Skip to main content
SpringerLink
Log in

Big Data Analytics: Applications, Prospects and Challenges

  • Chapter
  • First Online:
Mobile Big Data

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 10))

Abstract

In the era of the fourth industrial revolution (Industry 4.0), big data has major impact on businesses, since the revolution of networks, platforms, people and digital technology have changed the determinants of firms’ innovation and competitiveness. An ongoing huge hype for big data has been gained from academics and professionals, since big data analytics leads to valuable knowledge and promotion of innovative activity of enterprises and organizations, transforming economies in local, national and international level. In that context, data science is defined as the collection of fundamental principles that promote information and knowledge gaining from data. The techniques and applications that are used help to analyze critical data to support organizations in understanding their environment and in taking better decisions on time. Nowadays, the tremendous increase of data through the Internet of Things (continuous increase of connected devices, sensors and smartphones) has contributed to the rise of a “data-driven” era, where big data analytics are used in every sector (agriculture, health, energy and infrastructure, economics and insurance, sports, food and transportation) and every world economy. The growing expansion of available data is a recognized trend worldwide, while valuable knowledge arising from the information come from data analysis processes. In that context, the bulk of organizations are collecting, storing and analyzing data for strategic business decisions leading to valuable knowledge. The ability to manage, analyze and act on data (“data-driven decision systems”) is very important to organizations and is characterized as a significant asset. The prospects of big data analytics are important and the benefits for data-driven organizations are significant determinants for competitiveness and innovation performance. However, there are considerable obstacles to adopt data-driven approach and get valuable knowledge through big data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. United Nations: A world that counts. Mobilizing the data revolution for sustainable development. United Nations, New York (2014)

    Google Scholar 

  2. OECD: Data-driven innovation big data for growth and well-being: big data for growth and well-being. OECD Publishing (2015)

    Google Scholar 

  3. Chen, H., Chiang, R., Storey, V.C.: Business intelligence and analytics: from big data to big impact. Miss. Q. 36(4), 1165–1188 (2012)

    Google Scholar 

  4. Provost, F., Fawcett, T.: Data science and its relationship to big data and data-driven decision making. Big Data 1(1), 51–59 (2013)

    Article  Google Scholar 

  5. Economist, T.: Data is giving rise to a new economy. In: The Economist, 05 Jun 2017. https://www.economist.com/news/briefing/21721634-how-it-shaping-up-data-giving-rise-new-economy. Accessed 06 Oct 2017

  6. Sivarajah, U., Kamal, M.M., Irani, Z., Weerakkody, V.: Critical analysis of big data challenges and analytical methods. J. Bus. Res. 70, 263–286 (2017)

    Article  Google Scholar 

  7. Manyika, J., et al.: Big data: the next frontier for innovation, competition, and productivity (2011)

    Google Scholar 

  8. Gantz, J., Reinsel, D.: Extracting Value from Chaos, IDC (2011)

    Google Scholar 

  9. Friendly, M.: The golden age of statistical graphics. Stat. Sci. 23(4), 502–535 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  10. Power, D.J.: Understanding data-driven decision support systems. Inf. Syst. Manag. 25(2), 149–154 (2008)

    Article  Google Scholar 

  11. Cebr: Data equity: unlocking the value of big data Report for SAS, April (2012). https://www.cebr.com/wp-content/uploads/2013/03/1733_Cebr_Value-of-Data-Equity_report.pdf. Accessed 06 Nov 2017

  12. Website. https://www.news.microsoft.com/europe/2016/04/20/go-bigger-with-big-data/sm.0008u654e19yueh0qs514ckroeww1/XmqRHQB1Gcmde4yb.97. Accessed 15 Jun 2017

  13. McAfee, A., Brynjolfsson, E.: Big data: the management revolution. Harv. Bus. Rev. 90(10) 60–66, 68, 128 (2012)

    Google Scholar 

  14. Burstein, F., Holsapple, C.: Handbook on Decision Support Systems 1: Basic Themes. Springer Science & Business Media (2008)

    Google Scholar 

  15. Larson, D., Chang, V.: A review and future direction of agile, business intelligence, analytics and data science—Science Direct. Int. J. Inf. Manage. 36(5), 700–710 (2016)

    Article  Google Scholar 

  16. Davenport, T.: Big Data at Work: Dispelling the Myths. Harvard Business Review Press, Uncovering the Opportunities (2014)

    Book  Google Scholar 

  17. Gandomi, A., Haider, M.: Beyond the hype: big data concepts, methods, and analytics. Int. J. Inf. Manage. 35(2), 137–144 (2015)

    Article  Google Scholar 

  18. How to leverage the power of prescriptive analytics to maximize the ROI. In: IBM Big Data and Analytics Hub. http://www.ibmbigdatahub.com/blog/how-leverage-power-prescriptive-analytics-maximize-roi. Accessed 16 Jun 2017

  19. Demirkan, H., Delen, D.: Leveraging the capabilities of service-oriented decision support systems: putting analytics and big data in cloud. Decis. Support Syst. 55(1), 412–421 (2013)

