ANA: Analytics

Document Type

Code: ANA
Name: Analytics
Domain: Technology & Data

Abstract

This specification defines the Analytics document type within the BSpec 1.0 Universal Business Specification Standard. It establishes normative requirements, structured templates, and implementation guidance for organizations documenting analytics 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 Analytics document defines systematic approaches to designing, implementing, and managing analytics capabilities that enable data-driven decision making through business intelligence, advanced analytics, and strategic insights. It establishes analytics frameworks that ensure business value, data quality, and user adoption.

Document Metadata Schema

---
id: ANA-{analytics-domain}
title: "Analytics — {Analytics Domain or Platform}"
type: ANA
status: Draft|Review|Approved|Active|Deprecated
version: 1.0.0
owner: Analytics-Team|Data-Scientist
stakeholders: [analytics-team, data-team, business-stakeholders, users]
domain: technology
priority: Critical|High|Medium|Low
scope: analytics-platform
horizon: strategic
visibility: internal

depends_on: [DAT-*, SYS-*, INF-*, MET-*]
enables: [PER-*, QUA-*, GOV-*, STR-*]

analytics_maturity: Descriptive|Diagnostic|Predictive|Prescriptive
platform_type: Self-service|Enterprise|Embedded|Cloud-native
user_community: Business-users|Analysts|Data-scientists|Executives
technology_stack: Traditional|Modern|Hybrid

success_criteria:
  - "Analytics platform delivers actionable business insights"
  - "Analytics capabilities drive data-driven decision making"
  - "Analytics platform meets performance and usability requirements"
  - "Analytics investment generates measurable business value"

assumptions:
  - "Business analytics requirements are clearly defined"
  - "Data infrastructure supports analytics workloads"
  - "User community is engaged and trained for analytics adoption"

constraints: [Data and technology constraints]
standards: [Analytics and data standards]
review_cycle: quarterly
---

Content Structure Template

# Analytics — {Analytics Domain or Platform}

## Executive Summary
**Purpose:** {Brief description of analytics scope and objectives}
**Business Impact:** {How analytics drives business value}
**Technology Platform:** {Analytics technology stack}
**User Community:** {Analytics users and stakeholders}

## Analytics Strategy

### Business Objectives
- **Strategic Goals:** {How analytics supports business strategy}
- **Decision Making:** {Analytics-driven decision making}
- **Competitive Advantage:** {Analytics as competitive differentiator}
- **Value Creation:** {Business value creation through analytics}

### Analytics Maturity
    ```yaml
    maturity_assessment:
      current_state:
        descriptive: {Historical reporting and KPIs}
        diagnostic: {Root cause analysis capabilities}
        predictive: {Forecasting and prediction models}
        prescriptive: {Optimization and recommendation engines}
    
      target_state:
        vision: {Analytics vision and aspirations}
        capabilities: [capability1, capability2, capability3]
        timeline: {Maturity improvement timeline}
    ```

### Use Case Portfolio
- **Operational Analytics:** {Day-to-day operational insights}
- **Strategic Analytics:** {Long-term strategic analysis}
- **Customer Analytics:** {Customer behavior and experience analytics}
- **Product Analytics:** {Product performance and optimization}
- **Financial Analytics:** {Financial performance and forecasting}

## Data Architecture

### Data Sources
    ```yaml
    data_sources:
      internal_systems:
        - system: {Source system name}
          type: {OLTP|OLAP|File|Stream}
          frequency: {Real-time|Batch|Scheduled}
          volume: {Data volume characteristics}
          quality: {Data quality assessment}
    
      external_sources:
        - source: {External data provider}
          type: {API|File|Stream}
          purpose: {Data enrichment purpose}
          cost: {Data acquisition cost}
    ```

### Data Pipeline Architecture
- **Data Ingestion:** {Data collection and ingestion patterns}
- **Data Processing:** {ETL/ELT processing framework}
- **Data Storage:** {Data warehouse and lake architecture}
- **Data Quality:** {Data quality monitoring and remediation}

### Data Models
    ```yaml
    data_models:
      dimensional_model:
        fact_tables: [fact1, fact2, fact3]
        dimension_tables: [dim1, dim2, dim3]
        granularity: {Lowest level of detail}
    
      data_vault:
        hubs: [hub1, hub2]
        links: [link1, link2]
        satellites: [sat1, sat2]
    
      data_lake:
        raw_zone: {Raw data storage}
        curated_zone: {Processed data storage}
        consumption_zone: {Analytics-ready data}
    ```

