring Diversity and Representation

#### 3.1.1 Demographic Analysis Framework

**Inclusive Data Collection:**
- **Optional Self-Identification**: Voluntary, optional demographic self-identification systems
- **Comprehensive Categories**: Inclusive categories that recognize diverse identities
- **Intersectional Analysis**: Analysis that considers multiple, intersecting identities
- **Cultural Sensitivity**: Culturally sensitive approaches to demographic data collection

**Representation Metrics:**
- **Leadership Representation**: Representation of marginalized groups in leadership positions
- **Participation Rates**: Participation rates across different demographic groups
- **Content Creation**: Representation in content creation and community contributions
- **Recognition and Awards**: Representation in community recognition and achievement

#### 3.1.2 Engagement Equity Analysis

**Participation Pattern Analysis:**
- **Platform Usage**: Differences in platform usage across demographic groups
- **Engagement Quality**: Quality and depth of engagement across different groups
- **Barrier Identification**: Data-driven identification of barriers to full participation
- **Access Patterns**: Patterns in access to community resources and opportunities

**Outcome Equity Measurement:**
- **Career Advancement**: Career outcomes for community members from different backgrounds
- **Skill Development**: Skill development and growth opportunities across groups
- **Leadership Pathways**: Success rates in leadership development across demographics
- **Community Impact**: Measurement of community impact and contribution opportunities

### 3.2 Accessibility and Inclusion Metrics

#### 3.2.1 Digital Accessibility Analytics

**Platform Accessibility Measurement:**
- **Accessibility Compliance**: Regular measurement of platform accessibility compliance levels
- **Usage Patterns**: Analysis of how community members with disabilities use platforms
- **Accommodation Requests**: Tracking and analysis of accessibility accommodation requests
- **Assistive Technology**: Analysis of assistive technology usage and compatibility

**Accessibility Impact Assessment:**
- **Feature Accessibility**: Assessment of new feature accessibility before launch
- **Content Accessibility**: Regular assessment of content accessibility across platforms
- **User Experience**: Analysis of user experience for community members with disabilities
- **Barrier Documentation**: Documentation and tracking of accessibility barriers and resolutions

#### 3.2.2 Inclusion Experience Metrics

**Psychological Safety Measurement:**
- **Belonging Surveys**: Regular surveys measuring sense of belonging across different groups
- **Safety Indicators**: Metrics indicating psychological safety for marginalized community members
- **Microaggression Tracking**: Careful, privacy-conscious tracking of microaggression reports
- **Support System Effectiveness**: Analysis of support system usage and effectiveness

**Cultural Inclusion Assessment:**
- **Cultural Representation**: Analysis of cultural representation in community content and leadership
- **Language Access**: Assessment of language accessibility and multilingual support effectiveness
- **Cultural Events**: Participation and satisfaction with culturally diverse events and programming
- **Cross-Cultural Interaction**: Analysis of positive cross-cultural interactions and learning

## 4. COMMUNITY HEALTH AND ENGAGEMENT ANALYTICS

### 4.1 Community Vitality Metrics

#### 4.1.1 Engagement and Activity Analysis

**Quantitative Engagement Metrics:**
- **Active User Statistics**: Daily, weekly, and monthly active users across platforms
- **Content Creation**: Volume and quality of user-generated content and contributions
- **Interaction Patterns**: Patterns of interaction, collaboration, and mutual support
- **Event Participation**: Attendance and engagement at community events and activities

**Qualitative Engagement Assessment:**
- **Engagement Quality**: Depth and meaningfulness of community interactions
- **Community Connections**: Formation of lasting relationships and professional networks
- **Skill Sharing**: Instances of knowledge sharing and mutual learning
- **Collaborative Projects**: Success and participation in collaborative community projects

#### 4.1.2 Community Satisfaction and Well-being

**Satisfaction Measurement:**
- **Regular Satisfaction Surveys**: Comprehensive satisfaction surveys across different community segments
- **Net Promoter Score**: Community member likelihood to recommend the community to others
- **Platform-Specific Satisfaction**: Satisfaction with different community platforms and services
- **Support Service Satisfaction**: Satisfaction with community support and assistance services

