In order to get deeper insights, create forecasts, or come up with suggestions, advanced analytics examines data or content autonomously or partially autonomously using sophisticated tools and methodologies, often beyond those of traditional business intelligence (BI).
Organizations that employ advanced analytics are better able to take swift decisions and make predictions about the future with greater assurance. Organizations can use it to make data-driven decisions and learn more about customer preferences, market trends, and important company operations.
Fundamentally, advanced analytics differs from classic descriptive analytics, or business intelligence, in that it uses automation and artificial intelligence (AI) to handle far more complicated datasets and generate much more insightful conclusions and forecasts.
Data/text mining, machine learning, pattern matching, forecasting, visualization, sentiment analysis, semantic analysis, network and cluster analysis, multivariate statistics, graph analysis, simulation, complex event processing, and neural networks are examples of advanced analytical approaches.
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