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Big Data for the Greater Good: An Introduction

  • Vincent Charles
  • Ali Emrouznejad
Chapter
Part of the Studies in Big Data book series (SBD, volume 42)

Abstract

Big Data, perceived as one of the breakthrough technological developments of our times, has the potential to revolutionize essentially any area of knowledge and impact on any aspect of our life. Using advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing, analysts, researchers, and business users can analyze previously inaccessible or unusable data to gain new insights resulting in better and faster decisions, and producing both economic and social value; it can have an impact on employment growth, productivity, the development of new products and services, traffic management, spread of viral outbreaks, and so on. But great opportunities also bring great challenges, such as the loss of individual privacy. In this chapter, we aim to provide an introduction into what Big Data is and an overview of the social value that can be extracted from it; to this aim, we explore some of the key literature on the subject. We also call attention to the potential ‘dark’ side of Big Data, but argue that more studies are needed to fully understand the downside of it. We conclude this chapter with some final reflections.

Keywords

Big data Analytics Social value Privacy 

References

  1. 1.
    R. Agarwal, G. Gao, C. DesRoches, A.K. Jha, Research commentary—the digital transformation of healthcare: current status and the road ahead. Inf. Syst. Res. 21(4), 796–809 (2010)CrossRefGoogle Scholar
  2. 2.
    B. Baesens, R. Bapna, J.R. Marsden, J. Vanthienen, J.L. Zhao, Transformational issues of big data and analytics in networked business. MIS Q. 40(4), 807–818 (2016)CrossRefGoogle Scholar
  3. 3.
    B. Barrett, I. Nitze, S. Green, F. Cawkwell, Assessment of multi-temporal, multisensory radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches. Remote Sens. Environ. 152(2), 109–124 (2014)CrossRefGoogle Scholar
  4. 4.
    W. Bastiaanssen, D. Molden, I. Makin, Remote sensing for irrigated agriculture: examples from research and possible applications. Agric. Water Manage. 46(2), 137–155 (2000)CrossRefGoogle Scholar
  5. 5.
    M.A. Beyer, D. Laney, The Importance of ‘Big Data’: A Definition, META Group (now Gartner) [online] (2012) https://www.gartner.com/doc/2057415/importance-big-data-definition. Accessed 10 Aug 2017
  6. 6.
    T. Bodenheimer, High and rising health care costs. Part 1: seeking an explanation. Ann. Intern. Med. 142(10), 847–854 (2005)CrossRefGoogle Scholar
  7. 7.
    E. Brynjolfsson, A. Saunders, Wired for Innovation (The MIT Press, Cambridge, MA, How Information Technology is Reshaping the Economy, 2010)Google Scholar
  8. 8.
    T.G. Cech, T.K. Spaulding, J.A. Cazier, in Proceedings of the Twenty-First Americas Conference on Information Systems. Applying business analytic methods to improve organizational performance in the public school system, Puerto Rico, 13–15 Aug (2015)Google Scholar
  9. 9.
    V. Charles, T. Gherman, Achieving competitive advantage through big data. Strategic implications. Middle-East. J. Sci. Res. 16(8), 1069–1074 (2013)Google Scholar
  10. 10.
    V. Charles, M. Tavana, T. Gherman, The right to be forgotten—is privacy sold out in the big data age? Int. J. Soc. Syst. Sci. 7(4), 283–298 (2015)CrossRefGoogle Scholar
  11. 11.
    I.D. Constantiou, J. Kallinikos, New games, new rules: big data and the changing context of strategy. J. Inf. Technol. 30(1), 44–57 (2015)CrossRefGoogle Scholar
  12. 12.
    J.W. Cortada, D. Gordon, B. Lenihan, The Value of Analytics in Healthcare: From Insights to Outcomes (IBM Global Business Services, Somers, NY, 2012)Google Scholar
  13. 13.
