THE DATA LIFECYCLE
The data lifecycle is the ongoing process of collecting, using, storing, transferring, and destroying or permanently archiving data. This process is repeated again and again with different data sets. There are also foundational components that are key building blocks to successfully implementing the steps in the data lifecycle. These components are ongoing in the background and link together to support the management of the data lifecycle.
The diagram below illustrates the data lifecycle in the foreground, with the foundational components presented as puzzle pieces in the background. Move your mouse over the components in the diagram below to learn more about each component.
Information technology system
Tools used to collect, hold, analyze, and ensure the quality of data (e.g., data management software, spreadsheet software).
Data quality
Defines data quality objectives and integrates quality control measures into each part of the lifecycle (e.g., developing an error checking protocol).
Storage and backup
Includes data organization and storage processes, back-ups.
Indicator tracking
The iterative process of identifying, tracking, and refining indicators over time (see Acquiring and Working with Data).
Indicator tracking
The iterative process of identifying, tracking, and refining indicators over time (see Acquiring and Working with Data).
Security and privacy
Includes network security, network access, privacy, secure transfer of data, and physical protection.
Security and privacy
Includes network security, network access, privacy, secure transfer of data, and physical protection.
Identify data sources
Determining what data should be collected or obtained from existing datasets. For example, if you want to track the strength of cultural identity, you may want to measure access to traditional foods. An indicator might be the number of citizens that participate in fishing/hunting/harvesting (see Acquiring and Working with Data).
Acquire data
Obtaining data from external sources, developing a database and data entry forms, defining data standards, obtaining data, and developing quality assurance protocols (see Acquiring and Working with Data).
Analyze data
Analyses range in complexity. At its simplest, analysis may consist of visualizing data and exploring averages and distributions (see Acquiring and Working with Data).
Communicate and share knowledge
Translating data into knowledge and synthesizing with Indigenous ways of knowing and doing, including communicating to knowledge users through story-telling and visualizations. Information may be provided to internal and external audiences, including citizens, staff, other SGIGs, Canada, and the provinces and territories (see Acquiring and Working with Data).
Preserve/archive
Long-term storage of data.
Delete/destroy
Destruction of data at the end of the lifecycle.
This section of the Toolkit focuses on the foundational background components (the puzzle pieces), as well as the Preserve/archive and Delete/destroy steps in the data lifecycle. The other steps in the lifecycle (Indicator tracking; Identifying data sources; Acquiring data; Analyzing data; Communicating & sharing knowledge), are covered in Acquiring and Working with Data.
The webinar below is a presentation by Rebecca Wortzman and Lela Draganic (Big River Analytics) on data management, including the data lifecycle. For more webinars covering content in this toolkit click here.
