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The Future of Business Intelligence - Coursework Example

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The paper focuses on the future of Business Intelligence. This system is considered to be a combination of operational data with various analytical tools to present detailed and highly competitive information to planners and the organizations’ decision-makers…
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The Future of Business Intelligence
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Business Intelligence Business Intelligence Introduction Business intelligence system is a combination of operational data with various analytical tools to present detailed and highly competitive information to planners and the organizations’ decision makers. The major aim is to enhance the punctuality and the quality of the required inputs to the process of decision making. Business intelligence is applied in the evaluating the potentials and capabilities in the firm, dynamics of the market, the business environment and the legal and regulatory environment from which the business (Venkatraman and Brooks, 2012, p. 244). It is also applicable in the determining the actions of business competitors and the impact of their action. The business intelligence growth is facilitate by the emergence of data warehouse in form of a repository, the increased performance of the soft and the hard wares and advance in the information cleaning approaches. It finds use mainly in industries (Negash, 2004, p.177) Future of Business Intelligence Business intelligence has dynamic advancement of its complexity over-time, just like the information technology. The dynamism of the business intelligence is induced by various factors. These factors include the advance in technology, business needs such as real time information and data. Some changes also unravel better alternatives of carrying out specific tasks. The future of business intelligence lies in four main areas (Tuţă, Zara, Orzan, Purcarea, and Orzan, 2014, p. 1). There is constant need for the business intelligence to expand and develop to cope up with the future demands of the business. Key areas that influence the future of business intelligence include (Watson, 2009, p. 492). Scalability Scalability refers to the ability of a business intelligence system to handle more volumes and much complex information. The business intelligence of the future must be able to handle such ever increasing the information of the system. There is need of a business intelligent that support large number of concurrent users, has the capacity to handle more complicated queries and preserves information without degradation of its system. The scalablity of business is majorly influenced by the recent concurrent advancement in various hardware and sophisticated database software development that incapacitate the business intelligence system to handle large scale of information. As the future of the information management expands so is the scalability of the business intelligence system (Watson, 2009, p.492). Pervasive Business Intelligence There urgent need to make business intelligence pervasive in the all business approach. The BI extent of operation require step up to handle more people in a business. The extent of spread of the BI in future should exceed employees and cover other key players in a business like the suppliers and all customers (Al-Aqrabi, Liu, Hill, and Antonopoulos, 2015, p.85). The current studies reveal that only 11 percent of the business intelligence users have the BI pervasive all through the business organization. It’s therefore evident that the future of BI in developing the information management is adoption of its use throughout the business enterprise to advance the information management. However, there are several challenges that hinder the development of pervasive BI. The principal problems encountered include high cost of acquiring BI, the complexity and need for training of users (Watson, 2009 p. 494). Operational Business Intelligence The business intelligence developed earlier was limited to storing, processing and interpreting historical data. The current and the future course of advancement is developing business system able to process real time operational data. There is future demand to upgrade the BI to handle operational data by capturing storage and application of real time data. Real time data has impacted so enormously on supporting the decision making data management. Advancement of BI generations has occurred overtime and there is need for future advancement in the future generations to allow for more efficient handling of the real time information (White, 2005, p. 34). Due to the degrading effects of the latency time on the business data value, the operational data processing is the future of business enterprises and there the BI. The latency period refers to the time lapse between the information recordings to the time it is processed into a usable form. The length of latency period determines the size value for ultimate use of data. Current and BI generation are developed to adapt handling of the operational data to preserve the quality and accuracy of the business data (Watson, 2009 p. 92). BI-Based Organization There are stiff competitions in the business landscaping. Business enterprises are adopting businesses intelligence to enhance their landscape. Such firms employ extensively the use of BI in their operations and are therefore BI-based. Recently, there are just a few BI-based companies. However, the future trend will be integration of BI may business operations (Watson, 2009 p.94). Development in Business Intelligence on Organization BI influences landmark