Abstract This paper explains the general concept of datawarehousing . The paper explores the use of datawarehousing at Humana, Inc., a giant healthcare company. The paper explains the strengths and weaknesses of datawarehousing at Humana.
From the Paper "Humana Incorporated is a healthcare company with ... billion in revenue and stakeholders that include medical professionals, employees, corporate clients and other agents. Until early ..., the company had separate databases for various parts of its business making it difficult to understand the large amounts of data that was being generated by the organization and even making it difficult for healthcare providers to have access to all appropriate information on occasion. As a result, the company developed a data warehouse that uses two discrete data ..."
Abstract This paper explores datawarehousing in terms of data mining with intelligent agents such as bots and ants and clarifies the ethical dilemma posed by the use of such data.
From the Paper " Data warehousing is no longer simply a storage system for data. Today's data warehousing involves innovative technological software, automated agents known as intelligent agents robots-or bots and ants. These agents ..."
Tags:datawarehousing, data mining, intelligent agents, robots, bots, ants, personalization, ethics
Abstract Many authors have provided an enormous amount of literature on datawarehousing concepts, processes, and characteristics. However, the key to a successful data warehouse is proper implementation. Previous publications have come up with different ideas and methods to implement a data warehouse successfully. Managers don?t have enough time to go through all these readings This paper provides an integration of the various implementation guidelines with practical examples ranging from the FBI to Wal-Mart.
I. Introduction
II. Basic Definitions and Concepts of DataWarehousing III. Brief History of DataWarehousing IV. DataWarehousing Characteristics
V. Drivers of DataWarehousing VI. DataWarehousing Process
VII. Current Issues and Practices of DataWarehousing VIII. Guidelines in Implementing a Data Warehouse
IX. Conclusions, Limitations, and Future Research Guidelines
X. References
From the Paper "Data warehousing is one of the hottest developments of the 1990s. In 1998, the expenditure on data warehousing was $14 600 million (META Group 1996). It is estimated that 95% of the Fortune 1000 either have a data warehouse or are planning to develop one (META Group 1996). A data warehouse may help increase a company's sales by supporting decision-making and understanding consumer behavior. For example, Office Depot sales increased by $117 million after investing on data warehousing (Anthes 2003)."
Abstract This paper considers key factors regarding datawarehousing. It looks at the goal of datawarehousing and the differences of datawarehousing and relational databases.
From the Paper "Data warehousing is particularly popular in environments which have complex data requirements and a broad spectrum of data types contained in its database. The goal of data warehousing is to take full advantage of the power of hardware to contain large quantities of data and use the databases to manipulate that data. Although not yet implemented across all computing environments data warehousing is becoming popular as hardware becomes more powerful and cost effective..."
Tags: distributed datawarehousing systems, data warehouses
Abstract This paper discusses the realities of a new company in the datawarehousing/data services industry and the exigencies of thriving in this field. The types of database products, services and supporting infrastructure are discussed as well as business processes and market requirements. The corporation as a business entity is also discussed in terms of its use and implementation of current and emerging technologies, change management techniques and the Internet as a tool and device.
From the Paper "PanData is a data intelligence business concentrating on the data services industry: warehousing, intelligence, customer relations management (CRM) and list generation. PanData amasses data on the Retail & Foodservice Industries across the North American continent. It has over 70k unique companies in its database. The collected data consists of the following data elements: company contact information, personnel--CEO to mid-level management & buyers, trade areas, products, franchise information, parent companies, locations--geo-codes and addresses, market share information, technology related information--POS hardware/software, scanners, software systems, servers (corporate and in-store), databases/data warehouses, communications and connectivity, EDI, RFID, and Wifi. The types of data are considerable and this list is not all-inclusive. PanData envisions revenue in excess of 10m annually and this revenue is PanData's long-term goal. "
Abstract This paper explains that one of the major challenges in any data-warehousing project is the proficient amalgamation of large volumes of information of data available for analysis, which must include the customer database, the supplier database and the distributor database, all well integrated into the data-warehousing project. The author points out that datawarehousing is an expensive undertaking especially because the beer industry depends extensively on distributors and suppliers and must maintain data on their extensive logistic and distributing channels. The paper stresses that knowledge acquisition is the first step for gaining advantages in the market place; therefore, datawarehousing should facilitate internal research to identify new ways of doing tasks within the organization and systematic problem solving efforts. Illustrations.
