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What Is Data Integrity?
Data integrity is the overall completeness, accuracy, and consistency of data over its entire lifecycle to ensure it remains reliable for its intended use.
When data has integrity, mechanisms have been put in place to ensure that data-in-use, data-in-transit, and data at rest cannot be changed by an unauthorized person or program. An important goal of preserving data integrity is ensuring that when data is recovered after a disruption, it can be trusted.
Data integrity plays an important role in business continuity and disaster recovery and can be enforced both at the physical and logical levels. Physical integrity protects data from external factors such as power outages or hardware failures. Logical integrity initiatives ensure data remains accessible and error-free.
Why is data integrity important?
- Ensures the completeness, accuracy, and consistency of data
- Ensures only authorized users access or modify the data
- Ensures organizations comply with regulatory requirements
Common reasons why data integrity may be compromised include human errors, formatting errors, syntax errors, malware, ransomware attacks, or other cybersecurity threats.
Key Takeaways
- Integrity of data means the overall completeness, accuracy, and consistency of data over its entire lifecycle.
- It plays an important role in business continuity and disaster recovery.
- Data integrity is enforced in both hierarchical and relational database models.
- Regular backups and limiting access to sensitive data help protect the integrity of data.
- Technologies include blockchain, data validation, and error correction codes.
History of Data Integrity
To explain the term data integrity, it’s helpful to know its history. The concept of data integrity was first introduced in 1938 by the U.S. Food and Drug Administration (FDA). This and subsequent guidelines were related to data integrity for the pharmaceutical industry. What was the main data integrity issue? Regulators wanted to make certain that the industry captured accurate data during the drug development lifecycle and through commercialization.
How Data Integrity Works
Data integrity is enforced in both hierarchical and relational database models. Integrity is usually imposed during the database design phase through the use of standard procedures and rules. It is preserved through the use of various error-checking methods and data validation procedures.
The concept of database integrity ensures all data can be traced and connected to other data. This makes everything recoverable and searchable. Having a single, well-defined, and well-controlled data integrity system increases stability, performance, reusability, and maintainability.
Best practices such as planning regular backups, controlling access to sensitive data sets, tracking changes, and auditing through logs help protect the integrity of data. Additionally, antivirus software helps protect against malware and other security exploits that can compromise data integrity.
To ensure data accuracy and consistency, organizations can leverage alternative data providers and sources along with traditional methods to validate and improve datasets.
Data Integrity Types
When defining data integrity, it’s important to understand how to achieve it. Data integrity control involves integrity constraints used in a relational database structure. If one of these features cannot be implemented in the database, it must be implemented through the software.
Data Integrity vs. Data Quality & Data Security
| Data integrity | Data quality | Data security |
|---|---|---|
| The overall completeness, accuracy, and consistency of data over its entire lifecycle. | The degree to which a given data set meets a user’s needs. | The protection of data from unauthorized access, corruption, or theft. |
| Prevents data corruption and maintains reliability. | Ensures data relevance, accuracy, and timeliness. | Secures data against breaches, loss, or tampering. |
| Technologies include blockchain, database constraints, data validation, and error correction codes. | Technologies include data cleansing, extract, transform, load (ETL), and master data management (MDM). | Technologies include access control systems (ACS), encryption, firewalls, and multi-factor authentication (MFA). |
Data Integrity Risks
What are data integrity issues? Common reasons why data integrity may be compromised, and the associated risks include:
- Cybersecurity threats – corrupt, alter, or delete data
- Data transmission errors – lost or corrupted data during transfer
- Formatting errors – incomplete records
- Syntax errors – inaccurate data storage or retrieval
- System failures – corrupted or lost data
- Unauthorized access – compromises data accuracy or consistency
Data Integrity Examples
Data integrity examples include:
Data Integrity Pros and Cons
- Ensures accurate and reliable data
- Helps meet regulatory compliance standards
- Improves decision-making
- Protects information from unauthorized changes
- Cybersecurity breaches threaten data integrity
- Human errors can compromise data
- Monitoring and maintenance is resource-intensive
- Requires investment in tools and processes
The Bottom Line
The data integrity definition refers to the overall completeness, accuracy, and consistency of data throughout its lifecycle. It ensures that data-in-use, data-in-transit, and data-at-rest remain reliable, even after recovery from disruptions. Mechanisms such as primary keys, foreign keys, and domain rules enforce data integrity in database systems.
The benefits of data integrity include improved decision-making, regulatory compliance, and protection from unauthorized changes. While challenges like cybersecurity breaches and human errors exist, data governance – along with emerging technologies such as artificial intelligence (AI) and blockchain, help maintain the integrity of data over its entire lifecycle, ensuring it remains reliable for its intended use.
