Practical insights from the field of data and analytics
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Copilot in Microsoft Fabric and Power BI
Copilot in Microsoft Fabric and Power BI is fundamentally changing the way companies work with data. By generating reports, analyses, and DAX calculations using natural language, analytics becomes more accessible to business users and accelerates the path from question to decision. This article demonstrates the practical use of Copilot, its benefits for a modern data platform, and the limitations that must be considered from the perspective of data architecture and governance.
TOON: A New Format for Professional Prompt Notation (And Why It’s Not Just “Another JSON”)
The TOON (Token-Oriented Object Notation) format is a new way of representing structured data designed with LLMs in mind—particularly in cases where prompts contain large amounts of tabular data or the state of multi-agent systems. It maintains semantic compatibility with JSON but represents data more concisely and readably: less syntactic “noise,” the ability to represent homogeneous arrays in tabular form, and natural handling of indentation. The article demonstrates when TOON makes sense as a professional format for prompts, when JSON remains the better choice, and what risks the novelty of the standard and the more limited ecosystem of tools entail.
AI Governance and the EU AI Act
The EU AI Act shifts the governance of artificial intelligence from the realm of "best practices" to a framework of measurable obligations—particularly for high-risk systems that affect customers or employees. This article outlines a practical approach to implementing AI governance within a company: from conducting an AI inventory and risk classification, through defining roles, internal policies, and approval processes, to technical measures for monitoring, audit trails, security, and the controlled deployment of LLMs in the Azure environment. The result is a framework that reduces both regulatory and reputational risks while enabling the scaling of AI into production processes without losing control.
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Multi-agent systems in marketing
Multi-agent systems are shifting marketing work from one-off text generation to an "AI team" of specialized agents who share roles (planning, analysis, content creation, quality control) and collectively deliver consistent results. When combined with Microsoft Azure and the AutoGen framework, these agent workflows can be securely operated, scaled, and integrated into corporate data and marketing channels. The result is faster campaign preparation, more variants and iterations in less time, better control over tone of voice and compliance, and a higher degree of personalization thanks to working with segments and performance data. At the same time, risks (inconsistencies, latency, costs, debugging) must be managed using shared context, templates, agent QA/“reviews,” and robust logging. The most practical way to start is with 1–2 “low-hanging fruit” scenarios with clear KPIs (e.g., generating campaign variants, virtual focus groups, assisted personalization) and gradually expanding from a POC to production deployment.
Google Analytics 4 vs. On-Premise Alternatives
The transition to Google Analytics 4 has brought a modern event-based model and rapid deployment, but in enterprise environments it often runs into limitations regarding data sovereignty, security, and the long-term handling of detailed data. This article compares GA4 with on-premise and self-hosted alternatives (e.g., Matomo, Piwik PRO) and explains when it makes sense to choose self-hosting, a hybrid architecture, or a full-fledged enterprise platform. It focuses on controlling data flows, access to raw data, integration into a data warehouse (DWH) or lakehouse, and connectivity to BI, data science, and AI scenarios within a corporate data platform.
Propensity to Buy
How can raw data be transformed into accurate predictions of purchasing behavior? The Propensity to Buy model represents the gold standard of data monetization, but its success stands or falls on the chosen architecture. In this article, we will explore the technical background of implementation in Microsoft Fabric and Azure environments. Learn how to leverage Data Lakehouse principles, advanced machine learning, and generative AI to identify customers with the highest potential—and how to orchestrate it all in a modern, scalable infrastructure.
Customer 360
Creating a unified Customer 360 profile is not just a marketing goal, but a complex architectural challenge. This article analyzes the necessary transition to Data Lakehouse architecture in the Microsoft Fabric and Azure ecosystem. Learn how to build a modern data platform that serves as a robust foundation for advanced cloud analytics and hyper-personalized communications using AI and LLM.
Synthetic Users and AI Focus Groups
Synthetic users and AI focus groups offer a new way to quickly and scalably validate ideas related to the customer experience without the complex logistics of traditional research. Digital personas based on modern language models enable you to simulate how different segments react to a product, messaging, pricing, and changes in the customer journey, and to uncover barriers to understanding or trust before implementation. Data Mind’s Agora AI tool brings this approach to life: it “brings your segmentation to life” in the form of personas, leads virtual focus groups on real-world scenarios, and provides structured outputs for CX and product team iterations.
AI Governance and the EU AI Act
The EU AI Act shifts the governance of artificial intelligence from the realm of "best practices" to a framework of measurable obligations—particularly for high-risk systems that affect customers or employees. This article outlines a practical approach to implementing AI governance within a company: from conducting an AI inventory and risk classification, through defining roles, internal policies, and approval processes, to technical measures for monitoring, audit trails, security, and the controlled deployment of LLMs in the Azure environment. The result is a framework that reduces both regulatory and reputational risks while enabling the scaling of AI into production processes without losing control.