Improving analytics quality with the analytics development lifecycle

For years, analytics teams have borrowed superficially from software engineering: implementing source control for data pipelines, adding basic tests, or requiring code reviews. While these practices add value, they typically remain confined to the most technical segments of the analytics stack, such as data transformation. Critical processes like ingestion, analysis, and dashboarding often lack the rigor required to deliver business-critical insights with confidence.

The consequences are familiar to most data leaders:

• Eroded trust in outputs: Business users question dashboards when unexplained changes or errors appear
• Decision-making bottlenecks: Teams waste time investigating metric discrepancies rather than driving action
• Scaling challenges: As data teams grow, inconsistent processes breed duplicated efforts, conflicting logic, and longer development cycles

These are fundamentally process problems, not technology problems. The solution requires a lifecycle approach that treats the entire analytics workflow as a production software system—one subject to continuous improvement, quality assurance, and collaborative development practices.

The Analytics Development Lifecycle (ADLC): A strategic framework

The Analytics Development Lifecycle (ADLC) provides a structured approach for developing, deploying, and managing analytics assets with consistent quality and operational efficiency. Adapted from the Software Development Lifecycle (SDLC), this framework addresses the unique requirements of analytics workflows while maintaining enterprise-grade standards.

The ADLC encompasses six core phases:

1. Plan
2. Develop
3. Test
4. Deploy
5. Operate & Observe
6. Discover & Analyze

This lifecycle operates iteratively: insights from analysis inform future planning, and improvements flow continuously through production deployment. Each phase establishes clear expectations and proven practices that support cross-functional collaboration, complete traceability, and production reliability.

Requirements for enterprise analytics maturity

A well-implemented ADLC addresses several critical operational dimensions:

• Data scale: Maintains performance and reliability across varying data volumes
• Team scale: Supports individual contributors through hundreds of team members with robust collaboration workflows
• Accessibility: Enables meaningful participation from engineers, analysts, and business decision-makers
• Development velocity: Minimizes time from concept to insight without introducing costly handoffs
• Data correctness: Ensures outputs are accurate, testable, and dependable for business decisions
• Auditability: Provides complete change tracking and reproducibility for stakeholder confidence
• Governance: Integrates security and compliance requirements throughout the development process
• Use-case flexibility: Supports both exploratory analysis and mission-critical reporting
• Operational resilience: Enables rapid recovery from failures while minimizing business impact

While analytics organizations often structure teams around "data engineer," "analyst," or "business user" roles, effective ADLC implementation recognizes that these functions are fluid. The framework defines three core personas that may be distributed across job titles or embodied within individual contributors:

• Engineer: Develops reusable data pipelines, models, and metrics infrastructure
• Analyst: Leverages data assets to generate insights, reports, and recommendations
• Decisionmaker: Consumes analytical outputs to drive business strategy and operations

The ADLC explicitly encourages role flexibility, enabling team members to transition seamlessly between analyzing data patterns, proposing infrastructure changes, and interpreting insights for business impact. Tooling and workflow design should facilitate this adaptability rather than enforce artificial boundaries.

ADLC implementation: Phase-by-phase quality improvement

1. Plan

The planning phase establishes the foundation for all downstream work through clear business alignment, stakeholder identification, impact analysis, and technical design.

Implementation example: A marketing analyst identifies requirements for enhanced customer segmentation to improve campaign targeting effectiveness. Rather than developing an isolated solution, the analyst documents the business case, collaborates with data engineering on required schema changes, and evaluates impacts on existing dashboards and reporting workflows.

Critical Success Factors:

• Document comprehensive business rationale and success metrics
• Engage relevant stakeholders during requirements definition
• Design validation approaches and edge case handling upfront
• Define migration paths for changes affecting existing assets
• Plan for ongoing maintenance, access control, and documentation

2. Develop

The development phase implements planned changes using version-controlled code, whether SQL, Python, or declarative configuration languages.

Implementation example: A data engineer implements customer segmentation logic within a version-controlled repository, following established coding standards, writing maintainable and documented code, and extending shared libraries to minimize duplication.

