A Study of Business Intelligence and Analytics

Questions: The different types of decisions made within the business environment and how this type of decision making works. The six elements of the business intelligence environment. The five analytical outputs that business intelligence systems utilize to provide correct and real-time information to users. Discuss how business intelligence and analytics support improved decision making. The benefits to a modern business of employing the use of intelligent techniques in decision making and knowledge management. How these systems are used to help people working in groups make decisions more efficiently. Discuss the systems that are used for improving enterprise-wide knowledge management. Answers: Introduction The topic which has been discussed here is related to the matter of Management Information System that has brought a revolution in the field of Information Technology. The Management Information helps an organization to take decisions on the base of business knowledge and management (Ballard et al. 2012). The developments include business analytics and intelligence. The topic discusses the impact of business intelligence and analytics in the improvement of the decision making in the business and managing the knowledge of it. Findings and Analysis Types of decision made within the business environment and its functions There are six types of decisions which are made within the business environment of an organization (Bonczek et al. 2014). They are nonprogrammed and programmed decisions, strategic and routine decisions, tactical and operational decisions, organizational and personal decisions, major and minor decisions, individual and group decisions. The lower level managers take the programmed decisions for the problems of repetitive nature such as issues regarding the purchase of raw material, granting the leave to an employee. On the other hand, nonprogrammed decisions are taken by the upper-level management in case of the problems where the solution is not quickly to find out (Botha et al. 2014). Such problems can occur in the event of inaugural of the new office of the organization. The routine decisions are frequently based on the organization's general functioning. The lower level managers take the decisions without much analyzing or evaluating it (Chang 2014). The strategic decisions involve massive investment and are nonrepetitive and require careful analysis and evaluation of the higher management. In the case of policy matters of the organization, the top management take the tactical decisions which have a long-term impact on the company. Such as decision regarding channels of distribution systems (Chen et al. 2012). The operating decisions are subjected on day to day functioning of the business. The organizational decision takes place in such case where the individual takes the decision as an executive in the official capacity. If the person takes the decision personally affecting his personal life, then it becomes a personal decision (Chiang et al. 2012). The major decisions are made by top management such as purchasing new factory premises whereas the office superintendant takes the minor decisions such as purchasing office stationery. The individual decisions are made by a single personality in case of routine work whereas the group decisions are made by a committee where maximum personal opinions are involved (Fleisher and Bensoussan 2015). In all types of cases, the management information systems provide support to take decisions which eventually gives positive results. As the decision meets the requirement, the solution comes out in accordance. The six elements of the business intelligence environment The six elements that consists the environment of business intelligence are as follows Business environment data It is unstructured and structured data that are required to be organized and integrated from various sources. The infrastructure of business intelligence To capture all the business process data of relevancy, a database is required (Loucks and da Costa 2013). Toolset of business analytics This toolset is needed for tracking the progress of the enterprise and for analyzing the data to produce reports. Managerial methods and its users The decision is made by the managers on the goals of a strategic business and how progress is measured by the utilization of the BI and BA tools (Maloney et al. 2015). The platform of delivery (ESS, MIS, DSS) It comes from the Business Analytics and Business Intelligence that goes to every individual in the organization. The interface of the user The scorecards and the dashboards which are used for presenting the results of BI and BA. The Business Intelligence system provides six analytic functionalities: Reports of the production These are the reports which are predefined and based on industry specific requirements. Reports based on parameters Several parameters are entered by the users in a pivot table for filtering the data and isolation of the parameter impacts (Power et al. 2015). Scorecards or Dashboards These are the visual tools for presentation of the performance data as defined by the users. Creation of the Adhoc query / search / report Creating reports by the users based on the search and queries. Drilling down Moving from high-level summary to more detailed view (Sangari and Razmi 2015). Scenarios, forecasts, and models Performing linear forecasting, analysis of what-if scenario and analyzing data using tools of the statistical standard. The five analytical outputs providing correct and real time information to users Business Intelligence is the toolset that supports the transformation of raw data into useful information for making decisions. It provides reporting functionality, tools for identification of a cluster of data, data mining techniques and analysis for prediction. The five analytical outputs that BI systems utilize to provide correct and real time information to users are the OLAP, Data Visualisation, Reports, Dashboards, and Alerts (Shah et al. 2012). These outputs provide the users a detailed presentation of the results of the query they generate as per their requirements. For an instance, forecasting of BI capability is a package of an analytical application for sales forecasting. Many different types of analytical outputs indicate multi-industry business functions such as workforce, supply chain. Other outputs are claimed analytics and antimoney laundering used in industry-specific