development on the organization. It has set contemporary trends in the handling, management and storage of data in the Organization. Business intelligence has led to the development of the real time business information. The real time business information has significantly availed current information about the business for decision making. Business intelligence has thus created a new framework for the development of the organization information processing and management overtime (Khan & Quadri, 2014 p. 84), Business intelligence has allowed for the growth and expansion of companies. Various companies can handle their transaction data accurately. There is advanced access to the competitors’ next action plan and the company can thus take advantage of the access to such information. Business intelligence also potentiates the company to the identification of opportunity and exploitation of such opportunities. By the integration of the customers into the business intelligence system, there is development in the market targeting. The company only communicates directly to its current and potential customers. Through such development, business intelligence saves the organization’s expenditure on advertisement expenses. Business intelligence equips the organization with the capability of adapting to the dynamic market conditions. The corporate world consists of tight competition, dynamism of various factor factors of production and management. Business intelligence allows for the evaluation of the diverse variables such as competitors, performance, operational performance and other factors in real time. Business intelligence has also led to the collective analysis of the business information. The integration of BI into the business has allowed for the collectively evaluation of both static and real time information. The analysis of the organization information will therefore be dynamic and all-inclusive exercise (Williams & Williams, 2010, p. 48). Business intelligence has contributed to deeper understanding of the customers’ needs and behavior by the organization. The companies are thus able to respond positively to the preferences of their customers. Development in BI on Industries Several industries experiences positive growth impact on integration of the business intelligence in their systems. These impacts can be industry specific, in that it addresses particular challenges that an industry faces. Business intelligence can also increase an industry’s competitive edge. The following are development on industries by the BI (Muntean & Muntean, 2013 p. 56) In consumer goods industries, business intelligence can be applied in the captivating the customers’ loyalty in the consumer industries. Business intelligence facilitates such an initiative in a company by tracking of the relevant trends in the marketed and specific consumer preferences by employing a predictive model and thus stays ahead of its competitors. In consumer industries as well, there is need to maximize sale and their marketing power. Consumer industries may also require defining their supply chain. Business intelligence contributes to the development of such areas by acquiring wider visibility of the dynamics of demand as well as supply and allows for immediate response (Thamir & Poulis 2015 p. 55) In the financial service industries, there is need for rapid response to the ever changing market conditions. The industries are also faced with dynamics in the market and regular depreciation in the asset valuations. Business intelligence has led to the development in these factors by gaining the visibility into various transactions and the optimization of the processing lifecycle. These financial industries also face a very huge data capacity that requires accurate analysis. In respect to this factor business intelligence has drastically revolutionize data analysis in the industries. Apart from handling stored information, BI has capacitated the financial industries to accurate analyze real time information. Business intelligence has also addressed the stability and cost reduction initiatives and has streamlined the business IT. This is a consequence of the maximized increased performance of all assets in the industries by incorporation of the business intelligence (Azvine, Cui, & Nauc, 2005, p. 64). In the finance industry, business intelligence has resulted timely identification of growth opportunities and efficient risk management. Business intelligence has led to the maximization of the value of return with enhanced portfolio performance and valuation strategies. In the manufacturing industries, business intelligence has led to the real time visibility down the value chain. It has also allowed for the collaboration of the manufacturers’ partner and their consumer. Business intelligence has also led to the agility and increase in the profitability. These development exhibited by the introduction of business intelligence in a manufacturing industry is achieved through induction of specific changes in an industry. These may entail changes such as, enhancement of the real time management of the performance across business. Business intelligence has also led to the development of effectiveness in the manufacturing processes that entails methodologies. In healthcare industry, business intelligence has led to the development of various aspects and efficacy of the operation in the healthcare industry. BI has also eased the bulky information management in the hospital. In the context of the health care industries, business intelligence has led to landmark efficient analysis of the data especially during the diagnosis processes. BI has also improved the performance of other programs at lower operational costs. The business intelligence