Abstract
One of the most important prerequisites for creating impact with data science is the embedding of data science results in decision-making. One could say that for securing data science impact, data science should start and end with an extensive analysis of the related decision-making. The full embedding of data science in decision-making is often labeled data-driven decision-making (DDDM). This includes the use of data and data science concepts in preparing, processing, executing, and evaluating decisions. In this chapter, we describe the most relevant characteristics of decision-making, which are related to the need for, the form of, and the use of DDDM. Furthermore, we define DDDM, we discuss the most important reasons for applying DDDM, and we introduce the available concepts for the use of DDDM in programmed and nonprogrammed decision-making. We also include a brief description of the link between DDDM and successful data entrepreneurship. We conclude by listing some topics for discussion and further research.
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References
Abelson, R. P., & Levi, A. (1985). Decision making and decision theory. In I. G. Lindsey & E. Aronson (Eds.), Handbook of social psychology (Vol. I, 3rd ed., pp. 231–309). Random House.
Ackoff, R. L. (1979). The future of operational research is past. The Journal of the Operational Research Society, 30(2), 93–104. https://doi.org/10.2307/3009290
Bonabeau, E. (2003, May 1). Don’t trust your gut. Harvard Business Review. Retrieved from https://hbr.org/2003/05/dont-trust-your-gut
Brynjolfsson, E., Hitt, L. M., & Kim, H. H. (2011). Strength in numbers: How does data-driven decision-making affect firm performance? (SSRN Scholarly Paper ID 1819486). Social Science Research Network. https://doi.org/10.2139/ssrn.1819486
Brynjolfsson, E., & McElheran, K. (2016). The rapid adoption of data-driven decision-making. American Economic Review, 106(5), 133–139. https://doi.org/10.1257/aer.p20161016
Buchanan, L., & O’Connell, A. (2006, January 1). A brief history of decision making. Harvard Business Review. Retrieved from https://hbr.org/2006/01/a-brief-history-of-decision-making
Busenitz, L. W., & Barney, J. B. (1997). Differences between entrepreneurs and managers in large organizations: Biases and heuristics in strategic decision-making. Journal of Business Venturing, 12(1), 9–30. https://doi.org/10.1016/S0883-9026(96)00003-1
Cian, E. D. (2017). Actors, decision-making, and institutions in quantitative system modelling. Fondazione Eni Enrico Mattei (FEEM).
Courtney, H., Lovallo, D., & Clarke, C. (2013). Deciding how to decide. Harvard Business Review, 91(11), 62–70.
Cukierski, W., Herman, K., & E., & Kherloplan, A. (2015). The field guide to data science. Booz.
Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: The new science of winning (1st ed.). Harvard Business Review Press.
Donoho, D. (2017). 50 Years of data science. Journal of Computational and Graphical Statistics, 26(4), 745–766. https://doi.org/10.1080/10618600.2017.1384734
Ekstrand, M. D., & Willemsen, M. C. (2016). Behaviorism is not enough: Better recommendations through listening to users. In Proceedings of the 10th ACM Conference on Recommender Systems (pp. 221–224). https://doi.org/10.1145/2959100.2959179
Epstein, J. M., & Axtell, R. (1996). Growing artificial societies: Social science from the bottom up (p. xv, 208). The MIT Press.
Francès, G., Rubio-Campillo, X., Lancelotti, C., & Madella, M. (2015). Decision making in agent-based models. In N. Bulling (Ed.), Multi-agent systems (pp. 370–378). Springer International Publishing. https://doi.org/10.1007/978-3-319-17130-2_25
Gladwell, M. (2005). Blink: The power of thinking without thinking (p. viii, 277). Little, Brown and Co.