    Article  Google Scholar 

  20. Lodefalk, M.: Servicification of manufacturing—evidence from Sweden. Int. J. Econom. Bus. Res. 6(1), 87 (2013)

    Article  Google Scholar 

  21. Davenport, T.H., Barth, P., Bean, R.: How ‘big data’ is different. MIT Sloan Manag. Rev 54(1), 22–24 (2012)

    Google Scholar 

  22. Baesens, B.: Analytics in a Big Data World: The Essential Guide to Data Science and its Applications. Wiley (2014)

    Google Scholar 

  23. Big Data Analytics for Security—IEEE Xplore Document. http://ieeexplore.ieee.org/abstract/document/6682971/?reload=true. Accessed 18 Jun 2017

  24. Wang, G., Gunasekaran, A., Ngai, E.W.T., Papadopoulos, T.: Big data analytics in logistics and supply chain management: certain investigations for research and applications—science direct. http://www.sciencedirect.com/science/article/pii/S0925527316300056?via%3Dihub. Accessed 18 Jun 2017

  25. GE’s big bet on data and analytics|MIT sloan management review. In: MIT Sloan Management Review. http://sloanreview.mit.edu/case-study/ge-big-bet-on-data-and-analytics/. Accessed 14 Jun 2017

  26. Analytics 3.0: Harvard Business Review, 01 Dec 2013. https://hbr.org/2013/12/analytics-30. Accessed 21 Jun 2017

  27. Gartner Says 8.4 Billion Connected. http://www.gartner.com/newsroom/id/3598917. Accessed 21 Jun 2017

  28. Davenport, T.: Analytics and IT new opportunity for CIOs. In: Harvard Business Review (2016)

    Google Scholar 

  29. Double-digit growth forecast for the worldwide big data and business analytics market through 2020 led by banking and manufacturing investments, according to IDC. http://www.idc.com, http://www.idc.com/getdoc.jsp?containerId=prUS41826116. Accessed 21 Jun 2017

  30. Brynjolfsson, E., Hitt, L.M., Kim, H.H.: Strength in numbers: how does data-driven decision making affect firm performance?. SSRN Electron. J.

    Google Scholar 

  31. If your company isn’t good at analytics, it’s not ready for AI. In: Harvard Business Review, 07 Jun 2017. https://www.hbr.org/2017/06/if-your-company-isnt-good-at-analytics-its-not-ready-for-ai. Accessed 22 Jun 2017

  32. Ryan, L.: The Visual Imperative: Creating a Visual Culture of Data Discovery. Morgan Kaufmann (2016)

    Google Scholar 

  33. Lavalle, S., Lesser, E., Shockley, R., Hopkins, M.S., Kruschwitz, N.: Big data, analytics and the path from insights to value. MIT Sloan Manag. Rev. 52(2), 3–22 (2010)

    Google Scholar 

  34. The 2 types of data strategies every company needs. In: Harvard Business Review, 01 May 2017. https://hbr.org/2017/05/whats-your-data-strategy. Accessed 18 Jun 2017

  35. Waller, M.A., Fawcett, S.E.: Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. J. Bus. Logist. 34(2), 77–84 (2013)

    Article  Google Scholar 

  36. Janssen, M., van der Voort, H., Wahyudi, A.: Factors influencing big data decision-making quality. J. Bus. Res. 70, 338–345 (2017)

    Article  Google Scholar 

  37. Chahal, M., et al.: Marketers overestimate consumers’ attitude to data—Marketing Week. In: Marketing Week, 23 Jun 2016. https://www.marketingweek.com/2016/06/23/marketers-overestimate-consumers-attitude-to-data/. Accessed 18 Jun 2017

Download references

Author information

Authors and Affiliations

  1. Department of Economics, University of Crete, Gallos Campus, Rethymno, Crete, 74100, Greece

    Konstantinos Vassakis & Emmanuel Petrakis

  2. Department of Business Administration, Technological Educational Institute of Crete, Agios Nikolaos, Crete, 72100, Greece

    Ioannis Kopanakis

Authors
  1. Konstantinos Vassakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emmanuel Petrakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ioannis Kopanakis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Konstantinos Vassakis .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Nicosia, Nicosia, Cyprus

    Georgios Skourletopoulos

  2. Department of Business Administration, Technological Educational Institute Crete, Agios Nikolaos, Greece

    George Mastorakis

  3. Department of Computer Science, University of Nicosia, Nicosia, Cyprus

    Constandinos X. Mavromoustakis

  4. Department of Computer Science and Engineering, University Politehnica of Bucharest, Bucharest, Romania

    Ciprian Dobre

  5. Department of Informatics Engineering, Technological Educational Institute of Crete, Crete, Greece

    Evangelos Pallis

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Vassakis, K., Petrakis, E., Kopanakis, I. (2018). Big Data Analytics: Applications, Prospects and Challenges. In: Skourletopoulos, G., Mastorakis, G., Mavromoustakis, C., Dobre, C., Pallis, E. (eds) Mobile Big Data. Lecture Notes on Data Engineering and Communications Technologies, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-319-67925-9_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-67925-9_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67924-2

  • Online ISBN: 978-3-319-67925-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation