What are the factors that causes data inconsistency in distributed platform/application?

Data inconsistency in distributed platforms or applications can arise from a variety of factors, which can be categorized into several key areas:

Network and Infrastructure Issues

• Network partitions: When nodes in a distributed system become disconnected due to network issues, it can lead to data inconsistency as updates may not be propagated across all nodes1.

Concurrency and Race Conditions

• Concurrency and race conditions: Simultaneous access to data by multiple points can result in race conditions where the system fails to handle concurrent operations correctly, leading to inconsistencies1.

Replication and Synchronization

• Replication delays: Data replication across different nodes can be delayed or may fail to occur immediately, causing temporary inconsistencies1.
• Cache-Database inconsistencies: When data is changed in the database but not reflected in the cache, or vice versa, it can lead to stale data and inconsistencies5.

Data Entry and Human Error

• Manual data entry errors: Human errors such as typos, missing values, or incorrect formatting can propagate across systems and lead to inconsistencies34.
• Inconsistent data entry: Differences in data entry formats can cause inaccuracies and inconsistencies, for example, different formats for addresses4.

Data Integration and Standards

• Lack of data integration across systems: Data stored in multiple systems or databases can lead to inconsistencies if not integrated correctly. This includes issues with integration tools and data stored in silos134.
• Lack of data standards: Without established data standards, different interpretations and recordings of data can occur, leading to inconsistencies3.

System and Application Issues

• Dual writes: In microservices architectures, dual writes can cause inconsistencies when data is written to multiple locations but not consistently updated2.
• Legacy systems and data migration: Errors during data migration from one system to another can introduce inconsistencies due to improper transformation or incomplete data mapping3.

Organizational and Governance Issues

• Lack of data governance: The absence of data governance practices means there is no systematic oversight on data quality and consistency, allowing inconsistencies to persist undetected3.
• Organizational changes: Changes in roles, responsibilities, or organizational structures can disrupt data workflows and lead to inconsistent data-handling practices3.

AI and Complex System Challenges

• AI-related challenges: The use of AI in distributed systems introduces additional complexities, such as outdated training data and complex distributed architectures, which can challenge existing data consistency procedures1.

Addressing these factors requires a comprehensive approach that includes implementing robust data standards, automated validation processes, data governance frameworks, and thorough training programs.