Recent research has shown that many companies are data-rich but still information poor, despite having invested heavily in IT. This is because they have not invested in Business Intelligence (BI) systems, which can be exploited to improve business performance, and increase profits. Business Intelligence models are used to obtain business information and put it in one database, and this information is used by the company for different types of analysis. Upstream functions in the BI design support information needs of small teams and individual experts in the company. Downstream functions on the other hand provide cost-effective access to data that everyone uses.
Through these functions, the business administration and its experts are able to analyze their enterprise intelligence to drive business performance and profits. For instance, they may be able to determine who their most valuable customers are, what they want, and what measures can be applied to maximize profits from these customers. As a result, well structured business decisions are made with respect to the targeted recipients, for instance to motivate and retain them. Information collected and used for this purpose often includes; customer satisfaction analysis, customer complaint analysis, individual customer service history among others as may be deemed necessary.
BI systems must be connected to specific goals of the business, actions, analyses and decisions that will result in improved performance for the organization. In order to involve the whole enterprise in the BI process, the leadership of the business should guide a cultural change process within the enterprise. They should ensure that the use of business information and analytics is ingrained in the way the business is operated. These changes should include among others, re-defining of information requirements and the role information plays in the enterprise.
In configuring a BI system, focus should be on what the users want delivered, and why this information is required. Institutional knowledge should be combined with the information collected to optimize decision-making in the business. The BI vision should be shared with other leaders and management of the business so that they are all involved in the creation of the system, and the staff should be shown how the system can make their work easier.
The literature review of the article,” Forecasting Risk Impact on ERP Maintenance with Augmented Fuzzy Cognitive Maps” is titled; “Background” and it is about EPR maintenance and risk. The author reviews literature focused on the subject of maintenance and risk, using industry to describe the research and present the EPR life cycle. Maps, graphs, models and matrices have been used by the author to present his work visually, to illustrate for instance the FCM building process and the effect of choosing a value to inference or transform results (Salmeron & Lopez, 2011).
One outstanding difference between the article’s literature review and the typical sections found in a research article is that it does not contain the usual features of an introduction, body and conclusion. It however has well structured paragraphs. In addition, it does not contain full bibliographical details as required of literature reviews. Some of the roles of a literature review are to give an overview of the field under study, and show what others have written on the topic (Glatthorn & Joyner, 2005).
In the final project for the course, I will try to show how these prevailing ideas fit into my thesis, and how they agree or disagree with each other. I feel that I need to improve on my literature choice, find the relevant literature for my study, as not every material will qualify as literature for the literature review of my dissertation. Not much guidance was offered by the article in choosing a suitable dissertation topic for my research.
References
Glatthom, A. & Joyner, R. (2005). Writing The Winning Thesis or Dissertation: A Step-by-Step Guide. Corwin Press,Thousand Oaks, CA (2nd edition) 32-35.
Salmeron, J. & Lopez, C. (2011). Forecasting Risk Impact on ERP Maintenance with Augmented Fuzzy Cognitive Maps. Software Engineering, IEE Transactions, Vol. PP Issue 99.