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The Strategic Business Value of Big Data

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Big Data Management

Abstract

Most of the information about Big Data has focused on the technical side of the phenomenon. This chapter makes the case that business implications of utilizing Big Data are crucial to obtain a competitive advantage. To achieve such objective, the organizational impacts of Big Data for today’s business competition and innovation are analyzed in order to identify different strategies a company may implement, as well as the potential value that Big Data can provide for organizations in different sectors of the economy and different areas inside such organizations. In the same vein, different Big Data strategies a company may implement towards its development are stated, as well as insights on how enterprises such as businesses, non-profits, and governments can use data to gain insights and make better decisions. Current and potential applications of Big Data are presented for different private and public sectors, as well as the ability to use data effectively to drive rapid, precise and profitable decisions.

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Notes

  1. 1.

    Interestingly leading companies’ self-perception is different. At the Economist Intelligence Unit research, most of the companies classifying themselves as a strategic data manager indicating that they have developed a well-defined corporate data strategy.

  2. 2.

    Self-service data preparation tools enable business users to run machine-learning algorithms on the data to visually highlight the structure, distribution, anomalies and repetitive patterns in data with guided intelligent capabilities to recommend ways for users to improve their data.

  3. 3.

    In Business analytics platform as a service (baPaaS) solutions are architected with integrated information management and business analytics stacks. These comprise database, integration capabilities and business analytics tools—or solutions that include only business analytic tools (for example, reporting and dashboarding)—leveraging autonomous cloud-based or on-premises data repositories.

  4. 4.

    Smart data discovery is a next-generation data discovery capability that enables business users or citizen data scientists to find insights from advanced analytics on multistructured data.

  5. 5.

    Natural-language generation (NLG) combines natural-language processing (NLP) with machine learning and artificial intelligence to dynamically identify the most relevant insights and context in data (trends, relationships, correlation patterns).

  6. 6.

    Hadoop-based data discovery enables business users to explore and find insights across diverse data (such as clickstreams, social, sensor and transaction data) that is stored and managed in the Hadoop Distributed File System (HDFS).

  7. 7.

    Event stream processing is a computing technique in which incoming events are processed to generate higher level, more useful summary information (complex events).

  8. 8.

    https://www.car2go.com/.

  9. 9.

    https://de.mytaxi.com/index.html.

  10. 10.

    https://www.healthtap.com/.

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Authors and Affiliations

  1. Tecnológico de Monterrey, Monterrey, Mexico

    Marco Serrato & Jorge Ramirez

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  1. Marco Serrato
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  2. Jorge Ramirez
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Correspondence to Marco Serrato .

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Editors and Affiliations

  1. ETSI Industriales de Ciudad Real, University of Castilla-La Mancha, Ciudad Real, Spain

    Fausto Pedro García Márquez

  2. Drexel University, Philadelphia, Pennsylvania, USA

    Benjamin Lev

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Serrato, M., Ramirez, J. (2017). The Strategic Business Value of Big Data. In: García Márquez, F., Lev, B. (eds) Big Data Management . Springer, Cham. https://doi.org/10.1007/978-3-319-45498-6_3

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