**Well-being Indicators:**
- **Stress and Burnout**: Monitoring indicators of community member stress and burnout
- **Mental Health Support**: Usage and effectiveness of mental health resources and support
- **Work-Life Balance**: Community impact on members' work-life balance and well-being
- **Growth and Development**: Member perception of personal and professional growth opportunities

### 4.2 Predictive Analytics and Early Warning Systems

#### 4.2.1 Trend Analysis and Forecasting

**Participation Trend Analysis:**
- **Engagement Trends**: Long-term trends in community engagement and participation
- **Seasonal Patterns**: Understanding seasonal patterns in community activity and needs
- **Growth Projections**: Data-driven projections for community growth and resource needs
- **Platform Evolution**: Analysis of how platform usage evolves over time

**Risk Identification:**
- **Disengagement Patterns**: Early identification of patterns that predict member disengagement
- **Conflict Prediction**: Using data to identify potential sources of community conflict
- **Resource Strain**: Predicting when community resources may become strained
- **Leadership Gaps**: Identifying potential leadership succession needs and gaps

#### 4.2.2 Intervention Effectiveness

**Programme Impact Measurement:**
- **Initiative Effectiveness**: Measuring the impact of specific community initiatives and programmes
- **A/B Testing**: Controlled testing of different approaches to community improvement
- **Intervention Outcomes**: Analysis of outcomes from community interventions and changes
- **Long-term Impact**: Assessment of long-term impact of community programmes and policies

**Continuous Improvement Analytics:**
- **Feedback Loop Analysis**: Analysis of feedback loops and their effectiveness in driving improvement
- **Change Impact**: Measuring the impact of policy and procedural changes on community health
- **Innovation Adoption**: Analysis of how new initiatives and innovations are adopted by the community
- **Best Practice Identification**: Data-driven identification of best practices and successful approaches

## 5. PRIVACY-COMPLIANT DATA PRACTICES

### 5.1 Legal and Ethical Framework

#### 5.1.1 Privacy Law Compliance

**GDPR Compliance:**
- **Lawful Basis**: Ensuring lawful basis for all data processing activities
- **Data Subject Rights**: Supporting data subject rights including access, rectification, and erasure
- **Privacy by Design**: Implementing privacy by design principles in all analytics systems
- **Data Protection Impact Assessments**: Conducting DPIAs for high-risk data processing activities

**Other Privacy Regulations:**
- **CCPA Compliance**: California Consumer Privacy Act compliance for California residents
- **PIPEDA Compliance**: Personal Information Protection and Electronic Documents Act compliance for Canadians
- **Sector-Specific Regulations**: Compliance with any sector-specific privacy regulations
- **Cross-Border Transfer**: Appropriate safeguards for international data transfers

#### 5.1.2 Ethical Data Use

**Community Data Ethics:**
- **Community Benefit**: Ensuring data use benefits the community and its members
- **Harm Prevention**: Proactive measures to prevent data use from causing harm
- **Power Imbalance**: Recognition and mitigation of power imbalances in data collection and use
- **Community Consent**: Ongoing community consent and engagement in data use decisions

**Algorithmic Fairness:**
- **Bias Detection**: Regular testing for bias in analytical algorithms and models
- **Fairness Metrics**: Implementation of fairness metrics in all automated decision-making
- **Transparency**: Transparency in how algorithms and data analysis affect community members
- **Accountability**: Clear accountability mechanisms for algorithmic decision-making

### 5.2 Data Security and Protection

#### 5.2.1 Technical Security Measures

**Data Encryption:**
- **Encryption at Rest**: All stored data encrypted using strong encryption standards
- **Encryption in Transit**: All data transfers encrypted using secure protocols
- **Key Management**: Secure key management systems and procedures
- **Access Encryption**: Encrypted access to data analysis tools and platforms

**Access Controls:**
- **Role-Based Access**: Role-based access controls limiting data access to necessary personnel
- **Multi-Factor Authentication**: Multi-factor authentication for all data access
- **Audit Logging**: Comprehensive audit logging of all data access and usage
- **Regular Access Review**: Regular review and updating of data access permissions

