Data valuesDAMA: 30

Integrity

The degree of absence of data value loss or corruption.

Created: 10/30/2025
Business Impact
Positive Impacts
  • •Preservation of data accuracy and completeness during storage, processing, and transmission.
  • •Reduced risk of data corruption, unauthorized modification, or accidental loss.
  • •Increased trust in the reliability and wholeness of data assets.
  • •More stable and dependable system operations and data pipelines.
Negative Impacts (if poor quality)
  • •Data corruption leading to inaccurate information, process failures, or system errors.
  • •Loss of critical data due to system malfunctions, transmission errors, or inadequate controls.
  • •Reduced confidence in data if its integrity is questionable.
  • •Increased costs for data recovery, correction, and validation.
Technical Description

%

Examples
Good quality vs poor quality indicators
Good Quality Examples

Logistics: Checksums and validation rules are used during data transmission to ensure container details are not corrupted when moving between systems.

Database: Robust backup and recovery procedures are in place, and regular tests confirm that data can be restored completely and without corruption.

File Transfer: Secure file transfer protocols (like SFTP) with built-in error checking are used to ensure complete and uncorrupted file delivery.

Poor Quality Examples

Logistics: Data transmission errors cause container numbers to be corrupted (e.g., 'MSCU12?4567' instead of 'MSCU1234567') during EDI exchange.

Database: A database restore process fails, and a portion of transaction data from the last backup is permanently lost.

File Transfer: A large CSV file is truncated during FTP transfer, resulting in incomplete records in the destination system.

Local Network
Quick Stats
Dependent KPIs0
Improvement Standards1
CategoryData values
API Access
GET /api/dq-dimensions/c64f3097-ad8d-4afb-b5a5-f3af20928ac2