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Business Intelligence for Fairs, Congresses and Exhibitions

Smart Data Collective

While different companies, regardless of their size, have different operational processes, they share a common need for actionable insight to drive success in their business. Advancement in big data technology has made the world of business even more competitive. This eliminates guesswork when coming up with business strategies.

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Data virtualization

Dataconomy

Mechanics of data virtualization Understanding how data virtualization works reveals its benefits in organizations. Middleware role Data virtualization often functions as middleware that bridges various data models and repositories, including cloud data lakes and on-premise warehouses.

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

Data Science Blog

Companies use Business Intelligence (BI), Data Science , and Process Mining to leverage data for better decision-making, improve operational efficiency, and gain a competitive edge. A Data Product can take various forms, depending on the domain’s requirements and the data it manages.

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Important Features of Top Business Intelligence Tools

Dataversity

Business intelligence (BI) tools transform the unprocessed data into meaningful and actionable insight. BI tools analyze the data and convert them […]. The post Important Features of Top Business Intelligence Tools appeared first on DATAVERSITY.

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Exploring the Power of Microsoft Fabric: A Hands-On Guide with a Sales Use Case

Data Science Dojo

In the sales domain, this enables real-time monitoring of live sales activities, offering immediate insights into performance and rapid response to emerging trends or issues. Data Factory: Data Factory enhances the data integration experience by offering support for over 200 native connectors to both on-premises and cloud data sources.

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Beyond data: Cloud analytics mastery for business brilliance

Dataconomy

Data models help visualize and organize data, processing applications handle large datasets efficiently, and analytics models aid in understanding complex data sets, laying the foundation for business intelligence. It helps organizations understand trends, patterns, and anomalies in their data.

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Data Science Career Paths: Analyst, Scientist, Engineer – What’s Right for You?

How to Learn Machine Learning

Data scientists try multiple models, evaluate their performance, and fine-tune some parameters to get better accuracy. Data Visualization and Interpretation To make the data understandable to stakeholders, visualizations are created in the form of charts, graphs, and dashboards.