Data 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.
AUTHOR
Emiliia Zhuravel
Editorial contributor
Export processes, document workflows and the practical use of enterprise technology.
Publications
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.
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.