What is Data Integrity?

Data Integrity refers to the accuracy, consistency, and reliability of data throughout its lifecycle. It ensures that the data is unaltered and uncorrupted from its original form, providing an accurate representation of the facts it is supposed to convey. Data Integrity is crucial for decision-making, regulatory compliance, and trust in the data's quality.

  • Components: Data Integrity encompasses several components, including validity, accuracy, consistency, reliability, and timeliness. It assures the data is correct, accessible, and protected from unauthorized alterations.
  • Maintenance: Maintaining Data Integrity involves implementing data validation, error checking, encryption, backups, and access control to protect data from accidental or intentional alterations, deletions, or unauthorized access.
  • Importance: Ensuring Data Integrity is vital for accurate decision-making, regulatory compliance, maintaining customer trust, and safeguarding an organization's reputation.
  • Technologies and Practices: Various technologies and practices are utilized to maintain Data Integrity, including database management systems (DBMS), data validation and verification procedures, error detection and correction algorithms, and audit trails.

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Related Terms

RAG (Retrieval-Augmented Generation)

Retrieval-augmented generation (RAG) is an advanced artificial intelligence technique (Generative AI) that optimizes the output of large language models (LLMs)

LLMOps (Large Language Model Operations)

LLMOps refers to the set of practices and workflows dedicated to developing, deploying, and managing artificial intelligence (AI) models, specifically those based on large language models (LLMs).

Generative AI

Generative AI refers to a type of artificial intelligence that can create new data samples from the same distribution as a given set of training data.