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.
INSIGHTS & PRACTICE
Business process discovery
Methods, exercises and project tools for consultants implementing business solutions.
8 publications
Process mining: visualizing real flows and shadow processes
Process Mining methodology reconstructs real task flows using digital footprints, revealing hidden bottlenecks and unauthorized workarounds invisible to management.
Automation antipatterns: chaos, RPA, and AI risks
How to avoid cementing inefficiencies, replace fragile RPA scenarios with reliable API integrations, and safely implement AI into operational workflows using BPMN and DMN standards.
Overview of BPM platforms: process orchestration using BPMN 2.0 and DMN standards
An analysis of architectural approaches to business process automation. Comparing Scriptum, Camunda, Bonita, and UiPath to eliminate shadow processes and enable flexible modeling.
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.
When organizations truly need ECM: signals, ROI, and an honest filter
How to distinguish simple digitalization from a real need for ECM? An honest filter based on ISO and NIST standards for CIOs, COOs, and legal departments.
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-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