Practical insights from the field of data and analytics
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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.
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.
DirectLake in Power BI – Direct Access to Data Without Duplication
DirectLake in Power BI – Direct Access to Data Without DuplicationDirectLake takes Power BI to a new level where you no longer have to make the painful trade-off between import speed and the timeliness of DirectQuery. Data is read directly from OneLake in Microsoft Fabric, without unnecessary duplication into the data model and without lengthy scheduled refreshes that often slow down work with large datasets. The result is interactivity close to import mode, but with significantly fresher data and a simpler “single source of truth” architecture. In this article, we’ll explain how DirectLake technically works with Delta tables, where it typically delivers the greatest performance and cost benefits, and how it differs from DirectQuery in terms of latency, infrastructure load, and operational risks in an enterprise environment.
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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.
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.
Propensity to Buy
How can you transform raw data into accurate predictions of purchasing behavior? The Propensity to Buy model represents the gold standard of data monetization, but its success depends entirely on the chosen architecture. In this article, we’ll explore the technical background of implementation in Microsoft Fabric and Azure environments. Discover 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 within a modern, scalable infrastructure.
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.
AI Governance in the Financial Sector
Artificial intelligence is becoming a key tool in the financial sector for risk management, credit scoring, and decision-making automation—but it is also attracting increasing attention from regulators. The new EU AI Act and AI governance principles provide banks and insurance companies with clear rules on how to design, operate, and monitor AI so that it is transparent, auditable, and ethically responsible. This article summarizes the main impacts of the regulation on financial institutions and demonstrates how to effectively implement AI governance on a modern data platform.