#### 5.2.2 Data Anonymization and De-identification

**Anonymization Techniques:**
- **Direct Identifier Removal**: Removal of direct personal identifiers from datasets
- **Quasi-Identifier Treatment**: Appropriate treatment of quasi-identifiers to prevent re-identification
- **K-Anonymity**: Implementation of k-anonymity and other privacy-preserving techniques
- **Differential Privacy**: Use of differential privacy techniques when appropriate

**De-identification Validation:**
- **Re-identification Risk Assessment**: Regular assessment of re-identification risks
- **Expert Review**: Expert review of anonymization procedures and effectiveness
- **Ongoing Monitoring**: Ongoing monitoring for potential re-identification risks
- **Policy Compliance**: Ensuring de-identification procedures comply with applicable privacy standards

## 6. TECHNICAL TOOLS AND PLATFORMS

### 6.1 Data Analytics Infrastructure

#### 6.1.1 Data Collection and Storage

**Data Collection Systems:**
- **Automated Collection**: Automated collection systems with built-in privacy controls
- **Survey Platforms**: Privacy-compliant survey platforms for community feedback collection
- **API Integration**: Secure API integration for cross-platform data collection
- **Real-time Analytics**: Real-time data collection and analysis capabilities

**Data Storage Solutions:**
- **Cloud Security**: Secure cloud storage solutions with appropriate data residency
- **Database Management**: Secure database management with encryption and access controls
- **Backup and Recovery**: Comprehensive backup and disaster recovery procedures
- **Version Control**: Version control for datasets and analysis procedures

#### 6.1.2 Analysis and Visualization Tools

**Statistical Analysis Software:**
- **R and Python**: Advanced statistical analysis using R, Python, and relevant libraries
- **SQL Databases**: Proficient use of SQL for database queries and analysis
- **Machine Learning**: Application of appropriate machine learning techniques for community insights
- **Time Series Analysis**: Analysis of trends and patterns over time

**Visualization and Reporting:**
- **Dashboard Development**: Development of accessible, interactive dashboards for community insights
- **Data Visualization**: Creation of clear, compelling data visualizations
- **Report Generation**: Automated generation of regular reports and analyses
- **Accessible Design**: Ensuring all visualizations and reports meet accessibility standards

### 6.2 Platform Integration and Automation

#### 6.2.1 Multi-Platform Data Integration

**Cross-Platform Analytics:**
- **Discord Analytics**: Integration with Discord for community server analytics
- **Forum Analytics**: Analysis of forum participation and engagement patterns
- **Social Media Analytics**: Integration with social media platforms for broader community insights
- **Event Analytics**: Integration of event participation and satisfaction data

**API Management:**
- **Secure API Access**: Secure access to platform APIs for data collection
- **Rate Limiting**: Appropriate rate limiting and usage controls for API access
- **Error Handling**: Robust error handling and data quality assurance in API integrations
- **Documentation**: Comprehensive documentation of all API integrations and data flows

#### 6.2.2 Automation and Efficiency

**Automated Reporting:**
- **Scheduled Reports**: Automated generation and distribution of regular reports
- **Alert Systems**: Automated alert systems for significant changes or issues
- **Data Pipeline Automation**: Automated data processing and analysis pipelines
- **Quality Assurance**: Automated quality assurance checks in data processing

**Workflow Optimization:**
- **Process Automation**: Automation of routine data processing and analysis tasks
- **Template Development**: Development of templates and frameworks for common analyses
- **Collaboration Tools**: Tools for collaborating on data analysis with other staff members
- **Documentation Automation**: Automated documentation of data processing and analysis procedures

## 7. REPORTING AND COMMUNICATION

### 7.1 Data-Driven Storytelling

#### 7.1.1 Effective Data Communication

**Audience-Appropriate Communication:**
- **Executive Summaries**: Clear, concise summaries for leadership decision-making
- **Community Reports**: Accessible reports for community members about community health and progress
- **Technical Documentation**: Detailed technical documentation for other data professionals
- **Visual Communication**: Effective use of charts, graphs, and infographics to communicate insights

**Narrative Development:**
- **Story Structure**: Using narrative structure to make data insights compelling and memorable
- **Context Provision**: Providing appropriate context for data insights and recommendations
- **Action Orientation**: Framing data insights in terms of actionable recommendations
- **Impact Emphasis**: Emphasizing the human impact and community benefits of data insights

