Architecture for Business Intelligence Design on the IT Service Management Scope

  • C. P. Marin Ortega
  • C. P. Pérez Lorences
  • -Ing. Habil J. Marx-Gómez
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 55)

Abstract

In the present research we propose new business intelligence architecture to support the IT balanced scorecard cascade based on the integration of business and technological domains for the IT service management. This paper presents some preliminary results on the state of the art analysis on the topics: IT BSC, business intelligence, and aggregation methods based on the fuzzy logic operators to build management indicators. The main contributions are: new architecture for business intelligence design, an aggregation method to design new indicators based on the statistic and compensatory fuzzy logic approach taking into account as sources the indicators defined in the COBIT and ITIL frameworks. As the first result we define a new IT BSC for the Cuban enterprise.

Keywords

Business intelligence IT governance IT services management balanced scorecard IT balanced scorecard COBIT ITIL Aggregation methods 

References

  1. 1.
    Lucio-Nieto, T., et al.: Implementing an IT service information management framework: the case of COTEMAR. Int. J. Inf. Manage. 32, 589–594 (2012)CrossRefGoogle Scholar
  2. 2.
    Valiente, M.-C., Garcia-Barriocanal, E., Sicilia, M.-A.: Applying an ontology approach to IT service management for business-IT integration. Knowl. Based Syst. 28, 76–87 (2012)CrossRefGoogle Scholar
  3. 3.
    ITGI: COBIT4.1. Control objectives for information and related technology. http://www.itgi.org/COBIT.htm (2007)
  4. 4.
    ITIL: Information technology infrastructure library V.3. http://www.itil.co.uk (2007)
  5. 5.
    Espin Andrade, R.A., et al.: Compensatory logic: a fuzzy formative model for decision making. In: Congress of International Association for Fuzzy-Set Management and Economy, León, España (2003)Google Scholar
  6. 6.
    Gold, C.: Total Quality Management in Information Services – IS Measures: A Balancing Act. Research Note Ernst and Young Center for Information Technology and Strategy, Boston (1992) Google Scholar
  7. 7.
    Willcocks, L.: Information Management. The Evaluation of Information Systems Investments. Chapman and Hall, London (1995)Google Scholar
  8. 8.
    Van Grembergen, W., Van Bruggen, R.: Measuring and improving corporate information technology through the balanced scorecard technique. In: Fourth European Conference on the Evaluation Of Information Technology, Deflt (1997)Google Scholar
  9. 9.
    Van Grembergen, W., Timmerman, D.: Monitoring the IT process through the balanced scorecard. In: 9th Information Resources Management (IRMA) International Conference, Boston (1998)Google Scholar
  10. 10.
    Van Grembergen, W.: The balanced scorecard and IT governance. Inf.Syst. Control J. 2, 40–43 (2000)Google Scholar
  11. 11.
    Van Grembergen, W., De Haes, S.: The IT balanced scorecard as a framework for enterprise governance of IT. In: Enterprise governance of information technology. Achieving strategic alignment and value. Springer, New York (2009)Google Scholar
  12. 12.
    Van der Zee, J.: Alignment is not enough: integrating business and IT management with the balanced scorecard. In: 1st conference on the IT balanced scorecard, Antwerp (1999)Google Scholar
  13. 13.
    Van Grembergen, W., Saull, R., De Haes, S.: Linking the IT balanced scorecard to the business objectives at a major canadian financial group Google Scholar
  14. 14.
    Fatemeh Moghimi, C.Z.: A decision-making model to choose business intelligence platforms for organizations. In: Third International symposium on intelligent information technology application, IEEE (2009)Google Scholar
  15. 15.
    Ying Wang, Z.L.: Study on port business intelligence system combined with business performance management. In: Second international conference on future information technology and management engineering, IEEE (2009)Google Scholar
