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Storage Technologies for Data Reservoir

In: Business Analytics Progress on Applications in Asia Pacific

Author

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  • Balaji Rajamani

Abstract

Enterprises have been continuously studying and exploring data to gain insights and drive business decisions. Large enterprises have business intelligence systems for enabling business analytics. However, with growing competitions in the market, variety of data, and external factors, there is a need to bring in voluminous data into the enterprise and derive intelligence out of it to drive better decision-making. Big data involves managing high volume and variety of data that needs to be processed fast. In the big data world, data related to an enterprise will no longer be limited to ERP and internal systems, but will include internal and external data sources. The nature of the data can no longer be restricted to structured data alone, as the significance of unstructured data is increasing over time to drive business decisions. The data volume will be huge and diverse, requiring a careful design of the IT architecture. This study evaluates and assesses different storage technologies for such large enterprises, with the key considerations of type of storage, performance, security, cost, and big data integration support.

Suggested Citation

  • Balaji Rajamani, 2016. "Storage Technologies for Data Reservoir," World Scientific Book Chapters, in: Jorge L C Sanz (ed.), Business Analytics Progress on Applications in Asia Pacific, chapter 5, pages 104-126, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9789813149311_0005
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    More about this item

    Keywords

    Business Analytics; Entrepreneurship; Big Data; Information Technology;
    All these keywords.

    JEL classification:

    • L26 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Entrepreneurship

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