Advertisement

Difficulties Implementing Big Data: A Big Data Implementation Study

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10902)

Abstract

The exponential increase in the volume of data, the velocity at which they are created and their vast and progressively expanding varieties can be derived from virtually all aspects of our everyday lives. This situation prompts the need for urgent change in the way data are stored, received, and analyzed. Today, organizations under appreciate the changes, growth and strategic development their Big Data are capable of providing. Organizations also overestimate their own ability to access and interrupt their data in order to derive benefit from it. Big Data problems in organizations have historically been approached with an isolated outlook, rather than viewing issues as co-dependent parts of one another. The purpose of this research is to identify and summarize the general challenges faced by an organizations ability to adequately utilize and capitalize on the opportunities presented by its Big Data. Through the research methods of data collection and multiple case study analysis, this paper proposes a three-step framework model, which focuses on definition, organization and value creation. The proposed framework serves to mitigate Big Data implementation challenges and their involved issues faced by an organization.

Keywords

Big data Implementation Difficulties 

References

  1. 1.
    IDC. 2011 Digital universe study: extracting value from chaos (2011). http://www.emc.com/collateral/analyst-reports/idc-extracting-value-from-chaos-ar.pdf
  2. 2.
    Gobble, M.: Big Data: the next big thing in innovation. Res.-Technol. Manage. 56(1), 64–67 (2013)Google Scholar
  3. 3.
    Jin, X., Wah, B., Cheng, X., Wang, Y.: Significance and challenges of big data research. Big Data Res. 2(2), 59–64 (2015)CrossRefGoogle Scholar
  4. 4.
    Philip Chen, C., Zhang, C.: Data-intensive applications, challenges, techniques and technologies: a survey on Big Data. Inf. Sci. 275, 314–347 (2014)CrossRefGoogle Scholar
  5. 5.
    Isitor, E., Stanier, C.: Defining Big Data (2016)Google Scholar
  6. 6.
    IDC: New IDC forecast sees worldwide big data technology and services market growing to $48.6 billion in 2019, driven by wide adoption across industries [Press Release] (2016). http://www.idc.com/getdoc.jsp?containerId=prUS40560115
  7. 7.
    Huang, T., Lan, L., Fang, X., An, P., Min, J., Wang, F.: Promises and challenges of big data computing in health sciences. Big Data Res. 2(1), 2–11 (2015)CrossRefGoogle Scholar
  8. 8.
    Wang, Y., Kung, L., Byrd, T.: Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technol. Forecast. Soc. Chang. 126, 3–13 (2017)CrossRefGoogle Scholar
  9. 9.
    Kamilaris, A., Kartakoullis, A., Prenafeta-Boldú, F.: A review on the practice of big data analysis in agriculture. Comput. Electron. Agric. 143, 23–37 (2017)CrossRefGoogle Scholar
  10. 10.
    Friedberg, M., Chen, P., White, C., Jung, O., Raaen, L., Hirshman, S., Lipinski, L.: Increased importance of data and data analysis. In: Effects of Health Care Payment Models on Physician Practice in the United States, pp. 53–62 (2015)Google Scholar
  11. 11.
    Rastogi, N., Gloria, M., Hendler, J.: Security and privacy of performing data analytics in the cloud: a three-way handshake of technology, policy, and management. J. Inf. Policy 5, 129–154 (2015)CrossRefGoogle Scholar
  12. 12.
    Porche, I., Wilson, B., Johnson, E., Tierney, S., Saltzman, E.: Big data: challenges and opportunities. In: Data Flood: Helping the Navy Address the Rising Tide of Sensor Information, pp. 1–6 (2014)Google Scholar
  13. 13.
    Intelligence Science Board, Integrating Sensor-Collected Intelligence. Office of the Under Secretary of Defense for Acquisition, Technology, and Logistics, Washington, D.C., November 2008Google Scholar
  14. 14.
    Ribarsky, W., Xiaoyu Wang, D., Dou, W.: Social media analytics for competitive advantage. Comput. Graph. 38, 328–331 (2014)CrossRefGoogle Scholar
  15. 15.
    Zadeh, N.K., Sepehri, M.M., Farvaresh, H.: Intelligent sales prediction for pharmaceutical distribution companies: a data mining based approach. Math. Prob. Eng., 1–15 (2014)Google Scholar
  16. 16.
    Wegener, R., Sinha, V.: The value of big data: how analytics differentiates winnersGoogle Scholar
  17. 17.
    Alharthi, A., Krotov, V., Bowman, M.: Addressing barriers to big data. Bus. Horiz. 60(3), 285–292 (2017). Bain and Company (2013)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Florida Gulf Coast UniversityFort MyersUSA

Personalised recommendations