Decision Making Theories

1.1 Decision making Theories The success of any business depends on the quality of decision made by the management as well as employees. Companies that   are able to make better and quality decision have greater productivity, profitability and growth rate.    But how are decision made in an organization? Well there are several theories that try to explain how decisions are made. These theories includes  Bounded   Rationality model According to bounded rationality model, reality is complex and as such the decision makers (irrespective of their level of intelligence) usually have to work less than three unavoidable constraints.  The first constrain is their level of human cognition.   An individual’s mental process of acquiring knowledge and understanding it through thought is limited. The second constraint   is that decision maker does not have exhaustive knowledge of all available alternatives neither do they have adequate information to ranks these alternative or evaluate their benefit and cost.  The third constrain is time.    There is always a limited time available to make decision.  Given these constraints, decision maker does not try to find and optimal solution to a problem rather they seek satisfying choice (Sipp and carayanis 2013). Incremental model The incremental model argues that decision making is not a discrete event.  It is a step-by-step process consisting of incremental changes form status quo toward problem solving.  According to this theory, the decision making process is broken down into Small steps. The process of moving from one step to another  which is termed as ‘muddling through’ is based on combination of factors including the experience, Intuition, guessing and the use of different techniques (Bugajenko 2017).    At every step, a small number of alternatives and consequences are considered. Therefore, the changes that comes out of every step is small and as such   immediate effect is minimal and none disruptive.  In other words, this theory views decision making process as evolutionary rather than revolutionary.  As pointed by Sipp and Carayannis   (2013),   Incremental model put emphasis on   implementation of the decision rather than analytic steps that lead to the decision. The model advocate for continuous learning from the decision implemented, multiple feedback loops and decision adjustment to decision in order to achieved the intended goals.    Organization View model Organization View model sees decision as an output of organization’s standard procedure and policies rather than a specific and stand-alone decision making process (Sipp and Carayannis 2013).  According to this theory, where formal organizations are the setting in which the decision are made, the decision made by manager or individuals can be explained   through reference to their organization’s standard procedure and structure rule (Johnson 2005). In other words, this theory argues that, where decision are made within an organization, the decision making process is determined or shaped by that organization’s structure and procedures and therefore affecting the decision made.  The garbage can Theory   The garbage can theory as developed by Cohen et al (1972) argues that organization is a collection of  Problems in search of  solution ,  solutions in search of problems , issues and feeling looking for platform in which they can be aired and  decision makers looking for work. Cohen et al (1972) further argues  that, problems, solution, decision makers and  choice always flow in and out of the Garbage Can  and as such the process in  which a problems is  matched to a solution is largely  by chance (Sipp and Carayanis 2013). The theory do however acknowledge  that, leader  can make  significant difference in the ‘garbage  Can’ by   Timing issues creation carefully,  being sensitive to the shifting interest of participants and recognise the power  implication of  every choice. Naturalistic Decision-Making theory The Naturalistic Decision making theory argues that decision making process does not follow a rigorous   method as claimed by earlier or traditional theory of decision making (Klein 2008).  According to this theory, the context in which the decision is made is crucial.  When making the decision, decision makers not only recognize the situation itself but also it context, possible outcome and solution (Sipp and Carayanis 2013)   1.2 Definition of data driven Decision making  Decision making process can be simply defined as process of selecting a logical choice from many available alternatives.  Traditionally, decision making in organizations have been made using combination of factors including experiences, personal judgement, guess work and information available.   However, with the increase in digitalization, organizations are now able to generated big data which help them not only understand their business but also their customer market and environment.  The emergence of huge or big data in organization has lead to what is now commonly known as data driven decision making.   Data driven decision making is defined by Marr (2016) as an approach to business governance that values decision that can be backed by verifiable data.  Defined differently by Webb and Danson (2016), data driven decision making is the process of collecting and analyzing data decision making in an organization.   Simply, Data driven decision making is practice of   gathering data from various sources analyzing them     in order to inform decision making process 1.3 changing organizations culture to a data driven decision making paradigm Today, businesses are operating under environment where   lot of data are generated.  Due to digitalization, data are generated from almost every corner be it social media, emails, video, and other non-traditional information sources.  As mentioned by Frick (2014) this data gives companies’ management unprecedented ability not only to understand their customers but also their business as well as be able to anticipate challenges and identify Opportunities. As pointed out by Frick (2014), due to the changing Business environment, organizations are under pressure to make accurate and timely decision.   Data gives them the opportunity to do so. According to a recent study conducted by Harvard Business Review (2015), Organizations that rely entirely on data for their decision making process record better financial performance than those companies that rely on tradition decision making process.  This  fact is also supported by another study conducted by Brynjolfsson, Hitt, and Kim (2011) which found out that  companies that rely on data for their decision making process are  5 % More productive,  6 % more profitable and 50 percent  more market value.  In another study conducted by McKinsey global institute, organizations that have shifted to the data driven paradigm are 25 times to acquire new customers, 6 times  likely to retain those customers  and 19  times  profitable (Gaskell 2016). It is therefore with no doubt that data driven decision making is becoming the new competitive advantage.   Companies that want to stay ahead should therefore change their cultures from a tradition decision making process to a data driven decision making process. As pointed out by Gaskell (2016), most organizations do not failure to shift to data driven decision making culture due to   insufficiency of data. They do so because they are overwhelmed by fragmented data from various sources including social media, internet, among others. Therefore, for an organization to successfully transition from tradition  decision making process into  data driven decision making process, they have to put systems in place to  integrate various data sources and organize the data itself   so as it can become  valuable input in the decision making process.  In addition,  establishing and organization cultures of data driven decision making requires the staffs or people responsible for decision making in  the organization to have  good analytical skills  so they can be able to understand and make sense out of  the overwhelming data (Webb and Damson 2016).  Staffs training are therefore an important element of transition   from traditional decision making to   data driven decision making paradigm.