On Data Driven Organizations and the Necessity of Interpretable Models

  • Tony Lindgren
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 256)


It this paper we investigate data driven organizations in the context of predictive models, we also reflect on the need for interpretability of the predictive models in such a context. By investigating a specific use-case, the maintenance offer from a heavy truck manufacturer, we explore their current situation trying to identify areas that needs change in order to go from the current situation towards a more data driven and agile maintenance offer. The suggestions for improvements are captured in a proposed data driven framework for this type of business. The aim of the paper is that the suggested framework can inspire and start further discussions and investigations into the best practices for creating a data driven organization, in businesses facing similar challenges as in the presented use-case.


Data driven framework Interpretability Organization 


  1. 1.
    Anderson, C., Brown, C.E.: The functions and dysfunctions of hierarchy. Res. Organ. Behav. 30, 55–89 (2010)CrossRefGoogle Scholar
  2. 2.
    Diefenbach, T.: New public management in public sector organizations: the dark sides of managerialistic enlightenment. Public Adm. 87(4), 892–909 (2009)CrossRefGoogle Scholar
  3. 3.
    Hammer, M.: The process audit. Harvard Bus. Rev. 85, 111–119, 122 (2007)Google Scholar
  4. 4.
    Hoggett, P.: New modes of control in the public service. Public Adm. 74(1), 9–32 (1996)CrossRefGoogle Scholar
  5. 5.
    Karp, T.: Unpacking the mysteries of change: mental modelling. J. Change Manage. 5(1), 87–96 (2005)CrossRefGoogle Scholar
  6. 6.
    Lee, M.K., Kusbit, D., Metsky, E., Dabbish, L.: Working with machines: the impact of algorithmic and data-driven management on human workers. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, CHI 2015, pp. 1603–1612. ACM, New York (2015)Google Scholar
  7. 7.
    Lipton, Z.C.: The mythos of model interpretability. CoRR, abs/1606.03490 (2016)Google Scholar
  8. 8.
    Ribeiro, M.T., Singh, S., Guestrin, C.: “why should i trust you?”: explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 1135–1144. ACM, New York (2016)Google Scholar
  9. 9.
    Shearer, C.: The CRISP-DM model: the new blueprint for data mining. J. Data Warehous. 5(4), 13–22 (2000)Google Scholar
  10. 10.
    Srinivasan, V.: The Intelligent Enterprise in the Era of Big Data, 1st edn., Wiley (2017)Google Scholar
  11. 11.
    Tolomei, G., Silvestri, F., Haines, A., Lalmas, M.: Interpretable predictions of tree-based ensembles via actionable feature tweaking. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017, pp. 465–474. ACM, New York (2017)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.Department of Computer and System SciencesStockholm UniversityKistaSweden

Personalised recommendations