Business Intelligence Enhances the Quality of Decision-Making - Coursework Example

Business intelligence Name Course Tutor Date Introduction Business intelligence systems are the systems that are designed by the management of the organizations as one of the modern tools ought to be adopted by business organizations to improve their performance. Business intelligence systems are designed to be responsible for the transcription of data into information and knowledge. These systems tend to create the environment that encourages strategic thinking and enhancement of effective decision making in an organization. In simple terms, business intelligence systems are sets of methodologies, theories architectures and technologies that convert the raw data into more useful form for business use. A well designed business intelligence system handles voluminous information and aids in developing and identifying opportunities that could boost an organization’s performance. When the management of the organization realizes new opportunities, they adopt and implement effective strategies hence, providing competitive market advantage leading to stability of the organization. This paper majorly focuses on business intelligence system as one of the modern tools that firms are using to counter competition in their respective industries. It is not an old concept, but it came into existence recently after the invention of the computer software. For the short period this method has been in use in various firms, it has proved to be one of the best tools that can used to promote good performance of the organization in terms of management and financial growth. Business intelligence enhances the quality of decision making. Decision making is defined as the cognitive process which results in the selection of the course of action among the available options; every of the options available have a final choice. Therefore, to reach to the final choice that is fair to the organization requires careful selection of the alternative by the decision maker since it can affect the performance of the firms either positively or negatively. Business intelligence system when applied well tend to create the conducive environment that allows effective processes of decision making, strategic planning and credible action in a firm (Mohamed 2012). The contemporary world of business has become quite uncertain, this requires that the management have to be critical and courage to make difficult decision even if it means those decisions have many risks. Prominent managers of firms are different from other managers just because of the quality of the decisions they often make. This implies that decision making is one of the defining characteristics of a good manager. There is no big secret on how these best managers make decision other than applying effectively their business intelligence systems. Some of the ways in which business intelligence systems promote quality decision making are as follows. First, we know very well that in making decisions, the decision maker tend to look at the past records and experiences. Past records and experiences tend to impact on the future decision making, in most cases, when there is positive outcome from past decision made, a person would likely again make the same decision incase there is the same circumstance. This means that past experiences can affect future decision making (Juliusson, Karlsson &Graling, 2005). On the same account, individuals tend to avoid doing the past mistakes. Therefore, managers would likely to avoid selection the alternatives that once caused past mistakes incase they face similar circumstances in future. From this perspective, the decisions made basing on the past experiences are not always good because may be the circumstances that were in the past and the present are quite different (Mohamed 2012, p2). Therefore, it is not wise to rely of the past experiences to make the present decisions. This is where now business intelligence systems come in to assist the managers to make quality decisions. The managers are advised to make decisions according to the results after the analysis made using the business intelligence systems. This would reduce the overreliance on the past experiences hence making quality decisions. Another way in which business intelligence helps in making quality decision is through eliminating cognitive biasness. Cognitive biasness here means thinking patterns that are based on the observation and generalization that often lead to memory errors and inaccurate judgment. Every human being has a weakness of prejudgment of a particular situation without considering the underlying facts. Cognitive biasness is often limited to belief bias and the overreliance on prior knowledge (Mohamed, 2012, p2). This kind of decision making skills should be avoided by managers at al cost because we there are always numerous factors that face the organizations which one should not afford to ignore them and resort to prejudgment. Business intelligence system actually is very an important tool that can be used to avoid cognitive biasness by managers. When managers have mastered the skills in the business intelligence, they would not succumb to cognitive biasness because they know very well the impact it will have to bring to the organization (Mohamed 2012, p2). Thus, business intelligence is a managers’ guide stick that makes him avoid mere judgment of the situation by take a more credible approach in making decision, hence leading to quality decision making. Decision making sometime is influenced by the commitment and sunk outcomes. Individuals tend to make decision basing on the irrational commitments where a person invest a lot of time and money into decisions which they feel that they are responsible for their costs , time and the efforts. This means that decision making is dictated according to how far the person feels that he has been committed (Mohamed, 2012, p4). Decision made basing on this account some times are not good because some body will choose n alternative that he/she feels is the correct one to him even if when it is not a better option. Therefore, business intelligence systems come in to assists and eliminate this kind of decision making (Mohamed, 2012p3). One looks at the processes and analysis which the business intelligence has at hand and bases his decision on without looking at his previous commitment and expenditure hence making quality decision that suit the organization. Business intelligence enhances faster decision-making As mentioned earlier, business intelligence systems is one of the modern methods managers use to improve the efficiency and performances of the organization. Since it involves processes, analytical skills and methodologies that are more of scientific, it