Why Enterprise AI Starts With Data Architecture
AI pilots are easy to start and hard to scale. This article looks at why the architecture beneath AI — data models, metadata, access and lineage — determines whether AI can be trusted in production.
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Perspectives on Enterprise Technology, Data and AI
Practical viewpoints for executives and technology leaders on SAP, data platforms, analytics and enterprise AI.
Articles
7 articles shown
What Makes Enterprise Data AI-Ready?
“AI-ready” is used loosely. We outline the practical characteristics — quality, context, ownership, accessibility and governance — that make enterprise data usable by AI systems.
S/4HANA Migration: The Data Problems Organizations Underestimate
Functional design gets the attention, but data often decides the timeline. A look at the data challenges that surface late in S/4HANA programs and how to address them early.
Microsoft Fabric and the Modern Enterprise Data Platform
An executive-level view of where Microsoft Fabric fits in an enterprise data strategy, the architectural patterns it supports and the governance questions to settle before scaling it.
RAG vs Knowledge Graphs for Enterprise AI
Retrieval-augmented generation and knowledge graphs solve different problems. We compare when each is appropriate, and when combining them gives better grounding for enterprise AI.
Data Migration Is More Than an ETL Exercise
Treating migration as a technical extract-and-load task underestimates it. This article frames migration as a business program with ownership, quality gates and reconciliation.
The Future of Enterprise Analytics in an AI-First World
Natural-language interfaces and data agents are changing how people consume analytics. What changes for BI teams, semantic models and governance — and what stays the same.
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