concept#Data#Security#Architecture#Governance

Data Integrity

Principle ensuring accuracy, consistency, and trustworthiness of data across its lifecycle.

Data integrity denotes the accuracy, consistency, and reliability of data throughout its lifecycle.

Maturity

Established

Cognitive loadMedium

Classification

ComplexityMedium
Impact areaTechnical
Decision typeArchitectural
Organizational maturityIntermediate

Technical context

Integrations

Relational and NoSQL databasesBackup and archiving systemsMonitoring and SIEM platforms

Principles & goals

Principles

Enforce uniqueness of data statesVerification along the data flow chainSeparation of responsibilities for correctness and access

Value stream stage

Run

Organizational level

Enterprise, Domain, Team

Use cases & scenarios

Use cases

Scenarios

Compromises

Risks

False assumptions about integrity guarantees can lead to data loss
Lack of end-to-end verification in distributed systems
Excessive complexity from redundant integrity mechanisms

Best practices

Enforce principle of least privilege and auditing
Use checksums and signatures for critical data
Versioning and transaction logs for traceability

I/O & resources

Inputs

Data model and schema definition
Audit logs and change records
Backup strategies and checksums

Outputs

Integrity reports and alerts
Corrected and verified data sets
Audit trails for compliance

Resources

Description

Data integrity denotes the accuracy, consistency, and reliability of data throughout its lifecycle. It includes safeguards against accidental or malicious alteration and mechanisms for detection and correction of errors. Maintaining data integrity is essential for trust, regulatory compliance, and sound decision-making across systems and business processes.

✔Benefits

Increased trust in decision inputs
Reduction of errors through early detection
Support for compliance and audit requirements

✖Limitations

Additional storage and compute overhead for verification mechanisms
Increased implementation effort in heterogeneous environments
Not all types of errors are fully automatable

Trade-offs

Metrics

Integrity check rate
Share of records periodically verified for integrity.
Detection time
Time between occurrence of an integrity violation and its detection.
Recovery duration
Time to fully restore a consistent state after an incident.

Examples & implementations

Database constraints to prevent inconsistent states

Use of NOT NULL, FOREIGN KEY and UNIQUE to enforce structural integrity.

Checksums in distributed file system

Regular hash comparisons to detect bit-rot and corruption.

Provenance tracking for data pipelines

Tracking source, transformations and authorship for audit purposes.

Implementation steps

Analyze critical data paths and requirements

Define consistency and verification strategies

Implement verification mechanisms and monitoring

Regularly test recovery procedures

⚠️ Technical debt & bottlenecks

Technical debt

Missing checks in legacy data pipelines
Incomplete audit logs lacking integrity information
Ad-hoc correction scripts instead of stable processes

Known bottlenecks

I/O performanceData migrationsDistributed consistency

Misuse examples

Using only local checksums in a distributed system
Applying schema changes without a migration plan
Performing integrity checks only periodically and never in real time

Typical traps

Assuming database ACID solves all integrity problems
Ignoring metadata and provenance
Insufficient testing of recovery processes

Required skills

Database design and transaction modelsSecurity and cryptography fundamentalsOperational knowledge of backup and recovery

Architectural drivers

Traceability and auditabilityAvailability and performance requirementsSecurity and compliance requirements

Constraints

• Limited compute and storage resources
• Regulatory retention periods
• Heterogeneous system landscape with differing guarantees