Impediments to Integration of Business Analytics With Business Intelligence and Knowledge Management Data
Impediments to Integration of Business Analytics
The consumer is out to emphasize that ‘he is the king’. Accordingly, his demands for enhanced quality and timely deliveries of the products’ order by them has revolutionized the management gurus to evolve new business strategies to enhance the intensity of coordination and communication between the seller and the consumer. Two key business strategies that have evolved from this exercise are business intelligence and knowledge management, which have contributed effectively to the progression of new and competent management strategies. Great benefits have also been achieved in for form of cost reduction, thereby increasing the sales and profitability of the organization.
Information routing is a critical link between the production department up to the client and the optimal course of production steps are directly related to business intelligence and knowledge management. Accurate data systems are imperative properly to link every production steps to customer delivery. Otherwise an optimal and efficient distribution system cannot be envisaged. An efficient distribution strategy will not only track the flow of materials at every stage, but will also contribute to tremendous cuts in logistics costs (Kathe, 2008).
Impediments to Integration of Business Analytics with Business Intelligence and Knowledge Management Data
Business intelligence and knowledge management are vital cogs in the supply-chain management and have an easy working interface, are cost effective, and easy to use. These data facilitate easy access to multiple factors such as inventory levels, production capacities, production plans and work-in-progress, to enable a proper production planning and scheduling, to satisfy customer demands and aspirations every day (Lee, and Billington, 1995).
The benefits of amalgamating business intelligence and knowledge management data, is there for all to see, and it follows that useful data when available all levels of networking, provides the ammunition for the quick and successful purchase and raw-materials, their conversion through the work-in-progress stage to the finished goods stage when a proper distribution system would do well to give satisfaction to the customers.
It is now, important to discuss on the value of knowledge information vis a vis knowledge business intelligence and the method of distribution of these assets within the management system, so that it works in all fields of applications.
No industry worth its salt, can succeed without an efficient and comprehensive information system, which is imperative for improving not only the performance of the organization nut also enhancing the productivity besides achieving telling cost reduction figures by optimal application of data pertaining to production tracking, quality control, receiving, transport operations and production.
In short, data is everything, but it keeps accumulating so fast, that storage of this data becomes seemingly much more difficult. Assimilating and using them properly, are possible only with the help of Business Intelligence and Knowledge management, which has benefited the companies by their ability to create large receptacles of data storage.
Business Analytics – Advantages of Integration with Business Intelligence and Knowledge Management
The enhancement in the area of business analytics has shown a phenomenal improvement in the recent years, and business users have profited immensely by the improved insights from the operational data stored in transactional systems (Ron, et al., 2000, 2001). The integration of click-stream records from the websites with demographics and other data are sensational in volume (Ralph, and Richard, 2000). Such extensive databases demand an amalgamation of automated analysis and human resources to make them extremely pertinent to the requirements of business users by finding a pattern in the data, thereby helping to sift conveniently through the information available in the site and adopt the best trends for the future prosperity of the company (Ron, et al., 2002).
The business user who is the ultimate consumer of these facilities is looking for these efficient tools to improve the various features of his business, starting with profit, time management and market results. In the present business scenario, one can see an amalgamation of business analytics with business intelligence and knowledge management and data mining, thereby creating an umbilical cord connecting all the above principles to enable faster data analysis, and use this information by the organizations, quickly to alter their positions on multiple areas like fraud detection, marketing, supply chain optimization, and sales. In the present context, several business users are using this data in a highly pro-active manner for better returns on investment (Michael et al., 2000 and Usama, et al., 1996).
Impediments to Integration of Business Analytics
However, notwithstanding the yeoman service rendered by business analytics in the collection, identification and storage of extensive data, and its integration with business intelligence and knowledge management data, to provide useful and on the spot intelligence to the user, there are several factors within the current business and technology systems that stand as an impediment to its effective utilization by the ultimate user (Ron et al., 2002). Because of the gap between the information from the analytics and the users’ aspirations, project great divergence occurred. Some of the factors that stand on the way of the development are evaluated hereunder:
a) The overall time consumed for data collection analysis, and action is considered too long a period, for the ultimate user to benefit from the data. Business constraints, which impose a limit on an efficient cycle time, pose a problem, and if they persist, the user will continue to suffer.
b) To succeed with this cycle, the factors of time an analytical expertise for data analysis should be restrained.
c) Unrealistic expectation on analytics, and data mining should be replaced with realistic estimates and clear business metrics and goals.
d) Data analysis must be given the priority it deserves. Once the goals are set and metrics identified, no time should be lost in analyzing the data so collected. In several instances, it has been noticed that data analysis is an afterthought.
e) Mostly, analysis results are released only to a leaner audience, and hence never reach the wider spectrum of business users. Therefore, better results will be seen only when the analysis results are distributed to a larger audience. Moreover, the output should be translated into appropriate language and visualizations that will be understood by the user.
f) Consolidation of data from multiple sources is preferred, and it is to be borne in mind that the process of extract-transformation-load is not only expensive but also complicated.
A lot of emphasis is being placed on Information routing between the production departments up to the client. These links are being used to manage a business more intelligently and to accurately make deliveries to clients. The main feature of this distribution strategy is to track the flow of materials at every stage and at the same time aim to make cuts in logistics costs The effective combination of business analytics with business intelligence and knowledge management data has given the present business managers the magical wand to be able to forecast manufacturing requirements, production distribution and adopt key inventory procedures for optimizing sales. Hence, businesses are using this strategy to grow (Kathe, 2008).
Kathe, H. (2008). Research Paper Article: Business Intelligence and Knowledge Management, Retrieved from http://www.mightystudents.com/essay/Business.Intelligence.Knowledge.51740, 2008-11-21.
H.L. Lee and C. Billington, “Evolution of Supply Chain Management Models and Practice at Hewlett-Packard Company,” Interfaces, 25, 5, 1995, 42-63.
Ron, K; Neal, J.R.;Evangelos, S. (2002). Emerging Trends in Business Analytics, Retrieved from ai.stanford.edu/~ronnyk/cacmEmergingTrendsInBI.pdf, Volume 45, Number 8, Aug 2002, and pages 45-48.
Ron, K. & Foster, P. (2001), Applications Of Data Mining To Electronic Commerce: Data Mining And Knowledge Discovery, 5(1/2), 2001, Retrieved from http://robotics.Stanford.EDU/users/ronnyk/ecommerce-dm.
Ron, K.; Carla, B.; Brian, F; Llew, M. & Zijian, Z. (2000), KDD-Cup 2000 organizers’ report: Peeling the Onion, SIGKDD Explorations, 2(2):86-98, 2000, Retrieved from http://www.ecn.purdue.edu/KDDCUP.
Ralph, K. & Richard, M. (2000). The Data Webhouse Toolkit: Building the Web-Enabled Data Warehouse.
John Wiley & Sons, 2000.
Michael, J.A.B. & Gordon, S.L. (2000) Mastering Data Mining, John Wiley & Sons, Inc., 2000.
Usama, M.F.; Gregory, P. & Padhraic, S. (1996). From data mining to knowledge discovery: An overview. In Advances in Knowledge Discovery and Data Mining, chapter 1, pages 1-34. AAAI Press and the MIT Press, 1996.
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