Essay Topic Hub

Data Warehousing
Essays

73+ paper examples, study guides & outlines

73 papers
1 subject area
UG & Grad levels
Free to browse
About This Topic AI GENERATED

Data warehousing is the practice of collecting, storing, and managing large volumes of structured data from multiple sources into a centralized repository designed to support organizational decision-making. Students encounter this topic in information systems, database management, and business technology courses, where it serves as a foundation for understanding how organizations handle data at scale. The subject is academically interesting because it sits at the intersection of technical architecture and strategic business application, requiring students to think about both how systems are built and why they matter for performance and competitive advantage.

The archived papers on this topic approach data warehousing from several distinct angles. Some focus on real-world implementation, examining how specific organizations deploy warehouse systems to manage customer data and improve business outcomes, as seen in papers analyzing corporations and platforms like Sportsline.com and First American Corporation. Others take a more conceptual or comparative approach, distinguishing between data warehouses, data marts, and related tools such as data mining and business intelligence. A smaller number address specialized applications, including clinical decision support systems, showing how warehousing extends into healthcare and other data-intensive sectors.

A strong essay on data warehousing should establish a focused thesis that connects system design choices to measurable organizational outcomes rather than simply describing how a warehouse works. Evidence drawn from implementation cases, performance metrics, or system comparisons tends to carry the most analytical weight. One common pitfall is treating data warehousing as purely technical — strong papers consistently tie infrastructure decisions back to business strategy, customer management, or organizational performance to demonstrate why architectural choices carry real consequences.

Sort by:
Paper Undergraduate
Warehousing Book for an Airlines
For an airlines company, how can strategic information increase the number of frequent flyers? Discuss giving specific details.
Paper Undergraduate
Desirable to Separate the Technical
The idea of separating the technical issues of data warehousing from the political ones is by no means new. The reason why it is still discussed, however, seems to be that no one has yet been able to do it properly.
Research Paper Undergraduate
Harrah's database strategy and competitive advantage
Harrah's use of CRM and Analytics to Increase Gambling Revenue
Paper Doctorate
Continental Go Forward Strategy the Overarching Objective
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.
Paper Undergraduate
Comparison of database management systems
Appendix a Project Process Integration Diagram
Research Paper Undergraduate
Organizational and technical issues in global information systems management
The increasingly dynamic and fast-paced advancement of information technology is rapidly changing the business world. In this environment, identifying organizational and technical issues of significance in the…
Paper Undergraduate
Business intelligence and organizational change
Research Proposal on Business Intelligence Diffusion in Organizations
Paper Doctorate
CRM and Data Warehousing Strategy at First American Corporation
The transformation of First American Corporation (FAC) from a $60M loss in 1990 to $211M in 1998 can be attributed to the greater levels of effort and high priority placed on putting the customer and their needs at the center of the business. The many investments in Customer Relationship Management (CRM) systems, analytics platforms, integration of marketing, sales and financial reporting systems which combined to form the VISION data warehouse are pivotal to the ongoing efforts at attaining profitability and performance. As FAC has been able to achieve significant results using the Tailored Client Solutions (TCS) strategy, the most critical success factors of this framework can all be attributed to how they unified customer experiences across all segments First American serves. The initial results have been impressive, yet there is much more work that needs to be done in order to gain even greater profitability and customer loyalty. The intent of this assessment is to illustrate how FAC can continually gain greater market share while increasing customer loyalty and profitability in the process. The formidable investment in analytics, BI and CRM systems is paying off, yet there are additional initiatives FAC can take to further grow beyond its current constraints and become a dominant force in the industry. The Tailored Client Solutions (TCS) strategy that includes client information, flexible product lines that can be customized to customers' specific needs, support for distribution management, and consistent service are also proving to be a scalable, highly effective platform for serving the three dominant customers as well. What needs to be included is more of a focus on how to transform these customer experiences into a foundation of ongoing trust. With the series of insights and recommendations gained from this analysis, FAC will be able to become a trusted advisor to its most valuable clients while also using the pervasive analytical platform to better understand their needs, preferences, wants and requirements. It is the intent of this analysis to show FAC how best to accomplish greater customer loyalty, increase customer trust and understand customers to a greater depth and with greater insight than ever before. All of these efforts are unified by deliberately choosing to deliver a perfect customer experience to every customer on every interaction.
Essay Undergraduate
Software Development Life Cycle SDLC
Requirements engineering is a fundamental activity in systems development and it is the process by which the requirements for software systems are identified, systematized and implemented and are followed through the complete lifecycle. Traditionally engineers focused on narrow functional requirements. Now it is being argued by Aurum and Wohlin (2005) that focusing only on the functional and non-functional aspects of the system is no more appropriate. The developers have to concentrate on the entire business system for which it provides solutions even though some of the aspects may be out of the system. Thus there are complexities that arise based on the requirements of the system and the clients for which detailed analysis is required firsthand.
Paper Doctorate
Data Warehousing and Data Mining
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.