    M. Cox, D. Ellsworth, in Proceedings of the 8th IEEE Conference on Visualization. Application-controlled demand paging for out-of-core visualization (IEEE Computer Society Press, Los Alamitos, CA, 1997)Google Scholar
  14. 14.
    T.H. Davenport, P. Barth, R. Bean, How ‘big data’ is different. MIT Sloan Manage. Rev. 54(1), 43–46 (2012)Google Scholar
  15. 15.
    L. Einav, J.D. Levin, ‘The Data Revolution and Economic Analysis’, Prepared for NBER Innovation Policy and the Economy Conference [online] April (2013) http://www.nber.org/papers/w19035.pdf. Accessed 30 June 2018
  16. 16.
    A. Emrouznejad, Big Data Optimization: Recent Developments and Challenges. In the series of “Studies in Big Data”, Springer. ISBN: 978-3-319-30263-8 (2016)Google Scholar
  17. 17.
    J. Enck, T. Reynolds Network Developments in Support of Innovation and User Needs, No. 164, (OECD Publishing, 2009)Google Scholar
  18. 18.
    R.G. Fichman, B.L. Dos Santos, Z. Zheng, Digital innovation as a fundamental and powerful concept in the information systems curriculum. MIS Q. 38(2), 329–353 (2014)CrossRefGoogle Scholar
  19. 19.
    R. Frelat et al., Drivers of household food availability in sub-Saharan Africa based on big data from small farms. Proc. Natl. Acad. Sci. U.S. Am. 113(2), 458–463 (2016)CrossRefGoogle Scholar
  20. 20.
    C.B. Frey, M.A. Osborne, The Future of Employment: How susceptible are jobs to computerization? (Oxford Martin Programme on the Impacts of Future Technology, Oxford, 2013)Google Scholar
  21. 21.
    R. Galliers, S. Newell, G. Shanks, H. Topi, Call for papers for the special issue: the challenges and opportunities of ‘datification’; Strategic impacts of ‘big’ (and ‘small’) and real time data—for society and for organizational decision makers. J. Strateg. Inf. Syst. 24, II–III (2015)Google Scholar
  22. 22.
    C.M. Gillan, R. Whelan, What big data can do for treatment in psychiatry. Curr. Opin. Behav. Sci. 18, 34–42 (2017)CrossRefGoogle Scholar
  23. 23.
    J.M. Goh, G. Gao, R. Agarwal, Evolving work routines: adaptive routinization of in-formation technology in healthcare. Inf. Syst. Res. 22(3), 565–585 (2011)CrossRefGoogle Scholar
  24. 24.
    W.A. Günther, M.H. Rezazade Mehrizi, M. Huysman, F. Feldberg, Debating big data: a literature review on realizing value from big data. J. Strateg. Inf. Syst. 26, 191–209 (2017)CrossRefGoogle Scholar
  25. 25.
    K.J. Hammond, ‘The value of big data isn’t the data’, Harvard Business Review, May [online] (2013) http://blogs.hbr.org/cs/2013/05/the_value_of_big_data_isnt_the.html. Accessed 13 July 2017
  26. 26.
    I. Hashem et al., The rise of “big data” on cloud computing: review and open research issues. Inf. Syst. 47, 98–115 (2015)CrossRefGoogle Scholar
  27. 27.
    R. Herschel, V.M. Miori, Ethics and big data. Technol. Soc. 49, 31–36 (2017)CrossRefGoogle Scholar
  28. 28.
    IBM, The Four V’s of Big Data. [online] http://www.ibmbigdatahub.com/infographic/four-vs-big-data. Accessed 30 June 2018
  29. 29.
    Technology Advice, The Four V’s of Big Data [online] (2013) https://technologyadvice.com/blog/information-technology/the-four-vs-of-big-data/. Accessed 20 July 2017
  30. 30.
    IBM, What is Big Data? [online] (2016) https://www.ibm.com/analytics/hadoop/big-data-analytics. Accessed 20 Nov 2017
  31. 31.
    Á. Jóźwiaka, M. Milkovics, Z. Lakne, A network-science support system for food chain safety: a case from Hungarian cattle production. Int. Food Agribusiness Manage. Rev. Special Issue, 19(A) (2016)Google Scholar
  32. 32.
    J. Kallinikos, Governing Through Technology: Information Artefacts and Social Practice. (Palgrave Macmillan, Basingstoke, UK, 2011)CrossRefGoogle Scholar
  33. 33.
    A. Kamilaris, A. Kartakoullis, F.X. Prenafeta-Boldu, A review on the practice of big data analysis in agriculture. Comput. Electron. Agric. 143, 23–37 (2017)CrossRefGoogle Scholar
  34. 34.