#### 7.1.2 Accessibility and Inclusion in Reporting

**Accessible Report Design:**
- **Visual Accessibility**: Ensuring all charts and visualizations meet accessibility standards
- **Plain Language**: Using plain language that is accessible to diverse audiences
- **Multiple Formats**: Providing reports in multiple formats (text, audio, large print)
- **Cultural Sensitivity**: Ensuring reports are culturally sensitive and inclusive

**Community-Centred Reporting:**
- **Community Input**: Including community input in determining what data to report and how
- **Marginalized Voice Amplification**: Using data to amplify the voices and needs of marginalized community members
- **Deficit vs. Asset Framing**: Framing data to highlight community assets rather than just deficits
- **Actionable Insights**: Providing insights that the community can act on for improvement

### 7.2 Transparency and Accountability

#### 7.2.1 Public Reporting

**Regular Public Reports:**
- **Annual Community Reports**: Comprehensive annual reports on community health and progress
- **Quarterly Updates**: Quarterly updates on key community metrics and initiatives
- **Special Reports**: Special reports on significant issues or changes affecting the community
- **Crisis Communication**: Transparent communication during crises or significant challenges

**Data Transparency:**
- **Methodology Disclosure**: Clear disclosure of data collection and analysis methodologies
- **Limitation Acknowledgment**: Honest acknowledgment of data limitations and potential biases
- **Source Documentation**: Comprehensive documentation of data sources and collection methods
- **Privacy Protection**: Transparency balanced with appropriate privacy protection

#### 7.2.2 Stakeholder Engagement

**Community Engagement:**
- **Feedback Solicitation**: Regular solicitation of community feedback on data collection and reporting
- **Data Literacy**: Supporting community member data literacy and understanding
- **Participatory Analysis**: Engaging community members in data analysis and interpretation
- **Co-Creation**: Co-creating reports and analyses with community members

**Leadership Engagement:**
- **Strategic Planning Support**: Using data to support strategic planning and decision-making
- **Performance Monitoring**: Regular monitoring and reporting on organizational performance
- **Goal Setting**: Data-driven goal setting and progress tracking
- **Resource Allocation**: Supporting resource allocation decisions with data insights

## 8. PROFESSIONAL DEVELOPMENT AND ETHICS

### 8.1 Technical Competency Development

#### 8.1.1 Statistical and Analytical Skills

**Foundation Skills:**
- **Statistical Methods**: Proficiency in statistical methods appropriate for community data analysis
- **Data Visualization**: Skills in creating effective, accessible data visualizations
- **Research Design**: Understanding of research design and methodology for community studies
- **Survey Design**: Skills in designing effective, unbiased surveys and data collection instruments

**Advanced Techniques:**
- **Machine Learning**: Application of machine learning techniques for community insights
- **Predictive Modeling**: Development of predictive models for community planning and improvement
- **Text Analysis**: Analysis of text data from community discussions and feedback
- **Network Analysis**: Analysis of community networks and relationship patterns

#### 8.1.2 Technology and Tools

**Programming and Database Skills:**
- **Python/R Proficiency**: Advanced proficiency in Python or R for data analysis
- **SQL Skills**: Advanced SQL skills for database queries and analysis
- **Cloud Platforms**: Experience with cloud-based analytics platforms and tools
- **API Integration**: Skills in integrating with various APIs for data collection

**Specialized Tools:**
- **Business Intelligence**: Experience with business intelligence and dashboard tools
- **Survey Platforms**: Proficiency with survey platforms and data collection tools
- **Version Control**: Use of version control systems for code and analysis documentation
- **Collaboration Tools**: Effective use of collaboration tools for team data analysis

### 8.2 Ethical Practice and Professional Standards

#### 8.2.1 Professional Ethics

**Data Ethics Framework:**
- **Harm Prevention**: Proactive measures to prevent data use from causing harm to community members
- **Consent and Autonomy**: Respecting community member autonomy and informed consent
- **Fairness and Justice**: Using data analysis to promote fairness and social justice
- **Transparency and Honesty**: Maintaining transparency and honesty in all data work

