Glossary of terms
Data integrity refers to the accuracy, completeness, and consistency of data during its entire life cycle, from initial collection to eventual disposal or deletion. It involves maintaining and ensuring the quality of data over time, preventing unauthorized or unintended modifications or corruptions. Here’s a detailed definition, along with the main features and scope of data integrity:
Data integrity is the overall completeness, accuracy, and consistency of data. It ensures that data remains unaltered, uncorrupted, and maintains its intended quality and trustworthiness throughout its lifecycle, from creation, storage, processing, and transmission to eventual disposal or archival.
Main Features
1. Accuracy: Data should accurately represent the real-world entities or events it is intended to capture, without errors or mistakes.
2. Completeness: All necessary data elements should be present, and no data should be missing or omitted.
3. Consistency: Data should be coherent, uniform, and free from contradictions or conflicts across different systems, databases, or records.
4. Validity: Data should conform to defined rules, formats, and constraints, ensuring that it is meaningful and usable for its intended purpose.
5. Timeliness: Data should be available and up-to-date when needed, reflecting the most current and relevant information.
6. Accessibility: Authorized users should be able to access and retrieve data when required, while preventing unauthorized access or modifications.
7. Auditability: Changes to data should be traceable, with a clear audit trail detailing who made the changes, when, and why.
The scope of data integrity encompasses various aspects of data management, including:
1. Data Entry: Ensuring accurate and complete data capture during initial data entry or collection processes.
2. Data Storage: Maintaining data integrity during storage, preventing data corruption, unauthorized access, or unintended modifications.
3. Data Processing: Preserving data integrity during data transformations, calculations, or analysis operations.
4. Data Integration: Ensuring data integrity when combining or merging data from multiple sources or systems.
5. Data Transmission: Maintaining data integrity during data transfers or exchanges between systems, applications, or locations.
6. Data Backup and Recovery: Protecting data integrity through proper backup and recovery mechanisms, enabling data restoration in case of failures or disasters.
7. Data Retention and Archiving: Preserving data integrity during long-term data retention or archiving processes.
8. Data Deletion or Disposal: Ensuring proper and secure deletion or disposal of data when it is no longer needed, while maintaining compliance with relevant regulations and policies.
Data integrity is crucial for various applications, such as financial transactions, medical records, scientific research, and legal documents, where inaccurate or corrupted data can have severe consequences. It is a fundamental aspect of data quality and plays a vital role in ensuring the reliability, trustworthiness, and usefulness of data for decision-making, analysis, and reporting purposes.
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