The data life cycle consists of six stages:

  • Plan
    Decide what kind of data is needed, how it will be managed, and who will be responsible for it.
  • Capture
    Collect data or bring it into the organization from a variety of sources.
  • Manage
    Maintain and care for the data, including determining how and where it is stored and which tools are used to manage it.
  • Analyze
    Use the data to solve problems, support decision-making, and achieve business goals.
  • Archive
    Store relevant data for long-term use or future reference.
  • Destroy
    Remove data from storage and delete all shared copies.

Important Note

The six stages of the data life cycle (plan, capture, manage, analyze, archive, destroy) should not be confused with the six phases of the data analysis process (ask, prepare, process, analyze, share, act).

These two frameworks serve different purposes and are not interchangeable.

Variations of the Data Life Cycle Across Organizations

The data life cycle provides a general framework for how data is managed. However, depending on organizational goals, industry, or sector, the data life cycle may be defined differently. Below are examples of how various institutions approach the data life cycle.

U.S. Fish and Wildlife Service

The U.S. Fish and Wildlife Service uses the following data life cycle:

  • Plan
  • Acquire
  • Maintain
  • Access
  • Evaluate
  • Archive

U.S. Geological Survey (USGS)

The U.S. Geological Survey uses this data life cycle:

  • Plan
  • Acquire
  • Process
  • Analyze
  • Preserve
  • Publish / Share

Across all stages, the following cross-cutting activities are performed:

  • Describe (metadata and documentation)
  • Manage quality
  • Backup and secure

Financial Institutions

Financial institutions may follow a data life cycle such as the one described in Strategic Finance magazine:

  • Capture
  • Qualify
  • Transform
  • Utilize
  • Report
  • Archive
  • Purge

Harvard Business School (HBS)

A data life cycle informed by Harvard University research consists of eight stages:

  • Generation
  • Collection
  • Processing
  • Storage
  • Management
  • Analysis
  • Visualization
  • Interpretation

Key Takeaways

Understanding the importance of the data life cycle is essential for success as a data analyst. Individual stages may vary by organization, industry, or sector.

  • The U.S. Fish and Wildlife Service and the USGS place strong emphasis on archiving and backing up historical data.
  • Harvard Business School focuses on research and teaching, which is why its data life cycle includes visualization and interpretation, even though these stages are often associated with a data analysis process rather than a data life cycle. The HBS model does not explicitly include a data purge or destruction stage.
  • In contrast, financial institutions clearly distinguish between archiving and purging data.

In summary, while data life cycles differ across organizations, one data management principle is universal:
Data must be governed so that it is accurate, secure, and available to meet an organization’s needs.