Grove, W. M., & Meehl, P. E. (1996). Comparative efficiency of informal (subjective, impressionistic) and formal (mechanical, algorithmic) prediction procedures: The clinical–statistical controversy. Psychology, Public Policy, and Law, 2(2), 293–323. https://doi.org/10.1037/1076-8971.2.2.293
Hammond, K. R., Hamm, R. M., Grassia, J., & Pearson, T. (1987). Direct comparison of the efficacy of intuitive and analytical cognition in expert judgment. IEEE Transactions on Systems, Man, and Cybernetics, 17(5), 753–770. https://doi.org/10.1109/TSMC.1987.6499282
Harries, C. (2003). Correspondence to what? Coherence to what? What is good scenario-based decision making? Technological Forecasting and Social Change, 70(8), 797–817. https://doi.org/10.1016/S0040-1625(03)00023-4
He, C., Parra, D., & Verbert, K. (2016). Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities. Expert Systems with Applications, 56, 9–27. https://doi.org/10.1016/j.eswa.2016.02.013
Hwang, M. I., & Lin, J. W. (1999). Information dimension, information overload and decision quality. Journal of Information Science. https://doi.org/10.1177/016555159902500305
Jameson, A., Willemsen, M. C., Felfernig, A., de Gemmis, M., Lops, P., Semeraro, G., & Chen, L. (2015). Human decision making and recommender systems. In F. Ricci, L. Rokach, & B. Shapira (Eds.), Recommender systems handbook (pp. 611–648). Springer US. https://doi.org/10.1007/978-1-4899-7637-6_18
Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
Kaish, S., & Gilad, B. (1991). Characteristics of opportunities search of entrepreneurs versus executives: Sources, interests, general alertness. Journal of business venturing, 6(1), 45–61.
Ketter, W., Peters, M., Collins, J., & Gupta, A. (2016). Competitive benchmarking: An IS research approach to address wicked problems with big data and analytics. MIS Quarterly, 40(4), 1057–1080. https://doi.org/10.25300/MISQ/2016/40.4.12
Knight, F. H. (1921). Risk, uncertainty and profit (Vol. 31). Houghton Mifflin.
Knijnenburg, B. P., Willemsen, M. C., Gantner, Z., Soncu, H., & Newell, C. (2012). Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction, 22(4–5), 441–504. https://doi.org/10.1007/s11257-011-9118-4
Laursen, G. H. N., & Thorlund, J. (2010). Business analytics for managers: Taking business intelligence beyond reporting (1st ed.). John Wiley & Sons.
Liew, A. (2007). Understanding data, information, knowledge and their inter-relationships. Journal of Knowledge Management Practice, 8(2), 1–16.
Loveman, G. W. (2003, May 1). Diamonds in the data mine. Harvard Business Review. Retrieved from https://hbr.org/2003/05/diamonds-in-the-data-mine
McNee, S. M., Riedl, J., & Konstan, J. A. (2006). Being accurate is not enough: How accuracy metrics have hurt recommender systems. In CHI’06 Extended Abstracts on Human Factors in Computing Systems (pp. 1097–1101). https://doi.org/10.1145/1125451.1125659
Mintzberg, H., Raisinghani, D., & Théorêt, A. (1976). The structure of “unstructured” decision processes. Administrative Science Quarterly, 21(2), 246–275. https://doi.org/10.2307/2392045
Newell, A., & Simon, H. A. (1972). Human problem solving (p. xiv, 920). Prentice-Hall.
Parnell, G. S., Bresnick, T., Tani, S. N., & Johnson, E. R. (2013). Handbook of decision analysis. John Wiley & Sons.
Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The adaptive decision maker. Cambridge University Press.
Peter, K., Sebastian, P., Doyne Farmer J., & Stefan, T. (2015) To bail-out or to bail-in? Answers from an agent-based model. Journal of Economic Dynamics and Control 50144–154 S0165188914002097. https://doi.org/10.1016/j.jedc.2014.08.020.
Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big Data, 1(1), 51–59. https://doi.org/10.1089/big.2013.1508
Robbins, S. (1996). Organizational behavior. Prentice Hall.
