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What is the Future of Business Intelligence in the Coming Year?

Smart Data Collective

billion by 2025. Today, Microsoft’s Power BI leads the market of BI-a-a-S, being an excellent tool for data collection, analyzing and visualization. Unique feature: custom visualizations to fit your business needs better. Unique feature: drag and drop functionality to create visualizations faster. QlickSense.

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Low code/no code tools reap IT benefits—with caveats

CIO Business Intelligence

Low-code/no-code visual programming tools promise to radically simplify and speed up application development by allowing business users to create new applications using drag and drop interfaces, reducing the workload on hard-to-find professional developers.

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Top Graph Use Cases and Enterprise Applications (with Real World Examples)

Ontotext

Gartner predicts that graph technologies will be used in 80% of data and analytics innovations by 2025, up from 10% in 2021. Yet, the biggest challenge for risk analysis continues to suffer from lack of a scalable way of understanding how data is interrelated. Several factors are driving the adoption of knowledge graphs.

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Top Business Intelligence Features To Boost Your Business Performance

datapine

In fact, it is expected that by 2025, the BI market will grow to $33.3 Through powerful data visualizations, managers and team members can get a bigger picture of their performance to optimize their processes and ensure healthy project development. Thanks to modern data connectors , data integration has never been easier.

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How to choose the best AI platform

IBM Big Data Hub

.” When observing its potential impact within industry, McKinsey Global Institute estimates that in just the manufacturing sector, emerging technologies that use AI will by 2025 add as much as USD 3.7 Visual modeling: Combine visual data science with open source libraries and notebook-based interfaces on a unified data and AI studio.

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How data stores and governance impact your AI initiatives

IBM Big Data Hub

AI-optimized data stores enable cost-effective AI workload scalability AI models rely on secure access to trustworthy data, but organizations seeking to deploy and scale these models face an increasingly large and complicated data landscape.