AI-Driven Analytics
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AI-driven analytics refers to the use of artificial intelligence (AI) and machine learning (ML) techniques to analyze and derive insights from large and complex datasets. By leveraging AI algorithms, organizations can automate the process of data analysis, uncover patterns, trends, and correlations, and make data-driven decisions more effectively. AI-driven analytics systems can handle massive volumes of data, structured and unstructured, from various sources, including databases, sensors, social media, and IoT devices. These systems use advanced ML models, such as neural networks, decision trees, and clustering algorithms, to discover hidden patterns, predict future outcomes, and identify anomalies in the data. AI-driven analytics enables organizations to gain deeper insights into their operations, customers, and markets, leading to improved business outcomes, enhanced efficiency, and competitive advantage. Examples of AI-driven analytics applications include predictive maintenance, fraud detection, customer segmentation, personalized recommendations, and supply chain optimization.
References: - https://www.ibm.com/cloud/learn/ai-driven-analytics - https://www.sas.com/en_us/insights/analytics/what-is-ai-analytics.html