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Data Science and Visual Computing pp 11–31Cite as

Big Data

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Part of the book series: Advanced Information and Knowledge Processing ((BRIEFSAIKP))

Abstract

A major business trend for most organizations is big data and business analytics, along with mobile, cloud, and social media technologies. Big data may be characterized by its volume, velocity, and variety. Most data are heterogenous and unstructured as it contains mixed and often indeterminate amounts of different kinds of information such as text, images, dates, numbers, and other information in various formats. Data analysts and scientists spend most of their time in preparing, cleaning, and wrangling their data. Data analytics may be divided into descriptive analytics, predictive analytics, and prescriptive analytics. The continuing growth of data means that large-scale analytics becomes critical for business competitiveness, and also facilitating internal decision-making processes based on data internal to the organization. Big data requires complex and advanced visualization techniques in order to fully understand the information contained in the data. Machine learning and deep learning methods are being integrated into data analytics processes. Machine learning uses statistical techniques to give computer systems the ability to “learn” (i.e., progressively improve performance on a specific task) with data. Current issues and challenges with big data and its analysis are reviewed.

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  1. Simon Fraser University, Vancouver, BC, Canada

    John Dill

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Dill, J. (2019). Big Data. In: Data Science and Visual Computing. Advanced Information and Knowledge Processing(). Springer, Cham. https://doi.org/10.1007/978-3-030-24367-8_2

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  • DOI: https://doi.org/10.1007/978-3-030-24367-8_2

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