Organizations operate in markets that are full of challenges and uncertainties that prompt organizational managers to make decisions in an attempt to ensure the survival of the organization or enhance its performance or profitability. In this endeavor, managers and other leaders in organizations rely on a decision-making process that is firmly grounded in decision theory. Edwards (1954) points out that decision theory is a distinct field that focuses on the factors and processes that guide the choices of various agents. The development of decision theory had its origin in the mid 20th Century when it was an obscure field with little recognition, but it has now grown into a distinct academic subject. Decision theory borrows heavily from several disciplines including psychology, economics, statistics, and social sciences (Gilboa 2011).
Bell, Raiffa, and Tversky (1998) assert that decision theory is comprised of two major branches: normative decision theory and descriptive decision theory. The focus of normative decision theory is the manner in which agents ought to make decisions for them to be rational. On the other hand, descriptive decision theory focuses on actual decisions made by existing agents. Briggs (2014) points out that models of normative decision theory are based on the idea that the best option among multiple options available is the one one that brings about more good than all the other options. In this case, “good” is a measurable entity and refers to the degree to which individuals, groups, or organizations achieve their goals. Therefore, the option that facilitates the achievement of set goals to the greatest extent is the ideal option and, consequently, is desirable. Examples of normative decision theories are the expected utility theory and the probability theory, both of which are quantitative models.
According to Gilboa (2011), the expected utility theory deals with situations whereby agents engage in decision-making without being aware of the potential outcomes of the choices available. Therefore, they are making decisions under the state of uncertainty. Under the premise of this theory, agents tend to settle on the choice that results in the greatest utility. Thus, the expected utility theory is closely linked to the ethical theory of utilitarianism which holds that the most appropriate act is the one that yields maximum utility. According to the expected utility theory, the highest expected utility is determined by multiplying utility and probability for all the possible outcomes and finding the sum of the products. In essence, this choice of maximum utility translates to the option that allows the achievement of goals to the greatest extent. Additionally, decision-making under the expected utility theory not only depends on utility but also risk aversion. Normally, choices with the least risk are the most desirable (Briggs 2014).
The probability theory provides another quantitative model of decision making which is ideal for situations characterized by uncertainty. Consequently, the probability model is appropriate for decision-making in business organizations because businesses operate in a competitive environment that is full of uncertainties regarding future changes in the environment as well as the outcome of decisions. This uncertainty prompts managers to make appropriate assumptions when they are making decisions, with the likelihood of the possible outcomes being a ratio that ranges from 0 to 1. The probability model allows decision makers to make decisions based on their estimate of the probable future. They tend to rely on their intuition, which sometimes is influenced by past experiences. In many instances of uncertainty, the basis of decision-making is subjective probability, which in effect means that they rely on informed guessing (Berger 1985).
Descriptive decision theory is also as important in decision-making as normative decision theory. An examination of the prospect theory provides a valuable insight into the workings of descriptive decision-making. Similar to the expected utility and probability theories, the prospect theory involves decision-making that entails uncertainty but is descriptive rather than normative, meaning that its focus is the modeling of real-life choices. The model relies on behavioral economics and holds that the decisions of individuals are influenced by perceived gains and losses rather than the outcomes of those decisions (Kahneman & Tversky 1979). Additionally, perceived gains are more influential than perceived losses during decision-making. The prospect theory is also an example of a quantitative model that provides descriptions of choices people make based on probability and involving risk (Berger 1985).
There are numerous theories of decision-making, but the ones mentioned above are some of the most important and most studied. Many of the other theories of decision-making are psychological such as the consistency theory, cognitive dissonance theory, self-regulation theory, and the priming model. Others are merely based on rational decision-making, including the multi-attribute choice theory, filter theory, and the source credibility theory (Gilboa 2011).
Data-driven decision making is an approach to decision-making that focuses on the generation of decisions that are based on verifiable data (Fickes, 1998). Advancements in technology are presenting managers and administrators with growing opportunities to gather digital information and leverage it for informed decision-making. The outcome of this change is that managers are relying more on data in decision-making rather than their mere intuition. Business intelligence tools, applications, and methodologies facilitate data-driven decision-making by allowing organizations to collect high-quality data, analyze it effectively, and interpret it correctly. Additionally, these tools allow personnel to convert raw data into actionable information which decision makers can easily interpret through customized dashboards and data visualizations, among others (Provost & Fawcett, 2013).