## Analytics Platform

### Technology Stack
    ```yaml
    technology_platform:
      data_platform:
        data_warehouse: {Snowflake|Redshift|BigQuery|Synapse}
        data_lake: {S3|ADLS|GCS}
        processing_engine: [Spark, Databricks, Dataflow]
    
      analytics_tools:
        business_intelligence: [PowerBI, Tableau, Looker]
        self_service: [tool1, tool2]
        advanced_analytics: [Python, R, SAS]
    
      infrastructure:
        cloud_platform: {AWS|Azure|GCP}
        orchestration: [Airflow, Data Factory]
        monitoring: [tool1, tool2]
    ```

### Platform Capabilities
- **Data Integration:** {Data integration and connectivity}
- **Data Transformation:** {Data processing and transformation}
- **Analytics Workbench:** {Data science and analytics environment}
- **Visualization:** {Data visualization and dashboarding}
- **Collaboration:** {Analytics collaboration and sharing}

### Scalability and Performance
    ```yaml
    performance:
      scalability:
        compute_scaling: {Horizontal and vertical scaling}
        storage_scaling: {Storage capacity management}
        query_optimization: {Query performance optimization}
    
      performance_monitoring:
        query_performance: {Query response time monitoring}
        resource_utilization: {Resource usage tracking}
        user_experience: {User experience monitoring}
    ```

## Analytics Capabilities

### Descriptive Analytics
    ```yaml
    descriptive_analytics:
      reporting:
        operational_reports: [report1, report2]
        executive_dashboards: [dashboard1, dashboard2]
        kpi_monitoring: [kpi1, kpi2]
    
      data_exploration:
        ad_hoc_analysis: {Self-service analytics capabilities}
        data_discovery: {Data exploration tools}
        visual_analytics: {Interactive visualization}
    ```

### Diagnostic Analytics
- **Root Cause Analysis:** {Drill-down and drill-through capabilities}
- **Variance Analysis:** {Performance variance investigation}
- **Correlation Analysis:** {Statistical correlation and relationship analysis}
- **Comparative Analysis:** {Period-over-period and segment comparison}

### Predictive Analytics
    ```yaml
    predictive_analytics:
      forecasting:
        demand_forecasting: {Demand prediction models}
        financial_forecasting: {Financial projection models}
        trend_analysis: {Trend identification and prediction}
    
      machine_learning:
        classification: [use_case1, use_case2]
        regression: [use_case1, use_case2]
        clustering: [use_case1, use_case2]
    
      model_management:
        model_development: {ML model development process}
        model_deployment: {Production model deployment}
        model_monitoring: {Model performance monitoring}
    ```

### Prescriptive Analytics
- **Optimization:** {Business process optimization models}
- **Recommendation Systems:** {Automated recommendation engines}
- **Decision Support:** {Decision automation and support systems}
- **Simulation:** {What-if scenario analysis and simulation}

## Business Intelligence

### Reporting Framework
    ```yaml
    reporting:
      standard_reports:
        - report: {Report name}
          purpose: {Business purpose}
          frequency: {Daily|Weekly|Monthly}
          audience: [stakeholder1, stakeholder2]
    
      dashboard_suite:
        - dashboard: {Dashboard name}
          kpis: [kpi1, kpi2, kpi3]
          users: [user_group1, user_group2]
          refresh_frequency: {Data refresh schedule}
    ```

### Self-Service Analytics
- **Data Catalog:** {Searchable data asset catalog}
- **Self-Service Tools:** {User-friendly analytics tools}
- **Guided Analytics:** {Guided analysis workflows}
- **Data Preparation:** {Self-service data preparation tools}

### Mobile Analytics
    ```yaml
    mobile_analytics:
      mobile_dashboards: {Mobile-optimized dashboards}
      push_notifications: {Alert and notification system}
      offline_access: {Offline analytics capabilities}
      responsive_design: {Multi-device accessibility}
    ```

## Data Science and Advanced Analytics

### Data Science Platform
    ```yaml
    data_science:
      development_environment:
        notebooks: [Jupyter, Databricks, SageMaker]
        languages: [Python, R, Scala, SQL]
        libraries: [pandas, scikit-learn, tensorflow]
    
      mlops_platform:
        experiment_tracking: [MLflow, Weights & Biases]
        model_registry: {Model versioning and registry}
        deployment: {Model deployment pipeline}
        monitoring: {Model performance monitoring}
    ```

