Technology and Big Data Influences On Predictive Analytics
Big Data is a collection of data sets that are so large and complex that they become awkward to work with using traditional database management tools. The volume, variety and velocity of Big Data have introduced challenges across the board for capture, storage, search, sharing, analysis, and visualization. Examples of big data sources include web logs, RFID and sensor data, social networks, Internet search indexing, call detail records, military surveillance, and complex data in astronomic, biogeochemical, genomics, and atmospheric sciences. Thanks to technological advances in computer hardware—faster CPUs, cheaper memory, and MPP architectures-–and new technologies such as Hadoop, MapReduce, and in-database and text analytics for processing Big Data, it is now feasible to collect, analyze, and mine massive amounts of structured and unstructured data for new insights. Today, exploring Big Data and using predictive analytics is within reach of more organizations than ever before.
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—John Kenneth Galbraith (b. 1908)
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