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Big Data and augmented analytics.
Big Data refers to the large and complex data sets that are generated by businesses, governments, and individuals. This data can come from a variety of sources, including social media, sensors, and transactional systems, and it can be structured or unstructured. The growth of Big Data has been driven by the increasing availability of data, the growth of cloud computing, and the development of new data storage and processing technologies.
Big Data presents both opportunities and challenges. On the one hand, it allows organizations to gain new insights and make better decisions by analyzing large and complex data sets. On the other hand, the sheer volume and complexity of Big Data can make it difficult to process and analyze using traditional methods.
Augmented Analytics refers to the use of machine learning and artificial intelligence (AI) to automate and enhance the process of analyzing Big Data. Augmented Analytics allows organizations to quickly and easily analyze large data sets, identify patterns and trends, and make data-driven decisions. The goal of Augmented Analytics is to make data analysis more accessible and efficient, so that organizations can gain insights faster and more easily.
In conclusion, Big Data and Augmented Analytics are two related concepts that are transforming the way organizations gather, process, and analyze data. The growth of Big Data is driving the need for Augmented Analytics, which leverages machine learning and AI to make data analysis more accessible and efficient.
