Data Management Life Cycle

The data life cycle organizes and illustrates the elements of data management. Our data are corporate assets with value beyond our immediate need and should be managed throughout the entire data life cycle. 

Data lifecycle diagram


Planning for a project involves making decisions about data management, potential products, as well as data stewardship roles and responsibilities. It is important to document all stages of the data management life cycle and quality control prior to beginning a  new project.


Data can be acquired by collecting new data, processing old or legacy data, collaborating with partners, and contracting others to collect data.


Data maintenance includes processing data for analysis, creating metadata, and making sure data are in a format that can be accessed by others in the future.


The ability to prepare, release, and share quality data to the public, other agencies, and internally is an important part of the life cycle process.


Evaluate represents steps associated with processing and analyzing data. Important goals for both the processing and analysis of data are maximizing accuracy and productivity while minimizing costs.


Data archiving supports the long-term storage of scientific data and the methods used to read or interpret them.

Quality Assurance / Quality Control

Data quality management is the prevention of data defects that reduce our ability to apply data towards our science-based conservation efforts.