    C. Kempenaar et al., Big Data Analysis for Smart Farming, vol. 655 (Wageningen University & Research, s.l., 2016)Google Scholar
  35. 35.
    J.I. Ker, Y. Wang, M.N. Hajli, J. Song, C.W. Ker, Deploying lean in healthcare: evaluating information technology effectiveness in US hospital pharmacies. Int. J. Inf. Manage. 34(4), 556–560 (2014)CrossRefGoogle Scholar
  36. 36.
    G.-H. Kim, S. Trimi, J.-H. Chung, Big data applications in the government sector. Commun. ACM. 57(3), 78–85 (2014).CrossRefGoogle Scholar
  37. 37.
    D. Laney, 3D Data Management: controlling data volume, velocity and variety. Applications delivery strategies, META Group (now Gartner) [online] (2001) http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf. Accessed 1 Aug 2017
  38. 38.
    C. Loebbecke, A. Picot, Reflections on societal and business model transformation arising from digitization and big data analytics: a research agenda. J. Strateg. Inf. Syst. 24(3), 149–157 (2015).  https://doi.org/10.1016/j.jsis.2015.08.002CrossRefGoogle Scholar
  39. 39.
    C. Magnin, How big data will revolutionize the global food chain [online] (McKinsey & Company, 2016). https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/how-big-data-will-revolutionize-the-global-food-chain. Accessed 13 December 2017
  40. 40.
    L. Markus, New games, new rules, new scoreboards: the potential consequences of big data. J. Inf. Technol. 30(1), 58–59 (2015)CrossRefGoogle Scholar
  41. 41.
    M.L. Markus, Information Technology and Organizational Structure, in Information Systems and Information Technology, Computing Handbook, ed. by H. Topi, A. Tucker, vol. Ii. (Chapman and Hall, CRC Press, 2014), p. 67, 61–22Google Scholar
  42. 42.
    M.L. Markus, M.S. Silver, A foundation for the study of It effects: a new look at desanctis and poole’s concepts of structural features and spirit. Journal of the AIS, 9(10/11), 609–632 (2008).CrossRefGoogle Scholar
  43. 43.
    M.L. Markus, A. Dutta, C.W. Steinfield, R.T. Wigand, The Computerization Movement in the Us Home Mortgage Industry: Automated underwriting from 1980 to 2004, in Computerization Movements and Technology Diffusion: From mainframes to ubiquitous computing, ed. by K.L. Kraemer, M.S. Elliott (Information Today, Medford, NY, 2008), pp. 115–144Google Scholar
  44. 44.
    A. McAfee, E. Brynjolfsson, Big data: the management revolution. Harvard Bus. Rev. 90(10), 60–68 (2012)Google Scholar
  45. 45.
    McKinsey Global Institute, Game changers: five opportunities for US growth and renewal, [online] July (2013), http://www.mckinsey.com/insights/americas/us_game_changers. Accessed 13 Dec 2017
  46. 46.
    E. Miluzzo, M. Papandrea, N.D. Lane, A.M. Sarroff, S. Giordano, A.T. Campbell, In Proceedings of 1st International Symposium on from Digital Footprints to Social and Community Intelligence. Tapping into the vibe of the city using vibn, a continuous sensing application for smartphones (Beijing, China: ACM, 2011), pp. 13–18Google Scholar
  47. 47.
    S. Nativi et al., Big data challenges in building the global earth observation system of systems. Environ. Model Softw. 68(1), 1–26 (2015)CrossRefGoogle Scholar
  48. 48.
    S. Newell, M. Marabelli, Strategic opportunities (and challenges) of algorithmic decision-making: a call for action on the long-term societal effects of ‘datafication’. J. Strateg. Inf. Syst. 24(1), 3–14 (2015).  https://doi.org/10.1016/j.jsis.2015.02.001CrossRefGoogle Scholar
  49. 49.
    F. Ohlhorst, Big Data Analytics: Turning Big Data into Big Money (Wiley, Hoboken, NJ, 2013)Google Scholar
  50. 50.
    Z.A. Pardos, Big data in education and the models that love them. Curr. Opin. Behav. Sci. 18, 107–113 (2017)CrossRefGoogle Scholar
  51. 51.
    I.d.S. Pool, Forecasting the Telephone: A Retrospective Technology Assessment of the Telephone (Ablex, Norwood, NJ, 1983)Google Scholar