system, in hospital set up where they are applied, as resulted to empowering of the healthcare workers with the information regarding each particular decision made (Ivan, 2014, p. 20) Business intelligence has therefore led to a significant development in the industries. The information management and operational efficacy are realized in many most of the industries engaging BI in to their systems (Hsinchun, Chiang, and Storey, 2012) Importance of Business Intelligence in the Society Business intelligence has also impacted positively to the society since its integration into the corporate world. Business intelligence has resulted to the development of the society in several ways. Access to information Due to the incorporation of the business intelligence into commercial processes, members of the society have an increased access to relevant information regarding the different company. This occurs as a consequence of many industries incorporating their customers into their business intelligence system. There is access to the updated and real time information about any given company (Şerbănescu, 2012, p. 118) Access to variety of goods and services Business intelligence allows for the conveyance of particular information regarding the products of a given business organization. Therefore, it eases the access to such information to the members of the society (Sabanovic & Søilen, 2012, p.17) In conclusion, business intelligence has impacted greatly on the dynamisms of the corporate world, in regards to information management. Overt-time significant results have been observed due to the incorporation of business intelligence into daily business operations. References Al-Aqrabi, H, Liu, L, Hill, R, & Antonopoulos, N, 2015, Cloud BI: Future of business intelligence in the Cloud, Journal Of Computer & System Sciences, 81, 1, pp. 85-96, Academic Search Premier, EBSCOhost, viewed 20 April 2015. Azvine, B., Cui, Z., & Nauck, D. D. (2005). Towards real-time business intelligence. BT Technology Journal, 23(3), 214-225. Borrajo, M, Baruque, B, Corchado, E, Bajo, J, & Corchado, J 2011, hybrid Neural Intelligent System To Predict Business Failure In Small-To-Medium-Size Enterprises, International Journal Of Neural Systems, 21, 4, pp. 27. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS quarterly, 36(4), 1165-1188. Evelson, B., & Nicolson, N. (2008). Topic overview: Business intelligence. Forrester Research Papers Business Process Professionals. Hsinchun, C, Chiang, R, & Storey, V 2012, Business Intelligence And Analytics: FROM BIG Data To Big Impact, MIS Quarterly, 36, 4, pp. 1165-1188, Business Source Complete, EBSCOhost, viewed 20 April 2015. Ivan, M 2014, Characteristics of In-Memory Business Intelligence, Informatica Economica, 18, 3, pp. 17-25,. Johnson, M. W., Christensen, C. M., & Kagermann, H. (2008). Reinventing your business model. Harvard business review, 86(12), 57-68. Khan, R, & Quadri, S 2014, Business Intelligence: An Integrated Approach, International Journal Of Management & Innovation, 6, 2, pp. 21-31. Muntean, M, & Muntean, C 2013, evaluating A Business Intelligence Solution. Feasibility Analysis Based On Monte Carlo Method, Economic Computation & Economic Cybernetics Studies & Research, 47, 2, pp. 85-101. Muntean, M, & Surcel, T 2013, Agile BI - The Future of BI, Informatica Economica, 17, 3, pp. 114-124, Business Source Complete, EBSCOhost, viewed 20 April 2015. Negash, S. (2004). Business intelligence. The Communications of the Association for Information Systems, 13(1), 54 Sabanovic, A, & Søilen, K 2012, Customers Expectations and Needs in the Business Intelligence Software Market, Journal Of Intelligence Studies In Business, 2, 1, pp. 5-20. Şerbănescu, L 2012, Necessity To Implement A Business Intelligence Solution For The Management Optimization Of A Company, USV Annals Of Economics & Public Administration, 12, 2, pp. 114-122. Shyandilya, A, Gupta, S, & Goud, K 2014, Open Source Business Intelligence for Business Economic Development, Journal Of Management Policy & Practice, 15, 5, pp. 102-106. Thamir, A, & Poulis, E 2015, Business Intelligence Capabilities and Implementation Strategies, International Journal Of Global Business, 8, 1, pp. 34-45, Business Source Complete, EBSCOhost, viewed 20 April 2015. Turban, E., Sharda, R., Delen, D., & Efraim, T. (2007). Decision support and business intelligence systems. New York: Pearson Education India. Tuţă, M, Zara, I, Orzan, G, Purcarea, V, & Orzan, O 2014, competitive Intelligence- An Enhancement To Business Intelligence, Economic Computation & Economic Cybernetics Studies & Research, 48, 2, pp. 1-11, Business Source Complete, EBSCOhost, viewed 20 April 2015. Venkatraman, S, & Brooks, A., 2012, The Quest For Business Intelligence, Proceedings For The Northeast Region Decision Sciences Institute (NEDSI), pp. 244-254.. Watson, H, J., 2008, Why Some Firms BI Efforts Lag, Business Intelligence Journal, September, Watson, H. J. (2009). Tutorial: Business intelligence-Past, present, and future. Communications of the Association for Information Systems, 25(1), 39 White, C., 2005, The Next Generation of Business Intelligence: Operation BI, DM Review, 15, 5, pp. 34-37, Business Source Complete, EBSCOhost, viewed 20 April 2015. Williams, S., & Williams, N. (2010). The profit impact of business intelligence. Boston : Morgan Kaufmann. Wixom, B, Ariyachandra, T, Douglas, D, Goul, M, Gupta, B, Iyer, L, Kulkarni, U, Mooney, B, Phillips-Wren, G, & Turetken, O., 2014, The Current State of Business Intelligence in Academia: The Arrival of Big Data, Communications Of The Association For Information Systems, 34, pp. 1-13, Business Source Complete, EBSCOhost, viewed 20 April 2015. Read More
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