Practical insights from the field of data and analytics
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Creating dashboards in Power BI
A Power BI dashboard is useful when it shows users the status of key KPIs within seconds while also allowing them to quickly identify the causes of changes using filters, drill-through, and other interactions. In this article, we summarize practical principles for clear data visualization, the recommended structure for management reports, and common mistakes to avoid. We also take a look at the “backend”—how the dashboard fits into the corporate data platform on Azure (Azure SQL, Data Factory, Data Lake/Microsoft Fabric), and how to properly handle publishing, sharing, and security in the Power BI Service.
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.”
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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