Catalog
concept#Data#Analytics#Distributed Systems

Consistency

Consistency refers to the alignment of data and processes within a system and is crucial for reliability.

Consistency is a key principle in software architecture that ensures all parts of a system function harmoniously together.
Established
Medium

Classification

  • Medium
  • Technical
  • Architectural
  • Advanced

Technical context

CRM SystemsERP SolutionsAnalytical Tools

Principles & goals

Data IntegrityAccess ControlError Prevention
Build
Enterprise

Use cases & scenarios

Compromises

  • Data Conflicts
  • System Delays
  • Lack of Synchronization
  • Conduct Regular Audits
  • Train Staff
  • Document All Processes

I/O & resources

  • Available Data Sources
  • Technical Infrastructure
  • Resource Capacities
  • Consistent User Data
  • Updated Information
  • Reliable Reports

Description

Consistency is a key principle in software architecture that ensures all parts of a system function harmoniously together. This is especially important in distributed systems where data needs to be synchronized across various components.

  • Increased Reliability
  • Better Data Quality
  • Optimized Processes

  • Requires extensive testing
  • Can cause high complexity
  • Potential impact on performance

  • Error Rate

    Number of erroneous data entries per week.

  • System Response Time

    The time taken by the system to process a request.

  • Customer Satisfaction

    Measurement of user satisfaction with the system.

Case Study on Data Consistency

A case study illustrating how a company implemented data consistency to reduce errors.

Consistency in Agile Methods

An article on the importance of consistency in agile development methodologies.

Start-up Success Story

A start-up that successfully implemented data consistency to build customer trust.

1

Identify Data Sources

2

Define Consistency Requirements

3

Conduct Data Integrity Tests

⚠️ Technical debt & bottlenecks

  • Insufficient Data Architecture
  • Outdated Technologies
  • Missing Unified Data View
Data SynchronizationLack of InterfacesPoor Data Quality
  • Direct Data Manipulation Without Logging
  • Lack of Backup Strategies
  • Data Mismatch Between Systems
  • Overlooking Data Sources
  • Relying on Insufficient Processes
  • Poor Communication Within the Team
Data ManagementSoftware DevelopmentSystem Integration
Consistency of InformationScalability of SolutionsUsability
  • Compliance with data protection regulations
  • Existing IT infrastructure
  • Budget limitations