### Analytics Methodology
- **CRISP-DM:** {Cross-industry standard process for data mining}
- **Agile Analytics:** {Iterative analytics development}
- **Experimentation:** {A/B testing and experimentation framework}
- **Statistical Methods:** {Statistical analysis and hypothesis testing}

### Model Lifecycle Management
    ```yaml
    model_lifecycle:
      development:
        data_preparation: {Feature engineering and selection}
        model_training: {Model development and training}
        validation: {Model validation and testing}
    
      deployment:
        model_serving: {Real-time and batch model serving}
        integration: {Model integration with applications}
    
      monitoring:
        performance_monitoring: {Model accuracy and drift monitoring}
        business_impact: {Business impact measurement}
        retraining: {Model retraining procedures}
    ```

## Data Governance and Quality

### Data Governance Framework
    ```yaml
    data_governance:
      data_stewardship:
        data_owners: {Business data ownership}
        data_stewards: {Data stewardship responsibilities}
        data_custodians: {Technical data management}
    
      data_policies:
        data_usage: {Data usage policies and guidelines}
        data_access: {Data access control and permissions}
        data_retention: {Data retention and archival policies}
    
      metadata_management:
        business_glossary: {Business term definitions}
        data_lineage: {Data lineage tracking}
        impact_analysis: {Change impact analysis}
    ```

### Data Quality Management
- **Quality Dimensions:** {Accuracy, completeness, consistency, timeliness}
- **Quality Monitoring:** {Automated data quality monitoring}
- **Quality Remediation:** {Data quality issue resolution}
- **Quality SLAs:** {Data quality service level agreements}

### Privacy and Security
    ```yaml
    privacy_security:
      data_privacy:
        pii_handling: {Personal data handling procedures}
        anonymization: {Data anonymization techniques}
        consent_management: {Data consent tracking}
    
      access_control:
        role_based_access: {Analytics access control}
        data_masking: {Sensitive data masking}
        audit_logging: {Data access audit trails}
    ```

## User Experience and Adoption

### User Experience Design
    ```yaml
    user_experience:
      persona_based_design:
        business_users: {Self-service analytics for business users}
        analysts: {Advanced analytics for analysts}
        executives: {Executive dashboards and summaries}
    
      interface_design:
        intuitive_navigation: {User-friendly interface design}
        responsive_design: {Multi-device compatibility}
        accessibility: {Accessibility compliance}
    ```

### Training and Support
- **User Training:** {Analytics training programs}
- **Documentation:** {User guides and enable documentation}
- **Support Model:** {Analytics support structure}
- **Community:** {Analytics user community and collaboration}

### Adoption Metrics
    ```yaml
    adoption_metrics:
      usage:
        - metric: {Active users}
          target: {User adoption target}
          measurement: {Usage tracking approach}
    
      engagement:
        - metric: {Dashboard views}
          target: {Engagement target}
          trend: {Engagement trend analysis}
    
      value:
        - metric: {Decisions influenced}
          target: {Business impact target}
          measurement: {Impact measurement}
    ```

## Performance and Optimization

### Performance Monitoring
    ```yaml
    performance_monitoring:
      system_performance:
        query_response_time: {Query performance tracking}
        system_availability: {Platform uptime monitoring}
        resource_utilization: {Resource usage monitoring}
    
      user_experience:
        dashboard_load_time: {Dashboard performance}
        report_generation_time: {Report performance}
        data_refresh_time: {Data refresh performance}
    ```

### Optimization Strategies
- **Query Optimization:** {Query performance optimization}
- **Data Modeling:** {Optimal data model design}
- **Caching:** {Strategic caching implementation}
- **Resource Management:** {Compute and storage optimization}

## Business Value and ROI

### Value Measurement
    ```yaml
    value_measurement:
      cost_savings:
        operational_efficiency: {Process efficiency improvements}
        automated_reporting: {Report automation savings}
        faster_decisions: {Decision speed improvements}
    
      revenue_impact:
        customer_insights: {Customer analytics value}
        product_optimization: {Product improvement value}
        market_opportunities: {Market insight value}
    
      risk_mitigation:
        fraud_detection: {Fraud prevention value}
        compliance_monitoring: {Compliance automation value}
        operational_risk: {Risk monitoring value}
    ```

### ROI Analysis
- **Investment Tracking:** {Analytics investment tracking}
- **Benefit Quantification:** {Business benefit measurement}
- **ROI Calculation:** {Return on investment analysis}
- **Value Communication:** {Value story and communication}

