Data masking is a technique used to protect sensitive data by replacing it with fictitious data, ensuring that the actual data is not exposed during testing, analysis, or development.
A data warehouse is used to store large volumes of historical data that can be analyzed and used for reporting purposes.
ETL stands for Extract, Transform, Load, which are the three key steps in integrating data from different sources into a data warehouse.
A Data Steward is responsible for overseeing the entire lifecycle of data, ensuring its proper management, maintenance, and governance.
Data governance is the framework for managing data to ensure its availability, usability, integrity, and security.
Data normalization is the process of organizing data in a database to reduce redundancy and improve data integrity.
A DMP is a formal document that outlines how data will be managed during and after a research project, including handling, storage, and protection.
The primary purpose of data management is to ensure that data is accurate, available when needed, and secure from unauthorized access.
Data replication is a common method of data protection, where data is copied and stored in multiple locations to ensure availability and reliability.
Data validation is a key component of data quality management, ensuring that data is accurate, consistent, and reliable.
Metadata is data that provides information about other data, such as the date of creation, the author, the size of the file, and more.
