Data Management Practice
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A Data Management Practice is a data practice that involves the systematic control, organization, and maintenance of data throughout its lifecycle to ensure its quality, integrity, and accessibility.
- Context:
- It can (typically) include Data Collection, Data Storage, Data Processing, and Data Governance.
- It can (often) ensure data quality and integrity through processes like data validation, cleaning, and deduplication.
- It can involve organizing data in a way that makes it easily accessible and retrievable for various purposes.
- It can ensure compliance with legal and regulatory requirements regarding data privacy and security.
- It can range from simple data handling practices to complex data management systems.
- It can include the implementation of data policies and procedures to govern data usage and access.
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- Example(s):
- a Centralized Data Repository Practice that ensures all organizational data is stored and managed in a single location.
- a Data Governance Framework Practice that sets policies for data usage, access, and security within an organization.
- a ML Data Operations Practice that optimizes the lifecycle of data used for machine learning models.
- a Data Quality Management Practice that implements processes for continuous monitoring and improvement of data quality.
- a Customer Data Integration Practice that consolidates customer data from various sources to provide a unified view.
- a Data Warehousing Practice that integrates data from multiple sources for analysis and reporting.
- a Master Data Management (MDM) Practice that ensures consistency and accuracy of key business data across the organization.
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- Counter-Example(s):
- Poor Data Management Practices, which result in data silos, inconsistent data quality, and compliance issues.
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- See: Data Governance, Data Quality Management, Data Lifecycle Management, Data Security