Table of Contents
Introduction
Objective
Problem Statement
Hypothesis
Methodology
Sampling Procedures
Sources of Data Literature Review of DataWarehousing Discussion
Recommendations
Conclusion
From the Paper "Interviews will also be conducted with an additional 50 companies to identify the applications, if any, of data warehousing and the impact that this concept has made on the organization as a whole. This interview will be based on a fixed set of questions. All of the questions will be discussed with every individual. Interviews, in addition to data collection relevant to the question asked, can also identify the non-verbal reactions to the questions asked. Non-verbal communication could be in the form of the comfort level that the interviewee displays, the hand and eye movements and the facial expressions that might be made. The success of data collection using the interview methods is also dependent on the skill and personality of the interviewer. An interviewer who is able to introduce a level of comfort and camaraderie in the interviewee may be able to get more realistic and correct answers. Questions used in the study can be open-ended, where the subject is free to answer the question and discuss relevant issues that might be relevant to the question."
A study proposal to further explore the degree to which datawarehousing has been effective in assisting companies with the process and activities of forecasting, as well as in gaining competitive advantage.
Abstract This paper presents a study that aims to further establish the degree to which datawarehousing has been used by organizations in achieving greater competitive advantage within the industries and markets in which they operate. In chapter One of this paper, an introduction of the study is provided, with the overall aims and objectives of the research proposal discussed. Chapter Two involves literature review on the subject. Chapter Three explains the research methodology, and Chapter Four uses this proposal on four case studies. Finally, Chapter Five provides a discussion and a review of the results.
Table of Contents
Introduction
Aims of the Study
Objectives of the Study
Significance of and Justification for the Study
Literature Review
DataWarehousing: Background
Deployment Obstacles
Data Warehouse Design
Benefits and Disadvantages Associated with DataWarehousing Conclusions
Research Methodology
Research Design
Data Collection
Data Analysis
Results of the Study
Case Study One: Godrej Consumer Products Limited
Case Study Two: Safeway
Case Study Three: Wachovia Corporation
Case Study Four: Standard Chartered Bank
Discussion
Review of the Results
References
From the Paper "Three of the companies were in periods of ongoing growth in relation to the evolution of data warehousing and its use within the companies while one company was still in the initiation-early deployment phase. While it would appear that some were in the maturity stage, most had specific plans for using the data warehouse as the basis for launching new business activities and strategies. On the basis of this evidence, it is particularly important to note that even during the initiation phase, it was possible for companies to begin to recognize gains in competitive advantage, which further supports the potential for data warehousing to aid businesses in gaining competitive ground."
Abstract This paper looks at the cost effectiveness of datawarehousing with consideration toward high availability and business continuity plans. The paper reviews literature and addresses 'real world' examples from companies that use datawarehousing in their business continuity plans. According to the paper, all businesses should have continuity plans and those plans should include datawarehousing set up with high availability.
Table of Contents:
Outline
Abstract
Introduction
Discussion
Conclusion
References
From the Paper "Organizations are increasingly dependent upon IT systems and infrastructure. Eventually, these organizations are subjected to many risks, so their business is inherently risky. "Even brief business interruptions can mean reduced revenues, lost customers or reduced market share," says Davies and Walters (Davies and Walters, 1998, p.5). This is true of all businesses, not just banks or hospitals, and since more businesses are doing their business on the Web, there is the potential for much larger amounts of lost revenue if people cannot access the site or find the information that they need, or if those same people have difficulty processing an order. The same is true of companies that do not do business on the Web, but Web business makes things much faster and more convenient for many people. It also creates more of a chance for error, which is why redundancy and data warehousing are so important."
Abstract The paper examines the datawarehousing project Wal-Mart implemented in June, 2007 to streamline business analytics and reporting from its stores, in addition to synchronizing demand with suppliers through its retail link system. The paper emphasizes that, for Wal-Mart, information is what makes its entire supply chain and retail operations work. The paper concludes that the use of data warehouses significantly contributes to Wal-Mart's ability to stay ahead of the trends and costs that influence its business while being better aligned with customers' needs.
Outline:
Introduction
Wal-Mart's Selection of DataWarehousing Strategies
Conclusion
From the Paper "For Wal-Mart, creating data warehouses that provide demand visibility back to its supply chain is critical if the key measures of its internal efficiency are going to be attained. These include inventory turns and delivering orders that are flawless in execution and quality. The timeline for Wal-Mart's data warehousing project began in 2005 when the CIO and VPs in the IT Division realized that there were several hundred stores that were not specifically having their data read into the Master Data Management (MDM) hierarchy that had been created. Further, these retail locations, many of them superstores only had limited visibility into what products would be available for delivery in the next 72 hours (Duff, 14) which made the many processes required for forecasting demand quite time consuming and imprecise (Foote, Krishnamurthi, 15). All of these factors combined with the growing reliance on the Retail Link system in Wal-Mart for planning new store expansions made an enterprise-level data warehousing project a high priority."
