Hybrid development in enterprise: balancing custom code and low-code
How to combine the flexibility of custom development with the speed of low-code while maintaining engineering discipline, version control, and robust system architecture.
INSIGHTS & PRACTICE
System Integration
Practical guides to cross-team workflows, documents, shared data and responsible AI.
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25 publications
Data Mesh and data products: future of system integration by 2027
Transitioning to a Data Mesh architecture resolves integration chaos through data decentralization, data contracts, and moving away from fragile point-to-point connections.
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.
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.
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.
AI agents in workflow: first-line processing and BPM future
How to combine the flexibility of AI with the rigid logic of BPMN 2.0 and DMN to automate first-line request processing without security risks or operational chaos.
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.
Managing automation projects: Process discovery, pilots, and continuous improvement
Why traditional process maps doom IT projects to failure and how to build effective automation based on real logs, BPMN 2.0, and DMN decision tables.
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.