cases (Sharda et al. 2013). The analytical applications can be focused on the variety of vendors and internal teams. It can also support transaction processing applications and inventory control and product purchasing. Discussion of support in improved decision making through BI and BA The companies normally focus on the development of business, in such case, it's very important for making a quick decision on the base of available quality information (Wang et al. 2014). Some of the companies are managing to give relevant and precise data to provide their decision makers. The organisations have realized the value of capabilities of useful business intelligence. In many companies, the technologies which are new cause an overload of information. It makes the decision makers confused with inadequate information. Some companies manage in providing decision makers with automatically consolidated and processed data that are presented in a format which is understandable (Zakane et al. 2014). The companies provide quick and profound decision making with the help of Business Intelligence capabilities and processes. The Business Intelligence is the research field that aims at practical and theoretical aspects for achieving the solid information base of making decisions. The Business Intelligence and Analytics report provide information for decision making (Zhang et al. 2013). For that purpose, it needs the perfect mixture of the architecture, data structure, data collection process, IT systems, responsibilities for providing meaningful information. As the companies get the amalgamation of these above mentioned factors through business analytics and intelligence, they come at the stage for making improved decisions for the cases they face (Wang et al. 2014). The decision makers have a sound knowledge of the domain they study, get a clear picture to suggest a solution. Benefits to a modern business of employing intelligent techniques in decision making and knowledge management In the modern times, various organizations have devoted themselves to the implementation of using intelligent techniques. Such techniques are applied for taking business decisions. Here knowledge management plays a crucial role in determining the decisions (Sharda et al. 2013). The Decision Support System (DSS) is the application and technology that helps the decision makers in the managerial positions to utilize data and models for solving unstructured and semistructured problems. The combination of Artificial Intelligence and DSS has produced modern active DSS. The active DSS is a part of new DSS and Intelligent systems (Shah et al. 2012). The examples of active new DSS applications are the Expert System and Knowledge Based System. The other intelligent systems used for decision making are the Adaptive DSS along with Intelligent Decision Support System (IDSS). The Artificial Intelligence aims for studying the human thought process and duplicating the thought process through machines such as computers and robots. It, therefore, explores the behavior of a computer but performed by a human (Sangari and Razmi 2015). It has the following benefits in decision making. It helps to learn and understand from experience It concludes in such situations where uncertainty and fuzziness exists It uses the knowledge and experience for manipulating the environment It helps to respond successfully and quickly to the new situations It helps to recognize the importance of relativity of different elements in a situation Figure 1:The Knowledge Management System Cycle (Source: Chang 2014, pp- 525) The implementation of intelligent techniques has also made a drastic change in the Knowledge Management System. The available data or facts are processed to information by business context by the knowledge experts (Maloney et al. 2015). Such processed information are required to take decisions as per the business requirement. Assimilation of intelligent techniques in the Knowledge Management System makes it more flexible for decision making (Loucks and da Costa 2013). The enterprise wise knowledge management system is of three major types, structured knowledge systems, semistructured knowledge systems and knowledge networks. Support of the systems for groups to take efficient decisions The Decision Support Systems support a wide range of decision making tasks. In the mid-nineties, the majority of the software vendors presented the DSS to their clients who were very much general resulting to failed projects, painful memories, and unrealistic expectations (Fleisher and Bensoussan 2015). As a result, companies felt the need to build computerized decision support system for supporting their decision makers. As the new version was launched, it led to support in making more efficient decisions in the group work. The two fundamental concepts that are associated with support of computerized decision is first, IT and computers help people in making important decisions (Chiang et al. 2012). Secondly, the computerized DSS support assists the managers to keep them in connection to a loop of making decisions. Thus, the aim is to improve the decision making efficiency along with effectiveness and not the decisions of automated nature. Many companies are doing their daily activities such as monitoring of performance on integrated computerized decision support system. The managers often download and analyze data for creation of reports (Chen et al. 2012). They often analyze and evaluate forecasting results. The DSS helps the managers for allocation of resources, comparison of the predicted with the actual results, the projection of revenues and evaluation of scenarios. In todays era, various complicated situations rise in business cases where taking decisions become very much difficult (Chang 2014). Moreover, the requirement for making speed decisions has increased, information overloading is normal, and distortion of information is higher. In such case, the modern decision support system encourages fact-based decisions with improved quality efficiency and effectiveness (Loucks and da Costa 2013). Such system helps the groups to take fast decisions as per management requirements. Discussion of the systems for improving enterprise-wide knowledge management The systems that aim for the improvement of enterprise-wide knowledge management are mentioned below: The Extranet and the Intranet OLAP, Data Mining and Warehousing of Data Groupware Systems Systems of Content Management Decision Support Systems