Advanced analytics is an umbrella term for a type of analytics that uses sophisticated tools and techniques to autonomously (or semi-autonomously) explore data. These tools and techniques are typically beyond traditional BI capabilities and include predictive modeling, data and text mining, sentiment analysis, machine learning, neural networks, statistical algorithms, complex event processing, and more.
Big Data analytics is a type of advanced analytics that examines very large data sets – including structured, semi-structured, and unstructured data – from a wide range of sources. Using complex tools and techniques like predictive modeling, what-if analysis, and machine learning algorithms, Big Data analytics can surface hidden trends, unknown correlations, and other meaningful insights in data sets that are too large or diverse to be handled by traditional analytics.
Augmented analytics are analytics that have been “augmented” with artificial intelligence technologies, including machine learning and natural language processing (NLP). These powerful AI-driven analytics are not only capable of finding better insights, faster – they democratize advanced analytics by automating complex processes and allowing users to ask questions and understand answers with minimal training.