Business Intelligence

Skill Profile

(Select at least one profile to enable specific modules)

• DevOps
• Backend
• Frontend
• AI-RAG
• Security Critical

Overview

Business Intelligence (BI) is the process of transforming raw data into actionable insights through reporting, analytics, and visualization to support data-driven decision making. Effective BI systems integrate data from multiple sources, transform it into a usable format, and present it through dashboards and reports, enabling organizations to replace intuition with evidence and establish a single source of truth for metrics across the organization.

Why This Matters

• Data-Driven Decisions: Replace intuition with evidence-based decision making
• Single Source of Truth: Consistent metrics across organization eliminate discrepancies
• Real-Time Monitoring: Track performance as it happens for proactive response
• Self-Service Analytics: Empower non-technical users to explore data independently
• Competitive Advantage: Identify trends and opportunities faster than competitors
• Improved Collaboration: Shared dashboards align teams around common goals

Core Concepts & Rules

1. Core Principles

• Follow established patterns and conventions
• Maintain consistency across codebase
• Document decisions and trade-offs

2. Implementation Guidelines

• Start with the simplest viable solution
• Iterate based on feedback and requirements
• Test thoroughly before deployment

Inputs / Outputs / Contracts

• Inputs:

- Data sources (databases, APIs, files, web analytics) - Business questions and KPI requirements - User access requirements and permissions - Dashboard/report specifications

• Entry Conditions:

- Data sources identified and accessible - Business questions clearly defined - Data warehouse or storage provisioned - BI tool selected and configured

• Outputs:

- Data warehouse with star schema - ETL/ELT pipeline for data ingestion - Semantic layer (LookML, dbt models) - Dashboards and reports - Data documentation and catalog

• Artifacts Required (Deliverables):

- Data model documentation (ERD, schema) - ETL/ELT pipeline configuration - Dashboard JSON/config files - Metric definitions and calculations - User access and permissions configuration

• Acceptance Evidence:

- Dashboards load within SLA (< 5s) - Data accuracy validated (sample verification) - User acceptance testing completed - Documentation reviewed and approved

• Success Criteria:

- Dashboard query response time < 5s - Data freshness < 24 hours (or as required) - User adoption rate > 70% of target audience - Data quality alerts configured and tested

Skill Composition

• Depends on: [Dashboard Design](23-business-analytics/dashboard-design/), [KPI Metrics](23-business-analytics/kpi-metrics/), [SQL for Analytics](23-business-analytics/sql-for-analytics/)
• Compatible with: [A/B Testing Analysis](23-business-analytics/ab-testing-analysis/), [Cohort Analysis](23-business-analytics/cohort-analysis/), [Funnel Analysis](23-business-analytics/funnel-analysis/)
• Conflicts with: None
• Related Skills: [dashboard-design](23-business-analytics/dashboard-design/), [kpi-metrics](23-business-analytics/kpi-metrics/), [sql-for-analytics](23-business-analytics/sql-for-analytics/)

Quick Start / Implementation Example

1. Review requirements and constraints
2. Set up development environment
3. Implement core functionality following patterns
4. Write tests for critical paths
5. Run tests and fix issues
6. Document any deviations or decisions

# Example implementation following best practices
def example_function():
    # Your implementation here
    pass

Assumptions / Constraints / Non-goals

• Assumptions:

- Development environment is properly configured - Required dependencies are available - Team has basic understanding of domain

• Constraints:

- Must follow existing codebase conventions - Time and resource limitations - Compatibility requirements

• Non-goals:

- This skill does not cover edge cases outside scope - Not a replacement for formal training

Compatibility & Prerequisites

• Supported Versions:

- Python 3.8+ - Node.js 16+ - Modern browsers (Chrome, Firefox, Safari, Edge)

• Required AI Tools:

- Code editor (VS Code recommended) - Testing framework appropriate for language - Version control (Git)

• Dependencies:

- Language-specific package manager - Build tools - Testing libraries

• Environment Setup:

- .env.example keys: APIKEY, DATABASEURL (no values)

Test Scenario Matrix (QA Strategy)

Technical Guardrails & Security Threat Model

1. Security & Privacy (Threat Model)

• Top Threats: Injection attacks, authentication bypass, data exposure
• Data Handling: Sanitize all user inputs to prevent Injection attacks. Never log raw PII
• Secrets Management: No hardcoded API keys. Use Env Vars/Secrets Manager
• Authorization: Validate user permissions before state changes

2. Performance & Resources

• Execution Efficiency: Consider time complexity for algorithms
• Memory Management: Use streams/pagination for large data
• Resource Cleanup: Close DB connections/file handlers in finally blocks

3. Architecture & Scalability

• Design Pattern: Follow SOLID principles, use Dependency Injection
• Modularity: Decouple logic from UI/Frameworks

4. Observability & Reliability

• Logging Standards: Structured JSON, include trace IDs request_id
• Metrics: Track errorrate, latency, queuedepth
• Error Handling: Standardized error codes, no bare except
• Observability Artifacts:

- Log Fields: timestamp, level, message, requestid - Metrics: requestcount, errorcount, responsetime - Dashboards/Alerts: High Error Rate > 5%

Agent Directives & Error Recovery

(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)

• Thinking Process: Analyze root cause before fixing. Do not brute-force.
• Fallback Strategy: Stop after 3 failed test attempts. Output root cause and ask for human intervention/clarification.
• Self-Review: Check against Guardrails & Anti-patterns before finalizing.
• Output Constraints: Output ONLY the modified code block. Do not explain unless asked.

Definition of Done (DoD) Checklist

• Tests passed + coverage met
• Lint/Typecheck passed
• Logging/Metrics/Trace implemented
• Security checks passed
• Documentation/Changelog updated
• Accessibility/Performance requirements met (if frontend)

Anti-patterns / Pitfalls

• ⛔ Don't: Log PII, catch-all exception, N+1 queries
• ⚠️ Watch out for: Common symptoms and quick fixes
• 💡 Instead: Use proper error handling, pagination, and logging

Reference Links & Examples

• Internal documentation and examples
• Official documentation and best practices
• Community resources and discussions

Versioning & Changelog

• Version: 1.0.0
• Changelog:

- 2026-02-22: Initial version with complete template structure