Figure 4. Sharing experience across teams

The experiences of multiple teams in similar situations can be successfully identified, understood, and learned upon with the help of BDA. For example, the effects of using multiple communication marketing campaigns in multiple markets can be compared.

There is though a limitation of using BDA to support learning between the teams. It works well in a highly repetitive processes, where data on similar situations are easily obtainable, as for instance sales, or mass production. If there are not enough similar cases, or if the data variety to explain a cases is too high, BDA cannot adequately provide insight.

3.6 BDA support the interhierarchical learning processes and reduce the number of the hierarchical recursion levels.

Figure 5. Understanding the drivers

The BDA is used by the higher levels in two ways: First, by elaborating the feedbacks of the lower structural recursion levels, it can fine-tune the activities, guiding to the desired results. Secondly, it can use BDA to better understand the needs, processes and relations at lower levels to propose solutions that provide value added for all the subjects, affected by the organizations. The higher capacity to manage variety also reduces the need for hierarchy and allows structural recursion. In some cases, the automated guiding systems can entirely eliminate the need for intermediaries between the consumer and provider on a global scale.

1. Addo-Tenkorang, R., & Helo, P. T. (2016). Big data applications in operations/supply-chain management: A literature review. [Review]. Computers & Industrial Engineering, 101, 528–543. doi: 10.1016/j.cie.2016.09.023.

2. Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment? [Article]. International Journal of Production Economics, 182, 113–131. doi: 10.1016/j.ijpe.2016.08.018.

3. Argote, L., & Miron-Spektor, E. (2011). Organizational Learning: From Experience to Knowledge. [Article]. Organization Science, 22(5), 1123–1137. doi: 10.1287/orsc.1100.0621.

4. Ashby, W. R. (1964). An Introduction to Cybernetics. London: Methuen & Co Ltd.

5. Beer, S. (1979). The Heart of Enterprise. Chichester: Willey.

6. Beer, S. (1981). Brain of the Firm (2nd ed.). Chichester: Wiley.

7. Beer, S. (1985). Diagnosing the system for organisation. Chrichester: John Wiley.

8. Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016). Social big data: Recent achievements and new challenges. [Article]. Information Fusion, 28, 45–59. doi: 10.1016/j.inffus.2015.08.005

9. Bellomo, N., Clarke, D., Gibelli, L., Townsend, P., & Vreugdenhil, B. J. (2016). Human behaviours in evacuation crowd dynamics: From modelling to "big data" toward crisis management. [Review]. Physics of Life Reviews, 18, 1-21. doi: 10.1016/j.plrev.2016.05.014

10. Conant, R., & Ashby, W. R. (1970). Every good regulator of a system must be a model of that system. Intern. J. of Systems Science 1(2), 89–97.

11. Espejo, R. (1993). Management of Complexity in Problem Solving. In R. Espejo & M. Schwaninger (Eds.), Organizational Fitness: Corporate Effectiveness through management cybernetics (pp. 67–90). Frankfurt and New York: Campus Verlag.

12. Espejo, R., Bowling, D., & Hoverstadt, P. (1999). The viable system model and the Viplan software. [Article]. Kybernetes, 28(6–7), 661–678. doi: 10.1108/03684929910282944

13. Espejo, R., & Reyes, A. (2011). Organizational Systems: Managing Complexity with the Viable System Model. Heidelberg: Springer.

14. Espejo, R., Schuhmann, M., Schwaninger, M., & Bilello, H. (1996). Organizational Transformation and Learning. Chichester: Wiley.

15. Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. [Article]. Information & Management, 53(8), 1049–1064. doi: 10.1016/j.im.2016.07.004

16. Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of "big data" on cloud computing: Review and open research issues. [Article]. Information Systems, 47, 98-115. doi: 10.1016/j.is.2014.07.006

17. Kimball, R. (2002). The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling: Wiley.

18. Koskinen, K. U. (2012). Problem absorption as an organizational learning mechanism in project-based companies: Process thinking perspective. [Article]. International Journal of Project Management, 30(3), 308–316. doi: 10.1016/j.ijproman.2011.08.008