  52. 52.
    W. Raghupathi, V. Raghupathi, Big data analytics in healthcare: promise and potential. Health Inf. Sci. Syst. 2(3), 1–10 (2014).  https://doi.org/10.1186/2047-2501-2-3CrossRefGoogle Scholar
  53. 53.
    H. Rahman, D. Sudheer Pamidimarri, R. Valarmathi, M. Raveendran, Omics: Applications in Biomedical (CRC PressI Llc, Agriculture and Environmental Sciences, s.l, 2013)Google Scholar
  54. 54.
    G. Secundo, P. Del Vecchio, J. Dumay, G. Passiante, Intellectual capital in the age of big data: establishing a research agenda. J. Intellect. Capital 18(2), 242–261 (2017)CrossRefGoogle Scholar
  55. 55.
    R. Senanayake, Sustainable agriculture: definitions and parameters for measurement. J. Sustain. Agric. 1(4), 7–28 (1991)CrossRefGoogle Scholar
  56. 56.
    M.S. Silver, Systems That Support Decision Makers: Description and analysis (John Wiley & Sons, Chichester, UK, 1991)Google Scholar
  57. 57.
    T. Sparapani, How Big Data and Tech Will Improve Agriculture, from Farm to Table. [online] (Forbes, 2017). https://www.forbes.com/sites/timsparapani/2017/03/23/how-big-data-and-tech-will-improve-agriculture-from-farm-to-table/#503f16c25989. Accessed 13 December 2017
  58. 58.
    K. Tesfaye et al., Targeting drought-tolerant maize varieties in southern Africa: a geospatial crop modeling approach using big data. Int Food Agribusiness Manage. Rev. 19(A), 1–18 (2016)Google Scholar
  59. 59.
    The Government Office for Science, Foresight: The Future of Computer Trading in Financial Markets (Final Project Report, London, 2010)Google Scholar
  60. 60.
    A. Thiagarajan, L. Ravindranath, K. LaCurts, S. Madden, H. Balakrishnan, S. Toledo, J. Eriksson, in Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems. Vtrack: accurate, energy-aware road traffic delay estimation using mobile phones (ACM, Berkeley, California, 2009), pp. 85–98Google Scholar
  61. 61.
    A.C. Tyagi, Towards a second green revolution. Irrig. Drainage 65(4), 388–389 (2016)CrossRefGoogle Scholar
  62. 62.
    N. Ungerleider, IBM’s Watson is ready to see you now—in your dermatologist’s office. Fast Company [online] May (2014) http://www.fastcompany.com/3030723/ibms-watson-is-ready-to-see-you-now-in-yourdermatologists-office. Accessed 10 January 2018
  63. 63.
    G. Waldhoff, C. Curdt, D. Hoffmeister, G. Bareth, Analysis of multitemporal and multisensor remote sensing data for crop rotation mapping. Int. Arch. Photogrammetry Remote Sensing Spat. Inf. Sci. 25(1), 177–182 (2012)Google Scholar
  64. 64.
    Y. Wang, L. Kung, T.A. Byrd, Big data analytics: understanding its capabilities and potential benefits for healthcare organizations. Technol. Forecast. Soc. Change 126, 3–13 (2018)CrossRefGoogle Scholar
  65. 65.
    Y. Wang, L. Kung, C. Ting, T.A. Byrd, in 2015 48th Hawaii International Conference. Beyond a technical perspective: understanding big data capabilities in health care. System Sciences (HICSS) (IEEE, 2015), pp. 3044–3053Google Scholar
  66. 66.
    H.J. Watson, Tutorial: big data analytics: concepts, technologies, and applications. Commun. Assoc. Inf. Syst. 34(1), 1247–1268 (2014)Google Scholar
  67. 67.
    P. Weill, S. Woerner, Thriving in an increasingly digital ecosystem. MIT Sloan Manage. Rev. 56(4), 27–34 (2015)Google Scholar
  68. 68.
    Why Walmart Always Stocks Up On Strawberry Pop-Tarts Before a Hurricane (2017). [online] August (2014) http://www.countryliving.com/food-drinks/a44550/walmart-strawberry-pop-tarts-before-hurricane/. Accessed 10 January 2018
  69. 69.
    S. Wolfert, L. Ge, C. Verdouw, M.J. Bogaardt, Big data in smart farming—a review. Agric. Syst. 153, 69–80 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Buckingham Business SchoolUniversity of BuckinghamBuckinghamUK
  2. 2.Aston Business SchoolAston UniversityBirminghamUK

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