enables handling and analyzing of large amount of information within the shortest time possible (Olszak &Ziemba, 2006,p48). This enables faster decision making by organizational managers hence enhancing growth. For example in the contemporary business world, different groups of people participate in decision making i.e. Stakeholders, consumers, investors and suppliers take part in decision making. The scope of such decisions is global in nature. Regional and global interdependence need wider exchange of information and knowledge sharing. This requires more efficient coordination of all the activities that are undertaken in contrast to every process that took place. Therefore, having business intelligence in place, enable multinational organizations to disperse data and other useful information to the management of the branches (Olszak &Ziemba, 2006, p48). This makes decision within the shortest time possible because of the ease at which the business intelligence has brought in exchanging information. Another way in which business intelligence promote faster decision making is that business intelligence systems are referred to as integrated set of tools, programs and technologies that are used to collect integrate, analyze and make the required data available (Olszak &Ziemba, 2006,p48). These systems are, meant to support decision making at every management levels. They actually differ from the management information systems because of their wider range and multivariate analytic methods of the raw data that are taken from different sources and multidimensional presentation. These systems help in optimizing resources and profit maximization resulting to proactive decision making. Its though analysis of every detail increases the chances of making credible decisions within the shortest time possible. The current business firms do not face the problem of lack of information to base their decisions on but rather they face the problem of having a large volume of information which make it hard for them to select important information that would aid in making decisions faster. Current business world rely on what is called data warehouses which are thought to be major technologies that support decision making environment (Olszak &Ziemba, 2006, p49). Data warehouse are collections of subject oriented integrated, historical and non-volatile data which tend to support the firms’ decisions. Data warehouses are in most cases used to store the detailed summary data; this detailed data can be of particular department of the organization for example the sales, production, purchase or any other department within the organization. These data warehouses are developed out of business intelligence systems. Thus, an organization through sound business intelligence systems can make a decision faster in case of an urgent situation hence saving losses and costs. As we have discussed, business intelligence system helps to transform data into a useful information that can e used by the management to boost the firms performance. Transformation of data is considered to be the most complex stage of data processing. This process is often performed by traditional programming of language and scrip languages. Transformation of data involves processes such as unification, calculation of necessary aggregates, duplication of data and identifying some missing data. All these processes are complex and needs systems that are efficient and accurate to come out with accurate results that can be used to make concrete decisions that would help the organization. A good business intelligence system enhances faster transformation of this data hence faster decision making. Business intelligence in service evolution management Business intelligence systems also play an important role in eservice evolution management. Service evolution management is the process of monitoring the service that tends to be provided in an organization taking into account the new technological trends. Service delivery in all aspects has changed with time because of rapid technological innovations. The way customers and other stakeholders to the organizations were served by the organization n recent decades is quite different from the current times. Improvements in technology has made every thing to be easy in that firms can now make their sales to customers who are far without travelling these long distances (Ernando, Bruno, Karin & Renata, 2013, p1). Service evolution management tends to impose complex challenges to the service provider. The service provider has the task of identifying which changes are necessary and opting for those that would boost the performance of the entire organization. In choosing the best changes that should be implemented, the service provider relies on the business intelligence system. The use of business intelligence is based on the integration of the service usage data and other related business data that can be correlated and presented to the service providers (Ernando, Bruno, Karin & Renata, 2013). In facilitating service evolution management, business intelligence applies computer based systems that is known as decision support system (DSS). This system plays a crucial role in that it helps the professionals across various field s to know the kind of the information required when it is needed and in what form this information should be to make credible and valuable business decisions (Ernando, Bruno, Karin & Renata, 2013, p1). Therefore, through computer based system of business intelligence, unique combination of theory, applications and technology are adopted to find out the best methods that can enhance good service evolution management. Sometimes organizations tend to have several services that extended to consumers. This means that it has many service providers; this exposes it to very volatile workload in a specific period due to unpredictable external service evolution events (Susan &Dugerdil, 2009, p335). In case of a situation where workload exceeds the resources of the organization, it threaten the organization’s internal operations hence the services provided to the consumers are threatened too. Therefore, the business intelligence systems help at a great deal in balancing the organization’s resources with the service providing workload. In addition, service evolution as mentioned earlier is a continuous process. The current services offered to consumers would again change according to the technological advancements that would occur in future. Business intelligence systems already cater for these changes when they occur to facilitate a harmonious service evolution in future. The data stored in the organizations systems would be used by the stakeholders to draw out some possible future trends and using business intelligence methodologies