Evolution of Decision Making Approaches: The Evolvement to Data-Driven Decision Making

Decision-making can simply be termed as the principle of selecting between two or more initiatives or causes of action to effectively identify and solve problems. Over the years, the business and institutional environment has grown tremendously, thus leading to hiking demand for effective, accurate and timely decisiveness. Decision makers, managers or leaders, must proficiently identify opportunities in the market, challenges, and make quick and crucial choices that affect the competitiveness of their institution or organization. These decisions play a significant role in ensuring their survival in the global environment. On the other hand, Data-Driven Decision Making Approach provides accurate and verifiable information that is used in the decision-making process. Using the data guarantees positive outcomes of the process while improving the organization’s state in the modern technological world. The approach relies on quality research to support the decisions made by leaders or managers (Power, 2016). This paper focuses on the gradual change in decision-making approaches. It critically evaluates how early decision-making theories have evolved to modern approaches (data-driven decision-making).

Early Decision-making Approaches

Different sociologists, economists, and psychologists developed several theories regarding the concept of decision-making. Though the theories differed in assumptions and way of execution, they had various similarities where one would form the basis for the other. One of these theories was the classical and neoclassical approach. The theory stated that the decision making process involved investigation on issues, generating possible alternatives and determining their impact, then choosing the optimal alternative (Kalantari, 2010). However, “Simon asserted that the classical and neoclassical approaches in dealing with decision-making concept are not realistic and do not correspond with the real world” (Kalantari, 2010, p.512).

Cyert and Simon (1983) developed a theory known as the Behavioral Approach, which according to them, gave a picture of the nature of rationality in the real world. They also stated that “a theory of decision-making within organizations that combines both the internal variables of the firm and the market forces of the particular market structure the firm is operating within can be developed” (Cyert & Simon, 1983, p.95). The approach was developed to improve the unrealistic classical model and provide more profound decision-making support to managers.

Another theory was the expected utility theory, which “was accepted as a normative model rational choice” and dominate in “analysis of decision making under risk” (Kahneman & Tversky, 1979, p.263). They described the theory as a “descriptive model of decision making under risk, and developed an alternative model, called the prospect theory” (p. 263). Most of the decision made by leaders are under risk, therefore these models are crucial. In describing decision making in relation to leadership, Vroom (2000) used an example of an emergency response manager. According to him, the process begins with problem identification, then determining the possible solutions to solve the problem. As a leader, quick decisions are paramount in determining the survival of your organization or institution, especially where substantial risk is involved.

Development of the Decision Making Approaches

Kalantari (2010) explains that the main idea in Herbert A. Simon’s study was based on scientific evidences on human behavior. “His central goal was to explain the nature and mechanism of thoughts process that people use in making decisions”

(Kalantari, 2010, p.510). Simon’s studies were directed towards administrative behavior as a discipline dictating the process of making a decision. “Organizational decision-making is a complex process that is influenced by many factors in the organization” (Kalantari, 2010, p.511). The theory was mainly to rectify the previous classical and neoclassical approach, which was ineffective.

“The classical and neoclassical approaches in dealing with decision-making concept are not realistic and do not correspond with the real world” (Kalantari, 2010, p.512). The bounded rationality model was an ideal approach to use for decisions that are more effective. Simon defended his model stating that bounded rationality can be referred to as “limits of human capability to calculate the server deficiencies in human knowledge about the consequences of choice, and the limits of human ability at adjudicate among multiple goals” (Cited in Simon, 1979, p.270). He also stated that “modern definition of the economic sciences, whether phrased in terms of rational decision making, marks out a vast domain for conquest and settlement” (Simon, 1979, p.493). The classical model formed the basis in empirical evidences, and from that, the bounded rationality model was formed to disapprove it and became the starting point for economic theories (Cited in Simon, 1997). However, the bounded rationality had its own limitations since it emphasized more on individual rationality degree of individual decision maker rather than evidences to support the action taken. This meant that the individuals modified the rationality model themselves, accounting for limitations surrounding them for satisfactory decisions (Kalantari, 2010). The model did not guarantee optimal decisions; therefore, there was need for more research to formulate models that are more sophisticated.

Most of the early theories on decision-making emphasized more on person’s behavior as the major determinant of reliable decision-making process. However, some included more factors, for example, the normative model. It was realistic and enabled a leader to reflect on past description in order to make sound decisions. Similarly, it formed a base for the development of data-driven decision-making approach, which is highly common in top performing companies and institutions globally.

The Data-Driven Decision Making Approach

The global business and institution environment has changed tremendously with great demand for timely, accurate and reliable decisions pressuring leaders or managers (Harvard Business Review Analytic Service, 2012). To achieve sensitive goals of timely and effective decisions, while relieving this pressure, strategic approaches need to be followed. To this regard, reliance “on good pre-analysis and documented data” is a brilliant way to come up with accurate decision while minimizing “tedious complexity of the decision-making process” (Tank, 2015, p.43). According to Tank (2015) as technology changes, competitiveness increases, therefore increased need for sufficient and reliable data to support the decision-making process. “Contemplating decision-making support options forces managers and technologists to examine issues of rationality, information culture, and decision support and analytics design and deployment” (Power, 2016, p.345). The increased demand for evidence-based decisions resulted to the gradual development of the data-driven decision-making. This approach has significantly improved the process of making decisions, especially when under pressure and in risky environments.

