Practical insights from the field of data and analytics
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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.
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.
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.
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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.
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.
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.
DataOps and Data Pipeline Automation
DataOps applies DevOps principles to the data domain: it standardizes and automates the development and operation of data pipelines, from ingestion through transformation and quality checks to controlled deployment. The result is faster delivery of data outputs, as well as greater reliability, auditability, and reusability of solutions.Â