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Towards Differentiating Business Intelligence, Big Data, Data Analytics and Knowledge Discovery

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Innovations in Enterprise Information Systems Management and Engineering (ERP Future 2016)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 285))

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Abstract

Confusion, ambiguity and misunderstanding of the concepts and terminology regarding different approaches concerned with analysing massive data sets (Business Intelligence, Big Data, Data Analytics and Knowledge Discovery) was identified as a significant issue faced by many academics, fellow researchers, industry professionals and domain experts. In that context, a need to critically evaluate these concept and approaches focusing on their similarities, differences and relationships was identified as useful for further research and industry professionals. In this position paper, we critically review these four approaches and produce a framework, which provides visual representation of the relationship between them to effectively support their identification and easier differentiation.

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References

  1. Dedić, N., Stanier, C.: Measuring the success of changes to existing business intelligence solutions to improve business intelligence reporting. In: Tjoa, A.M., Xu, L.D., Raffai, M., Novak, N.M. (eds.) CONFENIS 2016. LNBIP, vol. 268, pp. 225–236. Springer, Cham (2016). doi:10.1007/978-3-319-49944-4_17

    Chapter  Google Scholar 

  2. Brannon, N.: Business Intelligence and E-Discovery. Intellect. Property Technol. Law J. 22(7), 1–5 (2010)

    Google Scholar 

  3. Alexander, A.: Case Studies: Business intelligence. Accounting Today, p. 32, June 2014

    Google Scholar 

  4. Marchand, M., Raymond, L.: Researching performance measurement systems: An information systems perspective. Int. J. Oper. Prod. Manage. 28(7), 663–686 (2008)

    Article  Google Scholar 

  5. Thamir, A., Poulis, E.: Business intelligence capabilities and implementation strategies. Int. J. Global Bus. 8(1), 34–45 (2015)

    Google Scholar 

  6. Olszak, C.M., Ziemba, E.: Business Intelligence Systems in the holistic infrastructure development supporting decision-making in organisations. Interdisc. J. Inf. Knowl. Manage. 1, 47–58 (2006)

    Google Scholar 

  7. Popovič, A., Turk, T., Jaklič, J.: Conceptual model of business value of business intelligence systems. Manage. J. Contemp. Manage. 15(1), 5–29 (2010)

    Google Scholar 

  8. Sandu, D.I.: Operational and real-time Business Intelligence. Informatica Economic XII(4), 33–36 (2008)

    Google Scholar 

  9. American Institute of CPAs. (2015). Business Intelligence. http://www.aicpa.org/INTERESTAREAS/INFORMATIONTECHNOLOGY/RESOURCES/BUSINESSINTELLIGENCE/Pages/default.aspx. Accessed 27 Mar 2015

  10. Kurniawan, Y., Gunawan, A., Kurnia, S.G.: Application of business intelligence to support marketing strategies: a case study approach. J. Theor. Appl. Inf. Technol. 64(1), 214 (2014)

    Google Scholar 

  11. Obeidat, M., et al.: Business intelligence technology, applications, and trends. Int. Manage. Rev. 11(2), 47–56 (2015)

    Google Scholar 

  12. Anadiotis, G.: Agile business intelligence: reshaping the landscape, p. 3 (2013)

    Google Scholar 

  13. Chaudhuri, S., Dayal, U., Narasayya, V.: An overview of business intelligence technology. Commun. ACM 55(8), 88–98 (2011)

    Article  Google Scholar 

  14. Runkler, T.A.: Data Analytics: Models and Algorithms for Intelligent Data Analysis, 1st edn. Springer Science & Business Media, Wiesbaden, Germany (2012)

    Book  MATH  Google Scholar 

  15. Ridge, E.: Guerrilla Analytics: A Practical Approach to Working with Data. Morgan Kaufmann, Waltham (2014)

    Google Scholar 

  16. Russom, P.: TDWI Best Practices Report: Big Data Analytics (2011)

    Google Scholar 

  17. Lussier, R.N., Hendon, J.R.: Fundamentals of Human Resource Management: Functions, Applications, Skill Development, 1st edn. SAGE Publications, Los Angeles (2016)

    Google Scholar 

  18. Fadairo, S.A., Williams, R., Maggio, E.: Using data analytics for oversight and efficiency. J. Gov. Financ. Manage. 64(2), 18 (2015)

    Google Scholar 

  19. Henry, R., Venkatraman, S.: Big Data analytics: the next big learning opportunity. Acad. Inf. Manage. Sci. J. 18(2), 17–29 (2015)

    Google Scholar 

  20. Belle, A., Thiagarajan, R., Soroushmehr, S.M.R., Navidi, F., Beard, D.A., Najarian, K.: Big Data analytics in healthcare. BioMed Res. Int., 1–16 (2015). http://doi.org/10.1155/2015/370194

