How to Think Like a Data Scientist: Application of a Variable Order Markov Model to Indicators Management

  • Gustavo IllescasEmail author
  • Mariano Martínez
  • Arturo Mora-Soto
  • Jose Roberto Cantú-González
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 405)


The growing demand for specialists in analyzing large volumes of data has led to an emerging profile of knowledge managers known as data scientists. How to address the different and complex scenarios with mathematical methods makes a difference when to apply them successfully in a dynamic environment such as the management indicators. For this reason, the authors present in this article a case study of prognostic indicators, developed in the field of finance, making use of mathematical Markov model which has prototyped in an abstract technological implementation with the capabilities to implement cases in other contexts. The purpose of the case study is to verify if the different levels of analysis of the Markov model provide knowledge to the prognosis by indicators while the application of the proposed methodology is shown. Thus, this work introduces to the threshold of a methodology that leads to one of the ways on how to think like a data scientist.


Data scientist Markov chains Indicators Knowledge management Forecast 


  1. 1.
    Illescas, G., Sanchez-Segura, M., Canziani, G.: Comprobación de métodos de pronóstico de indicadores dentro de la gestión del conocimiento organizacional. 3er Congreso Internacional de Mejora de Procesos Software (CIMPS 2014). Centro de Investigación en Matemáticas (CIMAT, Zacatecas A.C.). Octubre 2014, Zacatecas, México (2014)Google Scholar
  2. 2.
    Begleiter, R., El-Yaniv, R., Yona, G.: On prediction using variable order Markov models. J. Artif. Intell. Res. 22, 385–421 (2004)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Cooper, C.: Analytics and big data—reflections from the Teradata Universe conference 2012. Blog post. Accessed 27 April 2012
  4. 4.
    Kaplan, R., Norton, D.: Using the balanced scorecard as a strategic management system. Harv. Bus. Rev. (1996)Google Scholar
  5. 5.
    Berenson, M., Levine, D.: Estadística Básica en Administración, Conceptos y Aplicaciones. Sexta edición. Prentice Hall Hispanoamericana, S. A. México (1996)Google Scholar
  6. 6.
    Chan, S., Ip, W.: A scorecard—Markov model for new product screening decisions. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University. Industrial Management & Data Systems, vol. 110, No. 7, pp. 971–992. (c) Emerald Group. (2010)Google Scholar
  7. 7.
    Köppen, V., Allgeier, M., Lenz, H.: Balanced Scorecard Simulator—A Tool for Stochastic Business Figures. Part VI, pp. 457–464. Institute of Information Systems, Free University Berlin, D-14195 Berlin, Germany (2007)Google Scholar
  8. 8.
    Blumenberg, S., Hinz, D., Goethe, J.: Enhancing the prognostic power of it BSC with Bayesian belief networks. University, Frankfurt, Germany. In: Proceedings of the 39th Hawaii International Conference on System Sciences. 0-7695-2507-5/06 (c) IEEE (2006)Google Scholar
  9. 9.
    Prada Alonso, S.: Cadenas de Markov en la investigación del genoma (2013).
  10. 10.
    Mascareñas, J.: Procesos estocásticos: introducción. ISSN: 1988–1878. Universidad Complutense de Madrid (2013). Accessed 20 June 2015
  11. 11.
    Illescas G., Sanchez-Segura, M., Canziani, G.: Forecasting methods by indicators within the management of organizational knowledge. Revista Ibérica de Sistemas y Tecnologías de Información, pp. 29 a 41. ISSN: 1646-9895-©AISTI 2015 Edición Nº E3, 03/2015. Associação Ibérica de Sistemas e Tecnologias de Informação. Portugal (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Gustavo Illescas
    • 1
    Email author
  • Mariano Martínez
    • 1
  • Arturo Mora-Soto
    • 2
  • Jose Roberto Cantú-González
    • 3
  1. 1.Faculty of Exact Sciences, Computer Science DepartmentUniversidad Nacional del Centro de la Provincia de Buenos AiresTandilArgentina
  2. 2.Mathematics Research CenterZacatecasMexico
  3. 3.Department of Industrial & Systems EngineeringSystems School, Universidad Autónoma de CoahuilaAcuñaMexico

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