Data Practice
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A Data Practice is an organizational practice that involves the procedures and techniques used to handle, manage, and utilize data effectively in various applications and industries.
- Context:
- It can (typically) encompass a wide range of activities including Data Collection, Data Storage, Data Processing, and Data Analysis.
- It can (often) be applied in different domains such as business intelligence, scientific research, and machine learning.
- It can include the development of Data Standards to ensure consistency and quality in data management.
- It can involve the use of Data Tools and technologies to facilitate efficient data handling.
- It can range from simple practices like manual data entry to complex practices involving automated data pipelines and machine learning.
- It can include adherence to legal and regulatory requirements related to data privacy and security.
- ...
- Example(s):
- a Data Management Practice that focuses on the systematic control and maintenance of data throughout its lifecycle.
- a Data Quality Assurance Practice that ensures the accuracy, completeness, and reliability of data.
- a Data Integration Practice that combines data from different sources into a unified view.
- a Data Governance Practice that sets policies and standards for data usage and access.
- a Big Data Analytics Practice that analyzes large volumes of data to uncover patterns and insights.
- a Data Security Practice that protects data from unauthorized access and breaches.
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- Counter-Example(s):
- Ad-hoc Data Handling, which lacks standardized procedures and can lead to data inconsistency and errors.
- See: Data Management Practice, Data Governance, Data Quality Assurance, Data Integration.