Education literature played a key role in the formulation of this definition of data-driven decision-making because the concept was first explored by educational scholars as they explored ways of improving student performance. Key among them is Fickes (1998), who argues that techniques of data management can help improve the standards of teaching and learning in schools. Provost and Fawcett (2013), explore the applications of the concept in business organizations and, therefore, helped to frame the definition in a business context.
The adoption of data-driven decision-making is not a straightforward undertaking. It is bound to fail if the organization’s culture does not align with the requirements of the data-driven decision-making process. Consequently, Company XYZ might end up with a trove of useless data if it does not initiate a cultural shift that is conducive to the process. Many organizations have a bureaucratic structure that facilitates a rigid decision-making system. This culture of inflexibility is favored because it enhances efficiency and reduces waste but its downside is that it impairs rapid decision-making, making the organization unresponsive to sudden environmental changes. Middle and low-level managers often cannot engage in rapid decision making in response to real-time data because of excessive red tape that compels them to consult their superiors before making minor decisions. Such an organization is bound to find it challenging to integrate a data-driven decision-making paradigm into its processes and operations because it yields a constant stream of data that the organization needs to be able to utilize rapidly to leverage its competitive position (Tunguz & Bien 2016).
Tunguz and Bien (2016) assert that organizations with a culture of bureaucracy need to decentralize their processes and adopt less formal systems to become more flexible and, subsequently, facilitate the optimal integration of a data-driven decision-making paradigm. The organization’s culture needs to be based more on non-bureaucratic work with minimal formalization of behavior. These changes will promote a workplace democracy that is necessary for data-driven decision making to be effective in helping the company meet its informational needs and, subsequently, transform data into actionable insights.
Another cultural change that is necessary for the successful adoption of a data-driven decision-making process is for the company’s personnel to adopt a more receptive attitude to change. Personnel in organizations tend to get so accustomed to the established way of doing things that they view any disruption of this status quo as a grave inconvenience (Tunguz & Bien 2016). Consequently, Company XYZ needs to take steps that will pave the way for the establishment of a culture of receptiveness to change. For instance, there is a high likelihood that the executives and managers of Company XYZ will demonstrate a reluctance to adopt the changes because they believe their expertise and experience in decision making are sufficient to help the company achieve its goals. As a result, it will take extensive training and explanation to get them to accept greater reliance on data-driven decision making. It is not only senior personnel who will need convincing. Junior employees of Company XYZ will probably not be enthusiastic about the change because of the fear that the data will reveal deficiencies in their performance. Thus, comprehensive training and education are necessary to thaw their resistance and empower them to maximize their capabilities of data-driven decision-making in enhancing their performance and job satisfaction (McElheran & Brynjolfsson 2016).
Bell, DE; Raiffa, H & Tversky, A 1998, Decision-making: descriptive, normative, and prescriptive interactions, Cambridge University Press, New York, NY.
Berger, J 1985, Statistical decision theory and Bayesian analysis, Springer-Verlag, New York, NY.
Briggs, R 2014, ‘Normative theories of rational choice: expected utility,’ Stanford Encyclopedia of Philosophy. Retrieved Apr 18, 2017, from https://plato.stanford.edu/entries/rationality-normative-utility/
Edwards, W 1954, ‘The theory of decision-making,’ Psychological Bulletin, vol. 51 no. 4, pp. 380 – 417.
Fickes, M 1998, ‘Data-driven decision making,’ School Planning and Management, vol. 37, no. 4, pp. 54 – 57.
Gilboa, I 2011, Making better decisions: decision theory in practice, John Wiley & Sons, Malden, MA.
Kahneman, D & Tversky, A 1979, ‘Prospect theory: an analysis of decision under risk,’ Econometrica, vol. 47, no. 2, pp. 263 – 292.
McElheran, K & Brynjolfsson, E 2016, ‘The rise of data-driven decision making is real but uneven,’ Harvard Business Review. Retrieved Apr 19, 2017, from https://hbr.org/2016/02/the-rise-of-data-driven-decision-making-is-real-but-uneven
Provost, F & Fawcett, T 2013, ‘Data science and its relationship to big data and data-driven decision making,’ Big Data, vol. 1, no. 1, pp. 51 – 59. Tunguz, T & Bien, F 2016, Winning with data: transform your culture, empower your people, and shape the future, John Wiley & Sons, Malden, MA.