The Analytics Development Lifecycle (ADLC) is a comprehensive, iterative framework that guides organizations through the end-to-end process of building, deploying, and managing analytics solutions.
ADLC emphasizes collaboration, quality, and continuous improvement to ensure analytics outputs are accurate, relevant, and actionable.
The lifecycle aligns analytics development with business objectives, covering planning, development, testing, deployment, operation, monitoring, and deeper analytics.
By following ADLC, teams can accelerate delivery, maintain trust in analytics, and continuously uncover new insights that drive strategic decision-making.
Plan
Planning lays the groundwork by defining goals, requirements, and access controls for the analytics initiative.
1. Identify business questions and objectives.
2. Determine data sources, quality considerations, and governance policies.
3. Define user roles and access levels, especially for sensitive data.
4. Break projects into manageable units for iterative development.
5. Plan for scalability, security, and compliance.
A thorough plan improves alignment across stakeholders and sets clear expectations.
Develop
Development translates plans into actionable analytics code, models, and visualizations.
1. Build data transformation pipelines and models.
2. Leverage automation, modular coding, and version control.
3. Apply best practices for data validation and testing.
4. Ensure reproducibility and transparency in code.
5. Collaborate across roles—data engineers, analysts, and business experts.
Well-executed development accelerates delivery while maintaining quality and flexibility.
Test
Testing ensures analytics code and models perform correctly and reliably before production deployment.
Comprehensive testing reduces risk and builds stakeholder confidence.
Deploy
Deployment promotes analytics solutions into production environments for operational use.
1. Automate deployment via merge processes and source control integration.
2. Verify seamless environment setup, configuration, and version alignment.
3. Manage rollback and recovery strategies.
4. Document deployment procedures and user training materials.
Effective deployment ensures accessibility, reliability, and scalability.
Operate
Ongoing operation sustains analytics performance within business workflows.
1. Monitor system uptime, data freshness, and processing speed.
2. Manage resource allocations and update configurations as needed.
3. Support end-users and troubleshoot operational issues.
4. Maintain data security and compliance standards.
Stable operation delivers consistent, predictable analytics availability.
Observe
Observation focuses on monitoring analytics usage, accuracy, and impact.
1. Track key quality metrics, user interactions, and error rates.
2. Detect anomalies or degradations in data feeds and model performance.
3. Maintain audit trails and metadata for governance.
4. Gather feedback from users for continuous improvement.
Observability enables proactive maintenance and trustworthiness.
Discover
Discovery involves exploring existing data assets and metadata to foster insight generation.
Discovery accelerates innovation by maximizing data asset value.
Analyze
Analysis applies statistical techniques, machine learning, and domain expertise to extract insights.
1. Perform exploratory data analysis and hypothesis testing.
2. Build predictive or prescriptive models.
3. Generate reports and visualizations addressing business questions.
4. Iterate based on findings and evolving needs.
The analysis phase delivers the ultimate business value and closes the ADLC loop.