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.
INSIGHTS & PRACTICE
AI
Methods, exercises and project tools for consultants implementing business solutions.
6 publications
Common ECM migration errors: why IDP and workflows stall
Successful migration to modern intelligent ECM systems requires mandatory design of fallback rules in workflows and the creation of a high-quality training corpus for IDP models.
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.
Checklist for selecting electronic document management systems
A practical approach to choosing an electronic document management system: critical questions during demos to avoid overspending and reduce IT project value.
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.
AI-driven data governance for system integration quality
By 2026, over 70% of successful digital transformation initiatives will rely on an effective Data Governance strategy integrated with artificial intelligence capabilities. This will enable addressing data quality issues critical for system integration. The increasing complexity of enterprise IT land