Systems of Document Management Tools of Artificial Intelligence Semantic Networks Simulation Tools (Maloney et al. 2015) The extranet is the network of privacy that has the usage of public telecommunication and the internet for sharing a part of business information with the suppliers, partners, customers or other business in a secure manner (Fleisher and Bensoussan 2015). The intranet is an organization's network, normally a corporation based on the protocols of TCP / IP, accessible only by the employees of the organization. OLAP is the online analytical processing technique that is used for multidimensional analysis of data related to business. It provides the capability for trend analysis, complex calculations, and sophisticated data modeling. Data warehouse system is used for analyzing and reporting data (Chang 2014). They are the central repositories of integrated data from one or more sources. The Data mining system is required to be enabled as it is the process for computation of discovering the patterns in a large set of data which involves artificial intelligence, database and learning of machine. The groupware system is the application software that is implemented to support the people who are involved in doing a common work for achieving their targets (Chiang et al. 2012). The Content Management System supports the modification and creation of digital content using interface of the standard user and provides support to multiple users in a collaborative environment. The Decision Support System supports the organizational decision making work. The Document Management System is required to track, store and manage documents for reduction of paperwork. The system is capable of tracking the history (Power et al. 2015). The Artificial Intelligence tools aim for the creation of computers and software capable of behaving intelligently. The Semantic Network system is needed for storing the knowledge in a form of graphs having nodes marking objects in the world and arcs mentioning relationships between the objects. The simulation tools assist in understanding systems of complexities and support in making decisions (Sangari and Razmi 2015). Therefore, if all these systems are integrated into the organizations Knowledge Management Systems, then the overall system becomes much more concrete and flexible. Conclusion It has been discussed throughout the topic that adaptation of various Decision Support Systems has reduced the complexities in making decisions for the managers working in a group. Prompt decisions on critical matters have led to solutions which have benefitted the organization in the long run. Therefore, it can be concluded that the management authority has made themselves more involved in getting support from these systems in future for addressing feasible solutions regarding the cases they face. Recommendations From the discussions and the conclusion, some facts that can be recommended are as follows: i) Organisations should do in-depth research for inventing new support systems and intelligent techniques for decision making for avoiding the situation if the existing systems become of no use. ii) The management should get involved to apply the current systems for more extensive usage in other cases to ensure its optimum utilization. iii) The authority should maintain a strong architecture for safety and security of these systems they use so that unnecessary intrusion does not happen to result in data theft or sudden system shutdown. References Ballard, C., Farrell, D.M., Gupta, A., Mazuela, C. and Vohnik, S., 2012.Dimensional Modeling: In a Business Intelligence Environment. IBM Redbooks. Bonczek, R.H., Holsapple, C.W. and Whinston, A.B., 2014.Foundations of decision support systems. Academic Press. Botha, A., Kourie, D. and Snyman, R., 2014.Coping with continuous change in the business environment: knowledge management and knowledge management technology. Elsevier. Chang, V., 2014. The business intelligence as a service in the cloud.Future Generation Computer Systems,37, pp.512-534. Chen, H., Chiang, R.H. and Storey, V.C., 2012. Business Intelligence and Analytics: From Big Data to Big Impact.MIS quarterly,36(4), pp.1165-1188. Chiang, R.H., Goes, P. and Stohr, E.A., 2012. Business intelligence and analytics education, and program development: a unique opportunity for the information systems discipline.ACM Transactions on Management Information Systems (TMIS),3(3), p.12. Fleisher, C.S. and Bensoussan, B.E., 2015.Business and competitive analysis: effective application of new and classic methods. FT Press. Loucks, D.P. and da Costa, J.R. eds., 2013.Decision support systems: Water resources planning(Vol. 26). Springer Science Business Media. Maloney, K.O., Talbert, C.B., Cole, J.C., Galbraith, H.S., Blakeslee, C.J., Hanson, L. and Holmquist-Johnson, C.L., 2015. An integrated Riverine Environmental Flow Decision Support System (REFDSS) to evaluate the ecological effects of alternative flow scenarios on river ecosystems.Fundamental and Applied Limnology/Archiv fr Hydrobiologie,186(1-2), pp.171-192. Power, D.J., Sharda, R. and Burstein, F., 2015.Decision support systems. John Wiley Sons, Ltd. Sangari, M.S. and Razmi, J., 2015. Business intelligence competence, agile capabilities, and agile performance in supply chain: an empirical study.The International Journal of Logistics Management,26(2), pp.356-380. Shah, V., Turkbey, B., Mani, H., Pang, Y., Pohida, T., Merino, M.J., Pinto, P.A., Choyke, P.L. and Bernardo, M., 2012. Decision support system for localizing prostate cancer based on multiparametric magnetic resonance imaging.Medical physics,39(7), pp.4093-4103. Sharda, R., Delen, D. and Turban, E., 2013.Business Intelligence: A Managerial Perspective on Analytics. Prentice Hall Press. Wang, S., Noe, R.A. and Wang, Z.M., 2014. Motivating knowledge sharing in knowledge management systems a quasifield experiment.Journal of Management,40(4), pp.978-1009. Zakane, S.A., Gustafsson, L.L., Tomson, G., Loukanova, S., Si, A., Nasiell, J. and Bastholm-Rahmner, P., 2014. Guidelines for maternal and neonatal point of care: needs of and attitudes towards a computerized clinical decision support system in rural Burkina Faso.International journal of medical informatics,83(6), pp.459-469. Zhang, X., De Pablos, P.O. and Zhou, Z., 2013. Effect of knowledge sharing visibility on incentive-based relationship in Electronic Knowledge Management Systems: An empirical investigation.Computers in Human Behavior,29(2), pp.307-313.