Critical success factors:

• Maintain human-readable code as the authoritative source of truth
• Implement tool-agnostic solutions where possible to minimize vendor lock-in
• Provide flexible development environments while enforcing consistent standards
• Require comprehensive peer review for all production changes

3. Test

Production analytics assets require comprehensive automated testing that mirrors software engineering standards. The ADLC distinguishes between three essential testing categories:

• Unit tests: Validate business logic independent of underlying data
• Data tests: Assert expectations against production data samples
• Integration tests: Verify system-wide functionality, including cross-asset dependencies

Implementation example: Before deploying updated revenue calculation logic, the team implements data tests validating that revenue values remain positive and integration tests confirming that dependent dashboards render correctly with updated calculations.

Critical success factors:

• Enforce testing requirements for all production deployments
• Implement continuous integration (CI) pipelines that execute comprehensive test suites
• Establish and monitor test coverage metrics to ensure meaningful protection

4. Deploy

Deployment processes must be automated, reliable, and reversible. Manual deployment steps introduce risk and bottlenecks, while automated rollback capabilities ensure rapid recovery from production issues.

Implementation example: Following successful peer review and testing, an engineer merges changes to the main branch, automatically triggering deployment pipelines that promote changes to production. If regression testing identifies issues, automated processes execute immediate rollbacks to previous stable states.

Critical success factors:

• Implement branch-based environment management (development, staging, production)
• Automate all deployment and rollback procedures
• Design zero-downtime deployment capabilities for critical systems

5. Operate and observe

Production environments require continuous monitoring and proactive incident response. Data quality, system uptime, and processing performance must be tracked with automated alerting for rapid issue resolution.

Implementation example: When monitoring systems detect a failed nightly sales data load, automated alerts notify the responsible team immediately. Investigation traces the failure to an upstream schema change, enabling rapid correction and redeployment. Automated communications update business stakeholders on both the issue and resolution.

Critical success factors:

• Maintain high-availability standards for business-critical data systems
• Implement comprehensive monitoring with automated alerting for rapid issue detection
• Establish clear incident response procedures and regularly review operational metrics
• Continuously improve documentation and runbook procedures

6. Discover and analyze

This phase encompasses both data asset discovery and analytical work performed using those assets. Many organizations lack maturity in this area compared to their engineering-focused infrastructure.

Implementation example: A product manager investigating declining user engagement can efficiently search for certified datasets on user activity, understand data lineage and quality, perform exploratory analysis, and—upon developing actionable insights—create reusable dashboards subject to full ADLC governance.

Critical success factors:

• Provide intuitive search and discovery interfaces for curated, documented data assets
• Enable collaborative analysis with integrated feedback and review mechanisms
• Implement transparent access controls that maximize appropriate self-service capabilities
• Apply version control, testing, and maintenance standards to key analytical artifacts

Real-world implementation scenarios

ADLC adoption delivers measurable improvements in quality, velocity, and organizational trust regardless of company size or industry.

Scenario 1: Accelerating critical reporting issues

An enterprise discovers data discrepancies in quarterly revenue reports during board preparation. Traditional processes require tickets to data engineering with unclear timelines and lengthy email investigations. With ADLC implementation, business analysts can trace data lineage, propose corrections within core models, and execute automated testing and deployment workflows. Issues resolve in hours rather than weeks, maintaining stakeholder confidence and decision-making velocity.

Scenario 2: Enabling rapid innovation with compliance

A growth-stage company requires rapid iteration on customer segmentation while maintaining strict privacy compliance. ADLC enables development in isolated branches with code-enforced access controls. All changes undergo peer review and automated testing for both business logic and regulatory compliance before deployment. If production issues arise, automated rollbacks minimize exposure and stakeholder impact.

Scenario 3: Scaling through organizational growth

An enterprise managing rapid acquisition growth faces the challenge of integrating multiple data teams. ADLC provides common workflows for building, testing, deploying, and documenting assets across teams. Centralized, reusable logic prevents duplication while distributed teams contribute specialized enhancements. Success is measured through increased usage and trust in certified datasets across business units.

Implementation strategy for data leaders

• Incremental adoption: Begin with rigorous version control and testing for core pipelines, then extend mature practices to dashboards, notebooks, and broader workflows
• Organizational alignment: Foster culture change that empowers team members to contribute across traditional role boundaries while removing unnecessary process bottlenecks
• Strategic tooling decisions: Select platforms supporting reproducible workflows, automated testing, cross-functional collaboration, and asset discoverability
• Continuous improvement: Build feedback collection and operational review processes into every ADLC phase

Learn more about analytics workflows and best practices at getdbt.com/blog.