Common integration mistakes that destroy IT architecture
Why ignoring organizational principles, lacking data owners, and "big bang" migrations doom corporate integration, and how to build a managed architecture.
AUTHOR / FinTraining Academy
Oleksandra Dmytriv
Industry Journalist
About
Oleksandra Dmytriv. Industry Journalist. The profile covers practical publications and professional commentary on corporate learning, digital transformation, and team development.
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Author’s publications
Current articles · 7Data governance and data lineage: quality for AI
The success of AI initiatives depends on data lineage transparency and Data Governance maturity. Learn how to avoid poor data quality traps and prepare enterprise IT architecture for AI scaling.
API Management and Data Contracts: Overcoming Integration Chaos
How to transition from fragile point-to-point connections to a managed integration layer using API gateways and schema registries to ensure enterprise system stability.
Master Data Management: The role of Data Stewards and Owners
The transition to decentralized architectures and AI integration has revealed a critical bottleneck: the lack of clear data ownership undermines the reliability of enterprise systems.
System integration and MDM: architectural patterns for scaling without chaos
Transitioning from point-to-point integrations to standardized EIP patterns, API Gateways, and managed Master Data Management to preserve corporate data integrity.
Who owns the customer master record: Data Governance for Customer 360
Effective customer data management requires clearly defined responsibilities for master record modifications, especially in the context of AI and cybersecurity.
AI у master: data management and system integration
AI analytics is transforming master data management (MDM) in 2026, ensuring accuracy and efficiency in enterprise system integration.