Business Intelligence
Essays

How the Internet Is Changing eBusiness Planning

The overarching objective of the Go Forward Strategy was to continually accelerate the gains made in customer relationship management (CRM), customer service, operations and the maintenance, repair and overhaul of their jets. What Continental was after was the ability to unify their entire operation into a highly integrated, coordinated customer-based platform that could be used for streamlining every aspect of their operations to exceed customer expectations and deliver exceptional value (Watson, Wixom, Hoffer, Anderson-Lehman, Reynolds, 2006). The Go Forward strategy further galvanized Continental unto a very focused strategy for ensuring their Enterprise Data Warehouse (EDW) turned into a Powerful catalyst for customer-driven change (Watson, Wixom, Hoffer, Anderson-Lehman, Reynolds, 2006). The $30M investment in the Go Forward Strategy was one of the most effective investments in technology any airline has ever made in technology, with Continental netting a gain of $500M in increased revenue and cost savings. In the first year alone, Continental was able to eradicate $7M in fraud and drastically reduce the threat of bankruptcy. In addition to all of these benefits, the company skyrocketed in customer experience ratings and customer satisfaction polls, becoming over time the most respected and favored airline (Watson, Wixom, Hoffer, Anderson-Lehman, Reynolds, 2006). Another significant benefit was the ability to integrate many diverse sets of customer, financial and operational data into a single system of record, which gave Continental a very significant competitive advantage over competitors. With the depth of analytics and business intelligence that Continental Airlines has been able to achieve, they are transforming intelligence and knowledge into a competitive strength which is the most advanced and mature level of analytics decision making there is (Cunningham, Il-Yeol Song, Chen, 2006). All of these benefits are also allowing the Continental culture to heal from three bankruptcies and become stronger as a result, which has also given the entire company a chance to resurrect itself and serve customers more effectively than ever before.

The objective of this work in writing is to examine a multinational organization that has experienced cultural conflicts both internal and external to the organization. This work will define the culture conflict and…

Of the many types of forecasts that would need to be created to deliver accurate location analysis and expansion plans, the most critical of all are geo-economic analysis of potentially high growth areas that would not cannibalize the sales of existing Burger Queen restaurants. Location-based and impact assessment programs would need to be created for each of the specific locations being considered to ensure other restaurants' sales and the potential business of other franchisees is not negatively impacted by the decision to expand (Leung, 2003). Location and impact assessments would need to take into account the composition of the target market in the immediate radius of the potential sites by socio-economic, demographic, psychographic and existing brand loyalties as well. All of these analyses could be completed using data dining and advanced analytics processes and procedures to ensure orthogonality of each location relative to another and consistency of selection criteria being used (Prewitt, 2007). With econometric and customer segmentation data, both simple and gravitational methods for trade area analysis next need to be completed. Using a Geographic Information System (GIS) to integrate together data sets of population size, demographic composition, per capita incomes, discretionary income and an assessment of local competition . the manager for Burger Queen could have an excellent idea of where each store location needs to be based. Using GIS data to further differentiate by open retail locations could also give the manager greater insight into how best to geographically position the potential Burger Queen locations for greatest competitive advantage against the competition as well (Prewitt, 2007). In addition to accomplishing these tasks from an analytics standpoint, the GIS system could also tell the manager were competitors are the strongest, meaning they are areas that are unassailable in terms of market development (Leung, 2003). For example of there is a specific area of the city or region that is highly loyal to Subway or McDonald's, the GIS systems could quickly show that data, indicating high concentrations of very brand-loyal customers. This would make launching a store in any of these locations extremely difficult.

Research Proposal on Business Intelligence Diffusion in Organizations

This paper reveals the technique to develop a database in the DB2 environment. With DB2 software, the paper creates database for local community library to track the following information: • Customer first name and last name • City, state and zip code • Customer Social Security Number • Customer email, phone number and birth date • Date of applying for a library card. The paper also develops a database for students to generate the following report: • Students living in California • Student living outside California • Name of students taking ITM440 course • Name of students living in California and take ITM440 course • Student names, courses taken, and letter grade for each course.

Service-Oriented Architectures in Computing

Bendoly, E., Rosenzweig, E., & Stratman, J.. (2009). The efficient use of enterprise information for strategic advantage: A data envelopment analysis. Journal of Operations Management, 27(4), 310.

In this paper we determine benefits of data mining to the businesses when employing: 1. Predictive analytics to understand the behavior of customers 2. Associations discovery in products sold to customers 3. Web mining to discover business intelligence from Web customers 4. Clustering to find related customer information The paper also assesses the reliability of the data mining algorithms and then decides if they can be trusted and then predict the errors they are likely to produce. An analysis of the privacy concerns raised by the collection of personal data for mining purposes is also conducted.

Analytics, Business Intelligence (BI) and the exponential increase of insight and decision making accuracy and quality in many enterprises today can be directly attributed to the successful implementation of Enterprise Data Warehouse (EDW) and data mining systems. The examples of how Continental Airlines (Watson, Wixom, Hoffer, 2006) and Toyota (Dyer, Nobeoka, 2000) continue to use advanced EDW and data mining systems and processes to streamline their business models are a case in point. The greater the level of economic uncertainty, perceived and actual risk in any given strategy or endeavor, the more the reliance on EDW, data mining and advanced forms of predictive modeling including analytics (Sen, Ramamurthy, Sinha, 2012). From this standpoint, the emerging areas of high growth in the global economy are attracting a high level of investment in EDW, data mining, predictive modeling and analytics. The latest figures illustrate how valued EDW and data mining are in enterprise today. According to industry research and advisory firm Gartner, the EDW and data mining market began 2011 with a global value of $23.2 billion with a projection of market growth of 7% per year through 2015, making it one of the largest and perennially growing enterprise software market (Sen, Ramamurthy, Sinha, 2012). Gartner has defined the EDW and data mining architecture as being comprised of the architectural design, repository and execution platform. These three core components are how this research and advisory firm analyze the market from a software component standpoint, looking at the relative adoption of each EDW and data mining component (Sen, Ramamurthy, Sinha, 2012). The intent of this analysis is to evaluate the benefits and current trends in EDW and data mining, evaluating Continentals' and Toyota's best practices and results achieved. Additional objectives include an assessment of EDW and data mining optimization techniques, recommendations for storage solutions and an analysis of a potential EDW process workflow predicated on a Customer Relationship Management (CRM) system.