Data & AI in the Data Domain

Expert articles by Dietrich Bartsch on data architecture, AI applications, and modern data practices.

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Data Architecture & Systems

AI is only as good as the data it works with. This category bridges classical data management with modern AI: vector databases, RAG architectures, feature stores, and data lakehouse approaches.

Intelligence Layer / LLMs

Selecting and managing the actual brains of AI: proprietary vs. open-source LLMs, reasoning models, small language models, and the criteria for sound model decisions in enterprise environments.

Operationalization & Applications

How AI technology gets into production: agentic workflows, LLMOps, automated data analysis, and the journey from prototype to production AI application in everyday business.

The foundation of every AI initiative: before technology is rolled out, the guardrails must be in place. This category covers AI strategy, data sovereignty, compliance (EU AI Act), IP protection, vendor lock-in, and AI ethics.

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Operationalization & Applications

From Chatbot to Colleague: How Agentic AI Workflows Revolutionize Data Analysis

The simple AI chatbot is a thing of the past. In 2026, AI agents take center stage: autonomous systems that independently query SQL databases, generate reports, and deliver actionable insights. This article explains the architecture of agentic workflows and highlights what matters in LLMOps practice.

May 29, 2026

Data Architecture & Systems

The Foundation for Enterprise AI: How RAG and Vector Databases Eliminate Hallucinations

An LLM without access to internal enterprise data is of limited use in business settings. Retrieval-Augmented Generation (RAG) combined with vector databases solves this problem — but success depends entirely on data quality. This article explains the architecture behind modern enterprise AI systems.

May 29, 2026

Intelligence Layer / LLMs

The Brains of AI: GPT-4o, Gemini, Claude and the Evolution of Reasoning Models

GPT-4o, Gemini 1.5 Pro, Claude 3.5 Sonnet — the performance landscape of large language models in 2026 is hard to navigate. This article benchmarks the leading models on practical criteria, explains the paradigm shift toward reasoning, and answers the defining question: when is a local small language model sufficient — and when do you need the cloud giants?

May 29, 2026

Strategy & Governance

Data Sovereignty in the AI Era: Why Enterprises Are Betting on Open-Source and Local Hosting in 2026

Cloud APIs like OpenAI and Google Gemini promise AI capabilities at the push of a button — but for organizations handling sensitive data, they represent a double-edged sword. This article analyzes the tension between AI performance and data sovereignty, and explains why local hosting of open-source models has become a strategic imperative in 2026.

May 29, 2026