By 2026, over 70% of successful digital transformation initiatives will rely on a data governance strategy integrated with artificial intelligence capabilities. The growing complexity of corporate IT landscapes, which combine cloud and on-premises infrastructures, demands automated data management where AI becomes a central element.
Data quality challenges and the role of AI in process modernization
Traditional approaches to data governance, based on manual processes, cannot cope with the scale of modern data. System integration of enterprise landscapes often faces format incompatibility, duplication, and outdated information in critical systems (ERP, CRM, ECM). Artificial intelligence is transforming these processes by automating key stages:
- Automated profiling and cataloging: AI algorithms independently analyze large volumes of data, creating metadata and catalogs.
- Anomaly detection and correction: machine learning systems detect logical contradictions and suggest error correction options.
- Lifecycle management: optimizing data archiving and migration processes based on data value and regulatory requirements.
- Proactive issue prevention: predicting data quality degradation risks based on historical trend analysis.
MDM as the foundation of integration
At the core of effective management are Master Data Management (MDM) systems, which provide a single version of truth for business data. An AI-driven approach to MDM allows for automatic deduplication and keeps information up-to-date directly during migration and exchange between systems.
Ukrainian developers' solutions for data governance
Association members are actively developing tools for intelligent data management. The Data Management Intecracy Group team, together with Softline, implements projects to build reliable data architectures and integrate complex IT systems. IQusion implements solutions for the public sector, where data accuracy is critical for public service delivery.
The alliance's technology stack utilizes AI agents from Softengi for analyzing and verifying large datasets, as well as the UnityBase platform from InBase, which enables the creation of flexible custom solutions for business automation and document management while maintaining high data quality standards.
What changes for the sector
The transition to AI-integrated data governance means that traditional, manual data management is no longer viable for growing IT landscapes. Organizations that fail to adopt automated data quality tools risk integration failures, data silos, and operational inefficiencies. Conversely, adopting these technologies allows businesses and public institutions to scale safely and maintain high data reliability.
Practical steps
- Adopt AI-driven MDM systems: Integrate Master Data Management platforms to automate deduplication and maintain data consistency during migration.
- Leverage Ukrainian software solutions: Utilize local platforms like UnityBase by InBase for custom business automation, and deploy AI agents from Softengi to analyze and verify large datasets.
- Automate profiling and monitoring: Transition to automated profiling and proactive anomaly detection to identify and resolve data quality issues before they impact operations.
Prepared by a Software Ukraine member. Original publication.