  16. 16.
    Merigó, J.M., Gil-Lafuente, A.M.: Fuzzy induced generalized aggregation operators and its application in multi-person decision making. Expert Syst. Appl. 38(8), 9761–9772 (2011)CrossRefGoogle Scholar
  17. 17.
    Wei, G., Zhao, X.: Some induced correlated aggregating operators with intuitionistic fuzzy information and their application to multiple attribute group decision making. Expert Syst. Appl. 39(2), 2026–2034 (2012)CrossRefGoogle Scholar
  18. 18.
    Zandi, F., Tavana, M.: A fuzzy group multi-criteria enterprise architecture framework selection model. Expert Syst. Appl. 39(1), 1165–1173 (2012)CrossRefGoogle Scholar
  19. 19.
    Cebeci, U.: Fuzzy AHP-based decision support system for selecting ERP systems in textile industry by using balanced scorecard. Expert Syst. Appl. 36(5), 8900–8909 (2009)CrossRefGoogle Scholar
  20. 20.
    Yüksel, I., Dağdeviren, M.: Using the fuzzy analytic network process (ANP) for balanced scorecard (BSC): a case study for a manufacturing firm. Expert Syst. Appl. 37(2), 1270–1278 (2010)CrossRefGoogle Scholar
  21. 21.
    Grabisch, M., et al.: Aggregation functions: construction methods, conjunctive, disjunctive and mixed classes. Inf. Sci. 181(1), 23–43 (2011)MathSciNetCrossRefMATHGoogle Scholar
  22. 22.
    Fields, E.B., Okudan, G.E., Ashour, O.M.: Rank aggregation methods comparison: a case for triage prioritization. Expert Syst. Appl. 40(4), 1305–1311 (2013)CrossRefGoogle Scholar
  23. 23.
    Ting, A.: C omparison of different aggregation methods in coupling of the numerical precipitation forecasting and hydrological forecasting. Procedia Eng. 28, 786–790 (2012)CrossRefGoogle Scholar
  24. 24.
    Tsyganok, V.: Investigation of the aggregation effectiveness of expert estimates obtained by the pairwise comparison method. Math. Comput. Model. 52(3–4), 538–544 (2010)MathSciNetCrossRefMATHGoogle Scholar
  25. 25.
    ITGI: COBIT MAPPING: Mapping of ITIL with COBIT 4.1. ITGI (2008)Google Scholar
  26. 26.
    Balanced Scorecard Institute: What is the balanced scorecard? http://www.balancedscorecard.org/BSCResources/AbouttheBalancedScorecard/tabid/55/Default.aspx (2010) Accessed 04 Aug 2010
  27. 27.
    Group, G.: The gartner glossary of information technology acronyms and terms. http://www.gartner.com/6_help/glossary/Gartner_IT_Glossary.pdf (2004)
  28. 28.
    Nardo, M., et al.: Handbook on constructing composite indicators: methodology and user guide. OECD Statistics Working Paper, STD/DOC, Paris (2005)Google Scholar
  29. 29.
    Little, R., Rubin, D.: Statistical analysis with missing data. John Wiley, New York (2002)Google Scholar
  30. 30.
    Pearson, K.: On lines and planes of closest fit to a system of points in space. Philos. Mag. 2, 559–572 (1901)Google Scholar
  31. 31.
    Hotelling, H.: Analysis of a complex of statistical variables into principal components. J. Edu. Psychol. 24, 417–441 (1993)Google Scholar
  32. 32.
    Kaiser, H. The varimax criterion for analytic rotation in factor analysis. Psychometrika 23, 187–200 (1958)Google Scholar
  33. 33.
    Cronbach, L. Coefficient alpha and the internal structure of tests. Psychometrika 16, 297–334 (1951)Google Scholar
  34. 34.
    Dubois, D., Prade, H. Review of fuzzy set aggregation connectives. Inf. Sci. 36, 85–121 (1985)Google Scholar
  35. 35.
    Ceruto Cordovés, T, Rosete Suárez, A., Espín Andrade, R. A. Descubrimiento de predicados a través de la búsqueda metaheurística (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • C. P. Marin Ortega
    • 1
  • C. P. Pérez Lorences
    • 1
  • -Ing. Habil J. Marx-Gómez
    • 2
  1. 1.Department of Industrial EngineeringCentral University of Las VillasSanta ClaraCuba
  2. 2.Department of Computing Science, Business Information Systems I/VLBACarl von Ossietzky University OldenburgOldenburgGermany

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