Sarasvathy, S. D., Dew, N., Velamuri, S. R., & Venkataraman, S. (2010). Three views of entrepreneurial opportunity. In Z. J. Acs & D. B. Audretsch (Eds.), Handbook of entrepreneurship research: An interdisciplinary survey and introduction (pp. 77–96). Springer. https://doi.org/10.1007/978-1-4419-1191-9_4
Scott, S. G., & Bruce, R. A. (1995). Decision-making style: The development and assessment of a new measure. Educational and Psychological Measurement. https://doi.org/10.1177/0013164495055005017
Senge, P. M., Kleiner, A., & Roberts, C. (1994). The fifth discipline fieldbook: Strategies and tools for building a learning organization. .
Shafer, G. (1986). The combination of evidence. International Journal of Intelligent Systems, 1(3), 155–179. https://doi.org/10.1002/int.4550010302
Shane (2003). A general theory of entrepreneurship: The individual-opportunity nexus. Northampton, MA: Edward Elgar Publishing.
Shane, S., & Venkataraman, S. (2000). The promise of entrepreneurship as a field of research. The Academy of Management Review, 25(1), 217–226. https://doi.org/10.2307/259271
Sharma, S. C. (2006). Introductory operation research. Discovery Publishing House.
Shaver, K. G., & Scott, L. R. (1992). Person, Process, Choice: The Psychology of New Venture Creation. Entrepreneurship Theory and Practice, 16(2), 23–46.
Shepherd, D. A., Williams, T. A., & Patzelt, H. (2014). Thinking about entrepreneurial decision making: Review and research agenda. Journal of Management. https://doi.org/10.1177/0149206314541153
Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99–118. https://doi.org/10.2307/1884852
Simon, H. A. (1960). The new science of management decision (p. xii, 50). Harper & Brothers. https://doi.org/10.1037/13978-000
Simon, H. A. (1977). The new science of management decision. Prentice Hall PTR.
Smyth, B., & Cunningham, P. (2017). Running with cases: A CBR approach to running your best marathon. In Case-Based Reasoning Research and Development—25th International Conference, ICCBR 2017, Trondheim, Norway, June 26–28, 2017, Proceedings (pp. 360–374). https://doi.org/10.1007/978-3-319-61030-6_25
Smyth, B., & Willemsen, M. C. (2020). Predicting the personal-best times of speed skaters using case-based reasoning. In Proceedings ICCBR 2020.
Snijders, C., Tazelaar, F., & Batenburg, R. (2003). Electronic decision support for procurement management: Evidence on whether computers can make better procurement decisions. Journal of Purchasing and Supply Management, 9(5), 191–198. https://doi.org/10.1016/j.pursup.2003.09.001
Spetzler, C. S. (2016). Decision quality: Value creation from better business decisions. Retrieved from http://www.dawsonera.com/depp/reader/protected/external/AbstractView/S9781119144687
Starke, A., Willemsen, M., & Snijders, C. (2017). Effective user interface designs to increase energy-efficient behavior in a Rasch-based energy recommender system. In Proceedings of the 11th ACM Conference on Recommender Systems (pp. 65–73). https://doi.org/10.1145/3109859.3109902
Starke, A. D., Willemsen, M. C., & Snijders, C. C. P. (2020). Beyond “one-size-fits-all” platforms: Applying Campbell’s paradigm to test personalized energy advice in the Netherlands. Energy Research & Social Science, 59, 101311. https://doi.org/10.1016/j.erss.2019.101311
Tazelaar, F., & Snijders, C. (2004). The myth of purchasing professionals’ expertise. More evidence on whether computers can make better procurement decisions. Journal of Purchasing and Supply Management, 10(4), 211–222. https://doi.org/10.1016/j.pursup.2004.11.004
Winston, W. L., & Goldberg, J. B. (2004). Operations research: Applications and algorithms (Vol. 3). Thomson/Brooks/Cole.
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Buijsse, R., Willemsen, M., Snijders, C. (2023). Data-Driven Decision-Making. In: Liebregts, W., van den Heuvel, WJ., van den Born, A. (eds) Data Science for Entrepreneurship. Classroom Companion: Business. Springer, Cham. https://doi.org/10.1007/978-3-031-19554-9_11
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