Significance of Reliance on the Data-Driven Culture

Rational thinking and a desire for evidence-based decision-making is paramount when formulating effective data-driven approach (Power, 2016, p.347). Recent studies show that managers are faced with substantial challenges when executing their managerial tasks while using the new data streams. To solve this, great emphases have been put on evidence-based approaches in decision-making process. A good example of the effectiveness of the data-driven approach is in occupational therapy. The data-driven method “provides a framework for reasoning through the occupational therapy process with a focus on utilization of data guide and measure outcomes” (Schaaf, 2015, p.2).

Data science is a recent discipline that has contributed positively to the decision-making process (Power, 2016, p.345). The approach requires data scientists who encourage managers and leaders to “examine issues of decision-maker rationality, data and data analysis” (Power, 2016, p.345). According to Power (2016), managers look upon the data scientists to utilize the expanding data streams, which in turn enhance rational decision-making while ameliorating data-driven decision making in the organization or institution. Using past-well analyzed data substantially reduce the risks in decision-making process. Leadership challenges are eliminated since the leader can find past information on the area, learn from any mistakes made, and rectify them when making the current decisions. Risks of repeating past mistakes are close to none.

Current Situation on Data-Driven Decision Making

Today, “the imperative to make better decisions faster has increased the pressure on organizations and their employees” (Harvard Business Review Analytic Service, 2012, p.3). As indicated in Harvard Business Review Analytic Service (2012), the pressure has resulted to evolution “in the development of a data-driven culture, typically based on the use of analytics and business intelligence” (p.3). The evolution has been greatly influenced by standardization of the decision-making process, increased pressure for timely decisions in the competitive market, the need for analytical tools to expand skills, and creation of an analytical ecosystem on data-driven decisions (Harvard Business Review Analytic Service, 2012).

Additionally, there is a rapid adoption of the data-driven decision-making approach in organizations, government institutions as well as learning institutions. A study by LeMire et al. (2016) explains how statistical data is used to promote data-driven decisions in learning institutions. Collection and employment of crucial and relevant “reference statistics for the data-driven decision making is more important than ever” (LeMire et al., 2016, p.230). The approach has become more common in the modern global environment. The organizations’ ability to build and use management systems effectively is highly dependent on its staff’s capacity to comprehend various activities and deciding what to take and what to leave (Maxwell, Rotz & Garcia, 2016).

In the U.S, the use of data-driven decisions in manufacturing sectors has increased substantially. “However, adoption has been uneven” despite the nearly tripled use of data-driven decisions “between 2005 and 2010, from 11% to 30% of plants” (McElheran & Brynjolfsson, 2016, p.5). This great development on data-driven decision-making has been evident on firms with well-integrated IT investments, and less in firms with unsophisticated IT growth.

Similarly, “along with teamwork, communication, problem-solving and other employee requirements, the ability to analyze and act on data will become a core competency for professionals of all types” (Mozenda, 2017). Today, for a leader to perform tasks proficiently, he/she should be able to interpret relevant data in their organizations and make sensible and reliable decisions to solve problems.

Conceptual phrases like “big data” and “data lakes” will be used less and less in favor of implementing detailed processes that emphasize delivering useful, relevant data. Companies continue to thrive or stagnate by their ability to collect and act on data. Unfortunately, many organizations end up building large databases of information that are underutilized and do little to improve their bottom line. Data is truly only useful when it is able to answer important questions. (Mozenda, 2017)

Answering those questions will help solve problems facing a company or institutions.

Additionally, the technical challenge affecting decision makers is the effective use of big data and execution of task as part of the executive team in the organization (McAfee & Brynjolfsson, 2012). Therefore, in ensuring the success of a company in the big data era, members of the leadership team need to set clear goals, ask the right questions and clearly defining what needs to be done for the company to succeed (McAfee & Brynjolfsson, 2012, p. 66). It is also evident that performing companies have leaders who are able to think creatively, understanding the market and able to identify available opportunities proficiently.

Conclusion

Managers, being the decision makers, are obligated to make the right decisions for the effective running of an organization. They have to be familiar with the most effective approaches in the decision-making process. In the modern environment, “managers are facing both challenges and opportunities from new and expanding data streams” (Power, 2016, p.345). The gradually changing environment has prompted an improvement on the past decision making approaches to ones that are more sophisticated, enabling quick adoption of the changes. With the development of data-driven decisions, there is high demand for skilled decision makers. This is because; the data needs to be interpreted correctly to enhance effective decisions. For example, not all data that is useful, therefore a manager should be able to identify the important data that is relevant to the problem being tackled. Data-driven decision-making approach has helped “managers to decide on the basis of evidence rather than intuition” (McAfee & Brynjolfsson, 2012, p.63). Subsequently, “senior decision makers have to embrace evidence-based decision making” as companies “hire scientists who can find patterns in data and translate them into useful business information” (McAfee & Brynjolfsson, 2012, p.63).

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