Presents a research proposal that focuses on datawarehousing and its utilization as a strategic weapon to gain competitive advantage within organizations.
Abstract These paper presents the overall aims and objectives of a research proposal on datawarehousing. The paper then provides information that documents the state of the art in research focused on datawarehousing within organizations, as well as a justification for further research in this area. Finally, the research methodology to be employed, the deliverables associated with the research, and a work plan are delineated.
From the Paper "As noted by Foote and Krishnamurthi, (2001), until very recently, the forecasting process used by companies was relatively subjective and was dependent upon the opinions of company executives, sales force analysts, and industry analysts, who were not always extremely reliant in aiding the company to in the production of satisfactory outcomes. Quite frequently, as reported by Foote and Krishnamurthi, companies found that they had missed the mark in forecasting and consequently had failed in achieving profitability, reliability and a competitive vantage position in their industry. Thus, companies are increasingly recognizing the value of investing in an information system to support their forecasting process. According to Foote and Krishnamurthi, a data warehouse has come to be identified as assuming a pivotal role in gaining the knowledge needed by companies to implement reliable systems for forecasting."
Abstract The paper relates that real-time datawarehousing is emerging as a strategy many manufacturers are relying on to better synchronize their efforts with suppliers, commercial customers, warranty centers and service centers, in addition to channel partners. The paper proposes a dissertation that will evaluate whether manufacturers are attaining a positive return on investment (ROI) for adopting a real-time warehousing architecture and strategy. The paper outlines the conceptual framework and methods and methodologies for this dissertation.
Outline:
Introduction
Conceptual Framework
Analytical Methods and Methodologies
Conclusions
From the Paper "Data warehouses have progressed from being repositories of data, used only by accounting and finance, to becoming an indispensable part of all departments' analytics and reporting requirements. Having transitioned from being the repository of data in the past to a platform for inter-departmental and inter-divisional analysis of results at the tactical and strategic level, data warehouses form the foundation of many organizations' business intelligence (BI), predictive analytics, customer segmentation, pricing modeling, and measures of operating effectiveness including manufacturing key performance indicators (KPIs). Corresponding to the exponential demands on data warehouses across organizations has been the demand for real-time data warehousing over batch-oriented processes. Getting data in real-time is also exponentially more costly on the one hand yet critical for manufacturing companies competing globally. "
Abstract The paper explains that datawarehousing is a method of bringing together all of a company's data from various computer systems, including those relating to customers, employees, vendors, products, inventory and financials. The data warehouse connects different databases together in order to offer a more comprehensive data set for making decisions. The paper considers how different ways of shaping such warehouses have been developed and how certain organizations have used them to gain control over data and over decision making. The paper concludes that evidence shows how, for organizations that can develop a strong system, datawarehousing is worth the cost.
Outline:
Abstract
Introduction
DataWarehousing Development of DataWarehousing Examples of DataWarehousing Conclusion
From the Paper "Databases have traditionally been used to track individual records, but today's computers can handle data of a much different type that is not easily converted into traditional relational database formats. In many operational information systems, the data represent a structured collection. One record exists for each item and each has the same set of attributes. Information systems also have validation and referential integrity requirements. There should be no duplications, and multiple references for the same classification of data should have the same characteristics (for example, the same address for multiple contacts at a single company in a customer database)."
Tags: database, records, classification, information, computer, system
Abstract The paper explains that data mining is a process whereby enterprises or organizations in any industry approach their respective data and databases in a more constructive and targeted manner to produce actionable business strategies. As some researchers have observed, data mining and datawarehousing are becoming more prevalent because of the large quantities of data stored in various systems and the number of business decisions made based on the data.
From the Paper "Thus, data mining and data mining techniques have risen to prominence with the elevation in importance of databases and, more recently, the development of data warehouses that have changed the complexion of industry in all sectors. Data mining and data warehousing solutions have been especially important in customer relations management (CRM) and in the healthcare industries for example."
Abstract This paper discusses three articles on data collection and analysis tools and their applications. This includes data mining, datawarehousing and software packages used in the collection. This paper also analyzes the needs of the business upon which the correct data collection and analysis tools are selected.
From the Paper "Business today has more and more need for external consultants to use data collection and analysis tools in order to make assessment of business operations and processes. Many of the methods used today are computer-based, including software that does much of the job but still requires an able human operator to make decisions and input the correct information. Various analysts have made assessments of these methods to see how they are used and how effective they may be. Such tools are also used for analyzing performance in education, for assessing public programs, and for other tasks requiring a decision as to the value of a program or process. Bielski (2001) discusses the use of CRM, or Customer Resource Management system, which is used to track customer purchases while providing access to customer information using the computer. "
