Predictive maintenance in industrial IoT: data pipelines
Data architecture for Predictive Maintenance: how to standardize data flow via OPC UA and distribute computation between Edge and Cloud without replacing SCADA.
INSIGHTS & PRACTICE
Data and management metrics
Practical guides to business-process automation: from the first assessment to measurable results.
57 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.
Event-driven architecture: Kafka vs RabbitMQ and reliable event delivery
Comparing Kafka and RabbitMQ for building reliable event-driven architectures. Learn how to avoid integration issues and ensure data integrity.
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.
When to use ESB versus point-to-point integration
Comparing architectural approaches: when direct connections become inefficient and how implementing an integration bus helps avoid complexity during scaling.
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.
Integration architecture patterns for AI readiness: from chaos to Data Fabric
AI readiness is defined by architectural data maturity. Transitioning from point-to-point integrations to a managed layer with a unified API contract prevents data processing errors.
Secure IIoT integration: bridging IT and OT
Industrial IoT provides production data for analytics and predictive maintenance, but it also exposes OT systems to new risks. A practical approach involves standardizing data exchange via OPC UA, building cybersecurity based on ISA/IEC 62443, and never treating any protocol as a substitute for netw
Data Unification in IIoT Using OPC UA
OPC UA transforms disparate tags and registers from the OT environment into a unified semantic model for SCADA, MES, ERP, and Edge AI.