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Data Mesh Architecture on Cloud for BI, Data Science and Process Mining

Data Science Blog

BI provides real-time data analysis and performance monitoring, while Data Science enables a deep dive into dependencies in data with data mining and automates decision making with predictive analytics and personalized customer experiences.

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Differentiation: Microsoft Fabric vs Power BI

Pickl AI

Summary : Microsoft Fabric is an end-to-end Data Analytics platform designed for integration, processing, and advanced insights, while Power BI excels in creating interactive visualisations and reports. Key Takeaways Microsoft Fabric is a full-scale data platform, while Power BI focuses on visualising insights.

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Future-Forward: 2024’s Most Promising Power BI Project Ideas

Pickl AI

Summary: This blog dives into the most promising Power BI projects, exploring advanced data visualization, AI integration, IoT & blockchain analytics, and emerging technologies. But what truly sets Power BI projects apart? Power BI projects go beyond simply creating reports.

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5 Key Components of Power BI: A Comprehensive Guide

Pickl AI

Summary: Power BI is a business intelligence tool that transforms raw data into actionable insights. Power BI enhances decision-making by providing interactive dashboards and reports that are accessible to both technical and non-technical users. What Is Power BI?

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Effective strategies for gathering requirements in your data project

Dataconomy

This blog post explores effective strategies for gathering requirements in your data project. Example: For a project to optimize supply chain operations, the scope might include creating dashboards for inventory tracking but exclude advanced predictive analytics in the first phase.

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Object-centric Process Mining on Data Mesh Architectures

Data Science Blog

It is therefore hardly surprising that some process mining tools are actually just a plugin for Power BI, Tableau or Qlik. The post Object-centric Process Mining on Data Mesh Architectures appeared first on Data Science Blog. Nevertheless, process mining can be considered a sub-discipline of business intelligence.

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Data science vs data analytics: Unpacking the differences

IBM Journey to AI blog

Having the right data strategy and data architecture is especially important for an organization that plans to use automation and AI for its data analytics. The types of data analytics Predictive analytics: Predictive analytics helps to identify trends, correlations and causation within one or more datasets.