01 Data Collection
A tailored process of accumulating and measuring information Teams can specify interest and objective variables to determine which data units to examine or ignore
The data input phase starts the engine by providing the information and metrics required for accuracy and substantiality
03 Data Processing
02
Data Input
The processing part of the data life cycle gets the data ready for use by various team members
Data output provides the quantitative summary of a data science activity, so it’s essential to a data life cycle. 05 Data Storage
04 Data Output
Keeping records of your data collections, techniques, and outcomes. Involves maintaining a secure database that team members can easily access. 06
Data output provides the quantitative summary of a data science activity, so it’s essential to a data life cycle
07 Data Archiving and Deletion
Data Dissemination
Data archiving and deletion are delicate processes requiring excellent due diligence and quality control
To read the full article. visit KemiNelson.com