The Data Lifecycle

Learning objectives

• Describe the stages of the data lifecycle
• Apply governance and quality controls
• Understand retention and privacy obligations

The stages

1. Capture - data is collected at the point of activity.
2. Store - data is persisted in a structured system.
3. Process - data is cleaned, transformed, and aggregated.
4. Analyze - data is explored and modeled.
5. Retire - data is archived or deleted per policy.

Quality and governance

Decisions are only as good as the data behind them. Set data owners, define quality rules, and treat dirty data as a risk rather than an annoyance.

Privacy and retention

Collect only what you need, keep it only as long as required, and document how it is deleted. Privacy regulation is not a constraint to work around; it is a design input.

Key takeaways

• Data flows through capture, storage, processing, analysis, and retirement.
• Governance and quality protect decision quality.
• Retention and privacy are design requirements.