## Future Roadmap

### Technology Evolution
    ```yaml
    technology_roadmap:
      emerging_technologies:
        augmented_analytics: {AI-powered analytics}
        natural_language: {Conversational analytics}
        automated_insights: {Auto-generated insights}
    
      platform_evolution:
        cloud_native: {Cloud-native analytics}
        real_time: {Real-time analytics capabilities}
        edge_analytics: {Edge computing analytics}
    ```

### Capability Development
- **Advanced Use Cases:** {Next-generation analytics use cases}
- **Skill Development:** {Analytics skill building}
- **Platform Enhancement:** {Platform capability roadmap}
- **Innovation Labs:** {Analytics innovation initiatives}

## Validation
*Evidence that analytics platform delivers actionable insights, drives data-driven decisions, and generates measurable business value*

## Quality Standards

### Bronze Level (Minimum Viable)
- [ ] Basic reporting and dashboard capabilities
- [ ] Simple data integration and basic data quality
- [ ] Manual analytics processes with limited self-service
- [ ] All required metadata fields completed

### Silver Level (Investment Ready)
- [ ] Comprehensive BI platform with self-service capabilities
- [ ] Structured data governance and quality management
- [ ] Advanced analytics capabilities with some automation
- [ ] User training and support programs

### Gold Level (Operational Excellence)
- [ ] Advanced analytics platform with AI/ML capabilities
- [ ] Sophisticated data governance with automated quality monitoring
- [ ] Comprehensive self-service and citizen data science
- [ ] Strategic business value measurement and optimization

## Common Pitfalls

1. **Data silos**: Disconnected analytics environments and data sources
2. **Poor data quality**: Analytics based on poor quality or inconsistent data
3. **Limited user adoption**: Analytics tools that aren't used by business users
4. **Lack of business alignment**: Analytics without clear business value or ROI

## Success Patterns

1. **Business-driven analytics**: Analytics strategy aligned with business objectives with clear business value and ROI measurement
2. **Self-service culture**: Empowered business users with self-service capabilities and democratized access to data and analytics
3. **Continuous innovation**: Regular adoption of new technologies and techniques with innovation culture and experimentation
4. **Data quality focus**: Comprehensive data governance with automated quality monitoring and remediation

## Relationship Guidelines

### Typical Dependencies
- **DAT (Data Models)**: Data models drive analytics data architecture and quality requirements
- **SYS (Systems)**: System capabilities inform analytics platform integration and data sources
- **INF (Infrastructure)**: Infrastructure design supports analytics platform performance and scalability
- **MET (Metrics)**: Business metrics drive analytics requirements and success measurement

### Typical Enablements
- **PER (Performance Specification)**: Analytics insights drive performance optimization and monitoring
- **QUA (Quality Specification)**: Analytics quality standards drive overall data and system quality
- **GOV (Governance)**: Analytics governance enables data-driven governance and compliance
- **STR (Strategy)**: Analytics insights enable strategic decision making and planning

## Document Relationships

This document type commonly relates to:

- **Depends on**: DAT (Data Models), SYS (Systems), INF (Infrastructure), MET (Metrics)
- **Enables**: PER (Performance Specification), QUA (Quality Specification), GOV (Governance), STR (Strategy)
- **Informs**: Business intelligence strategy, data strategy, technology roadmap
- **Guides**: Analytics implementation, user experience design, data governance

## Validation Checklist

- [ ] Executive summary with clear purpose, business impact, technology platform, and user community
- [ ] Analytics strategy with business objectives, maturity assessment, and use case portfolio
- [ ] Data architecture with data sources, pipeline architecture, and data models
- [ ] Analytics platform with technology stack, capabilities, and performance characteristics
- [ ] Analytics capabilities with descriptive, diagnostic, predictive, and prescriptive analytics
- [ ] Business intelligence with reporting framework, self-service analytics, and mobile capabilities
- [ ] Data science and advanced analytics with platform, methodology, and model lifecycle management
- [ ] Data governance and quality with framework, quality management, and privacy controls
- [ ] User experience and adoption with design, training, and adoption metrics
- [ ] Performance and optimization with monitoring and optimization strategies
- [ ] Business value and ROI with value measurement and analysis
- [ ] Future roadmap with technology evolution and capability development
- [ ] Validation evidence of analytics effectiveness, business value generation, and user adoption

---

## Related Documents

- [Back to Document Types](/docs/document-types)
- [Domain: Technology & Data](/docs/domains/technology-data)
- [Full Specification](/spec/v1-0-0)