  21. Cárdenas, A.A., Manadhata, P.K., Rajan, S.P.: Big Data analytics for security. IEEE Secur. Priv. 11(6), 74–76 (2013)

    Article  Google Scholar 

  22. Gerard, G., Haas, M., Pentland, A.: Big Data and management. Acad. Manag. J. 57(2), 321–326 (2014)

    Article  Google Scholar 

  23. Barton, A.: Big Data. J. Nursing Educ. 55(3), 123–124 (2016). http://doi.org/10.3928/01484834-20160216-01

  24. Wu, X., Zhu, X., Wu, G.-Q., Ding, W.: Data mining with Big Data. IEEE Trans. Knowl. Data Eng. 26(1), 97–107 (2014)

    Article  Google Scholar 

  25. Chan, J.O.: An architecture for Big Data analytics. Commun. IIMA 13(2), 1 (2013)

    MathSciNet  Google Scholar 

  26. Cao, M., Chychyla, R., Stewart, T.: Big Data analytics in financial statement audits. Account. Horiz. 29(2), 423 (2015). http://doi.org/10.2308/acch-51068

  27. Lokhande, S., Khare, N.: An outlook on Big Data and Big Data analytics. Int. J. Comput. Appl. 124(11), 37–41 (2015). http://doi.org/10.5120/ijca2015905658

  28. IBM. The Four V’s of Big Data (2016). http://www.ibmbigdatahub.com/infographic/four-vs-big-data. Accessed 13 Apr 2016

  29. Tsai, C.-W., Lai, C.-F., Chao, H., Vasilakos, A.: Big Data analytics: a survey. J. Big Data 2(1), 1–32 (2015). http://doi.org/10.1186/s40537-015-0030-3

  30. Metz, S.: Big Data. Sci. Teach. 82(5), 6 (2015)

    Google Scholar 

  31. National Institutes of Health. What is Big Data? (2016). https://datascience.nih.gov/bd2k/about/what. Accessed 13 Apr 2016

  32. Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets, 1st edn. Cambrige University Press, Cambrige (2011)

    Book  Google Scholar 

  33. Nicol, D.: Mobile Strategy: How Your Company Can Win by Embracing Mobile Technologies, 1st edn. IBM Press, Boston (2013)

    Google Scholar 

  34. Dedić, N., Stanier, C.: An Evaluation of the challenges of multilingualism in data warehouse development. In: Proceedings of the 18th International Conference on Enterprise Information Systems, vol. 1, pp. 196–206 (2016)

    Google Scholar 

  35. Esfandiari, N., Babavaliana, M.R., Amir-Masoud, E.M., Tabarb, V.K.: Knowledge discovery in medicine: current issue and future trend. Expert Syst. Appl. 41(9), 4434–4463 (2014). http://doi.org/10.1016/j.eswa.2014.01.011

  36. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: Knowledge discovery and data mining: towards a unifying framework. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, pp. 82–88. AAAI Press (1996)

    Google Scholar 

  37. Chen, M.-S., Han, J., Yu, P.: Data mining: an overview from a database perspective. IEEE Trans. Knowl. Data Eng. 8(6), 866–883 (1996). http://doi.org/10.1109/69.553155

  38. Cortez, P., Santos, M.F.: Knowledge discovery and business intelligence. Expert Syst. 30(4), 283–284 (2013)

    Article  Google Scholar 

  39. Koua, E.L., Kraak, M.-J.: Geovisualization to support the exploration of large health and demographic survey data. Int. J. Health Geographics 3(12), 13 (2004). http://doi.org/10.1186/1476-072X-3-12

  40. Fred, A.: “Preface.” Preface. In: International Conference on Knowledge Discovery and Information Retrieval. Madeira, Portugal (2009)

    Google Scholar 

  41. Aradau, C., Van Munster, R.: Politics of Catastrophe: Genealogies of the Unknown. Routledge, Chippenham (2011). http://www.kdir.ic3k.org/

  42. Kimball, R., Margy, R., Thornthwaite, W., Mundy, J., Becker, B.: The Data Warehouse Lifecycle Toolkit, 2nd edn. Wiley, Indianapolis (2008)

    Google Scholar 

  43. Inmon, B.W.: Building the Data Warehouse, 4th edn. Wiley, Indianapolis (2005)

    Google Scholar 

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Authors and Affiliations

  1. Faculty of Computing, Engineering and Sciences, Staffordshire University, College Road, Stoke-on-Trent, ST4 2DE, UK

    Nedim Dedić & Clare Stanier

Authors
  1. Nedim Dedić
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  2. Clare Stanier
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Corresponding author

Correspondence to Nedim Dedić .

Editor information

Editors and Affiliations

  1. Andrassy University Budapest , Budapest, Hungary

    Felix Piazolo

  2. Software Competence Center Hagenberg , Hagenberg, Austria

    Verena Geist

  3. Munich University of Applied Sciences , Munich, Germany

    Lars Brehm

  4. Munich University of Applied Sciences , Munich, Germany

    Rainer Schmidt

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Dedić, N., Stanier, C. (2017). Towards Differentiating Business Intelligence, Big Data, Data Analytics and Knowledge Discovery. In: Piazolo, F., Geist, V., Brehm, L., Schmidt, R. (eds) Innovations in Enterprise Information Systems Management and Engineering. ERP Future 2016. Lecture Notes in Business Information Processing, vol 285. Springer, Cham. https://doi.org/10.1007/978-3-319-58801-8_10

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  • DOI: https://doi.org/10.1007/978-3-319-58801-8_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58800-1

  • Online ISBN: 978-3-319-58801-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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