one can easily predict the future of service delivery (Ernando, Bruno, Karin & Renata, 2013, p 1). Thus, the business intelligence systems helps much in preparing the organization for the future changes that would affect the business. This makes the organization to put some precautions measures in place so that when these changes come, they do not affect the operation of the firm negatively. Decision Support Systems for Business Intelligence Decision support systems through business intelligence started in 1960’s, it became practical when there was the development of microcomputers, time share, distributed computing and time share. The decision support systems are an applied discipline which adopts the knowledge and methodologies of other disciplines (Power, 2007,1). When decision support systems became in use, the researchers began studying systematically what is known as computerized quantitative models to help in making decision of the organizations. Decision support systems are categorized into about seven types. First we have the file drawer systems, this kind of decision support system provide access to the data items. It is the primary system that allows other systems to be used because when it is hard to access the file data, it becomes impossible for other types of data support systems to be in use (Sauter, 2011). Secondly, we have data analysis system. This system is responsible in supporting the manipulation of data using the computerized tools tailored to specific jobs (Power, 2007,1). The third type is the analysis of information system; this provides the access to a series of the decision oriented data and other small models. This means that managers at different when they need a specific data to make their own departmental decision they can use the analysis information system. The fourth decision support system is accounting and financial models. These models play a vital role in accounting and mathematical fields. They are used to calculate the consequences of the possible actions (Power, 2007, 1). Representational model is the fifth decision support system; this type is meant t estimates the results of the action basing on the simulation model. Optimization model is the sixth and its function id to provide the guidelines for the actions to be taken by operating an optimal solution which is consistence with the series of constrains (Sauter, 2011). Then the last kind of decision support system is the suggestion model. This model is designed to perform logical processing and leading to a particular suggested decision for a well understood job. Importance of business intelligence As we have discussed in the previous paragraphs, we can realize that business intelligence strategy carries with it many advantages to the organization that adopts. In its definition we found that business intelligence is the idea of delivering specific and useful information in the midst of the data explosion many firms are facing in the current times. Business intelligence is considered to be one of the corporate weapons that are used to boost the performance and fight fraud, abuse and wastage (Vitt, Luckevich, & Misner, 2010, 45). An organization that has designed effective business intelligence systems to monitor its operations is in a better place to compete effective in this competitive business world. As discussed earlier, data warehouse is one of the aspects in business intelligence. This data warehouse is shared among the interested parties within the organization. It helps returning the lost information easily and quickly (Vitt, Luckevich, & Misner, 2010, 45). This process need clear definition of business requirements that is translated into technical terms through extraction, transformation and loading data. Therefore, through data warehouse, an organization can retrieve useful information that had lost using certain process. This means that business intelligence systems ensure security of the organization’s information. Lastly, business intelligence systems facilitate service outsourcing. With the help of the internet, organization is in a position to access external experts who can bring more expertise in the operation of the firm. Firms tend to hire experts from outside in terms f contracts, these external experts are delegated with information processing related tasks (Vitt, Luckevich, & Misner, 2010, 45).This trend of outsourcing the experts is driven by the idea that these service experts will have certain in-house skills for the related tasks, there they would be in a position to handle these tasks more efficiently so that they can get other contracts outside or within the same organization. Thus, business intelligence has helped in bringing efficiency in organization through outsourcing. Bibliographic references. Ernando, S., Bruno, V., Karin, B. & Renata, G. (2013). A Business Intelligence Approach to Support Decision Making in Service Evolution Management. Accessed on web 15th April 2015. Juliusson, Karlsson &Graling, (2005). Factors that influence decision making. Accessed on 15th April 2013. Mohamed, A.H. (2012). Impact of Business Intelligence and Decision Support on the Quality of Decision making. Retrieved from http://www.meu.edu.jo/ar/images/document/a3mal/elec/The%20Impact%20of%20Busine ss%20Intelligencepdf Karlsen, I. A. & Nord, M. (2012). Real time business intelligence and decision-making: how does a real time business intelligence system enable better and timelier decision-making? An exploratory case study. University of Agder Peppard, Ward &Daniel, E. (2007). Improving decision making in organizations: Unlocking business intelligence. Accessed on web 15th April 2013. Olszak, Celina &Batko, Kornelia. (2012). The business intelligence systems in healthcare organizations in Poland. Retrieved from http://proceedings.fedcsis.org/2012/pliks/76.pdf Power, J.W. (2007). A brief history of decision support systems. Accessed on web 15th April 2013. Retrieved from http://dssresources.com/history/dsshistory.html Rud Olivia. (2011). Business intelligence success factors: Tools for aligning your business in global economy. Accessed on web 15th April 2013. Retrieved from http://menaxhim.files.wordpress.com/2011/09/business_intelligence_success_factors__to ols_for_aligning_your_business_in_the_global_economy__wiley_and_sas_business_seri es_.pdf Sauter, Vicki. (2011). Decision support system for business intelligence. Accesed on eb 15th April 2013. Retrieved from http://umsl.edu/~rtb44f/DSS/files/Decision_Support_Systems.pdf Susani, Odorico &Dugerdil, Philippe. (2009). Cross-organization service evolution management. Accessed on web 15th April 2013. Retrieved from http://doc.rero.ch/record/12491/files/IEEE1.pdf. Vitt, E., Luckevich, M., & Misner, S. 2010. Business Intelligence. O’Reilly. Read More