**Professional Standards:**
- **Accuracy and Integrity**: Maintaining the highest standards of accuracy and integrity in data analysis
- **Confidentiality**: Protecting confidential information and respecting privacy
- **Competence**: Maintaining and developing professional competence in data analysis
- **Objectivity**: Maintaining objectivity while acknowledging the value-driven nature of community work

#### 8.2.2 Continuous Learning and Development

**Ongoing Education:**
- **Industry Trends**: Staying current with trends in data analytics and community measurement
- **Privacy Developments**: Keeping up with developments in privacy law and ethical data use
- **Community Research**: Engaging with research on community development and social justice
- **Technical Skills**: Continuously developing technical skills and capabilities

**Professional Networks:**
- **Data Professional Communities**: Participation in data professional communities and organizations
- **Academic Partnerships**: Partnerships with academic researchers studying community development
- **Conference Participation**: Regular participation in relevant conferences and professional development
- **Peer Learning**: Engaging in peer learning and knowledge sharing with other data professionals

## 9. CERTIFICATION AND COMPETENCY REQUIREMENTS

### 9.1 Data and Analytics Coordinator Competencies

#### 9.1.1 Essential Skills Assessment

**Technical Proficiency:**
- **Statistical Analysis**: Demonstrated proficiency in statistical analysis methods and interpretation
- **Data Management**: Skills in secure, privacy-compliant data management and storage
- **Visualization and Reporting**: Ability to create accessible, compelling data visualizations and reports
- **Privacy Compliance**: Understanding of privacy laws and ethical data use practices

**Community Focus:**
- **Social Justice Analytics**: Understanding of how to use data to advance social justice goals
- **Community Engagement**: Skills in engaging community members in data collection and analysis
- **Cultural Competency**: Understanding of how cultural factors affect data collection and interpretation
- **Equity Measurement**: Ability to design and implement metrics for measuring equity and inclusion

#### 9.1.2 Advanced Competencies

**Strategic Analysis:**
- **Predictive Analytics**: Ability to develop and use predictive models for community planning
- **Impact Measurement**: Skills in measuring the impact of community programmes and initiatives
- **Trend Analysis**: Advanced skills in identifying and analysing long-term community trends
- **Research Design**: Ability to design and implement research studies for community insight

**Leadership and Communication:**
- **Data Storytelling**: Exceptional skills in communicating data insights to diverse audiences
- **Training and Education**: Ability to train others in data literacy and analysis
- **Policy Development**: Skills in using data to inform policy development and decision-making
- **Community Advocacy**: Using data to advocate for community needs and social justice

### 9.2 Training and Performance Standards

#### 9.2.1 Certification Requirements

**Technical Certifications:**
- **Relevant Professional Certifications**: Appropriate professional certifications in data analysis or research
- **Privacy and Ethics Training**: Certification in data privacy and ethical research practices
- **Statistical Software Proficiency**: Demonstrated proficiency in relevant statistical software and programming languages
- **Platform-Specific Training**: Training on community-specific platforms and data sources

**Community-Specific Training:**
- **Community Values Integration**: Training on integrating community values into data analysis work
- **Cultural Competency**: Training on culturally responsive research and data analysis methods
- **Social Justice Analytics**: Training on using data analysis to advance social justice goals
- **Community Engagement**: Training on engaging community members as partners in research and analysis

#### 9.2.2 Performance Evaluation

**Effectiveness Measures:**
- **Analysis Quality**: High quality, accurate, and insightful data analysis
- **Community Impact**: Demonstrable positive impact on community decision-making and improvement
- **Privacy Compliance**: Consistent adherence to privacy laws and ethical data use standards
- **Communication Effectiveness**: Effective communication of data insights to diverse stakeholders

**Professional Development:**
- **Continuous Learning**: Ongoing professional development and skill enhancement
- **Innovation**: Development of innovative approaches to community data analysis
- **Collaboration**: Effective collaboration with community members and other staff
- **Leadership**: Leadership in promoting data literacy and evidence-based decision-making

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*This Data Analytics and Reporting Training document is part of our comprehensive staff development program designed to ensure ethical, privacy-compliant, and socially just approaches to community data analytics. For questions about data analytics practices or to report training completion, please contact the Data and Analytics Coordinator or designated leadership through staff channels.*

**Document Version**: 1.0
**Last Updated**: 25 September 2025
**Next Review Date**: 25 March 2026