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.
INSIGHTS & PRACTICE
Process Automation
Methods, exercises and project tools for consultants implementing business solutions.
11 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.
Process modeling standards: BPMN, CMMN, and DMN
Effective enterprise process automation requires separating workflow orchestration, flexible case management, and business rules. Learn how to avoid hardcoded logic and identify shadow routes.
Understanding BPM versus RPA and workflow automation
A guide to BPM, RPA, and workflow automation for IT architects. Learn how to combine BPMN, DMN, and Process Mining to avoid technical debt and achieve end-to-end orchestration.
RPA after ERP: A Bridge or a Digital Patch?
RPA is a useful temporary bridge to legacy systems following an ERP deployment. However, automating Excel-based workflows, email approvals, and fragmented responsibilities often accelerates technical debt rather than resolving it.
Phased legacy ERP migration for uninterrupted transformation
Phased migration of legacy ERP systems in the public sector helps avoid downtime and ensures process continuity by focusing on automation and data.
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
Data governance and enterprise data management for system integration
Increasing data volumes and stricter regulatory requirements necessitate that organizations implement comprehensive Data Governance approaches. Effective system integration is a key factor in realizing these strategies.
Data integration and MDM for large organizations
The challenge of disparate data and the absence of a single source of truth for key entities is critical to business efficiency. System integration and MDM are key to resolving this.