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.
Advanced Segmentation in E-commerce
Advanced segmentation in e-commerce takes targeting beyond demographics and focuses on what customers actually do: how they shop, what they respond to, and when they’re ready to convert. By combining behavioral data with predictive models (RFM, propensity to buy, churn risk, Next Best Offer, LTV), you gain segments that can be used for personalization, more efficient budget management, and frequency and channel control. The result is more relevant campaigns, higher conversion rates, and a sustainably better ROI without unnecessary “shooting in the dark.”
Server-Side Tracking
Server-side tracking represents a major shift in digital analytics amid the end of third-party cookies and growing privacy concerns. Shifting measurement to the server allows companies to obtain more accurate marketing data, bypass technical limitations of browsers and ad blockers, and maintain full control over data flows. This article explains how SST fits into modern data architecture and when it becomes a strategic component of cloud analytics.
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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.
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.
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.
RAG over company data
Want to deploy generative AI on top of your company's own know-how without the risk of hallucinations? This technical deep dive into RAG architecture shows that AI success stands and falls with quality data engineering and data architecture. Explore how to build a modern data platform for AI based on Microsoft Fabric and Azure Data Lakehouse and how to overcome the challenges of data vectorization, security, and transition to a production enterprise environment.
Server-Side Tracking
Server-side tracking represents a major shift in digital analytics amid the end of third-party cookies and growing privacy concerns. Shifting measurement to the server allows companies to obtain more accurate marketing data, bypass technical limitations of browsers and ad blockers, and maintain full control over data flows. This article explains how SST fits into modern data architecture and when it becomes a strategic component of cloud analytics.