Data Life Cycle
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Once a study concept is developed data is then collected for that study.
Data life cycle. In workflows data transported from one activity to another is stored in a temporary work table. Analysis collection data life cycle ethics generation interpretation management privacy storage story-telling visualization. It gives an overview of the life of data from the stage of acquisition to its usereuse and finally leading to its deletion.
Data Science Life Cycle 1. It occurs in the data archive and is sometimes accompanied by a communication both inside and outside the enterprise. Today the full Data Life Cycle is more common.
In the final stage of the life cycle data is retained or destroyed. While there are many interpretations as to the various phases of a typical data lifecycle they can be summarised as follows. Every search query we perform link we click movie we watch book we read picture we take message we send and place we go contribute to the massive digital footprint we each generate.
The data lifecycle begins with a researcher s developing a concept for a study. Questions of documentation storage quality assurance and ownership need to be answered for each stage of the lifecycle. It seems obvious to mention this but it has to be evaluated what are the expected gains and costs of the project.
What is important is that we define the Data Life Cycle because each phase has distinct Data Governance Needs. The data lifecycle represents all of the stages of data throughout its life from its creation for a study to its distribution and reuse. The data life cycle talks about the transition of data through a series of stages during its utilization in digital marketing.
The activity of managing the use of data from its point of creation to ensure it is available for discovery and re-use in the future. The final phase of the data life cycle is the removal of the data and any copies from the enterprise. Data Science life cycle Image by Author The Horizontal line represents a typical machine learning lifecycle looks like starting from Data collection to Feature engineering to Model creation.
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