Data mart

About: Data mart is a research topic. Over the lifetime, 559 publications have been published within this topic receiving 8550 citations.

Understanding and Implementing Successful Data Marts

Abstract: Data marts are beginning to emerge from the shadow of their all-encompassing, enterprise-scale cousins, data warehouses, as a more timely and cost-effective approach to organizing and accessing critical business data. Easier, quicker, and less expensive to design and implement, data marts can be designed to meet the specific needs ental concepts and guides you step-by-step through the entire data mart process, from the initial planning stages through implementation and maintenance. With an emphasis on "success," the author draws from his many years of real-world experience to reveal potential pitfalls and offer helpful tips that can make the difference between success and failure in the implementation of a working data mart. You will find thorough coverage of available technologies, fundamental design, specific methodologies, and industry trends. The two major types of data marts are covered: the subset data mart and the increasingly popular incremental data mart, with a special emphasis placed on integration issues.The book also details each step involved in the creation of a data mart: identifying business drivers, forming a team, surveying users, choosing among tools and design options, working with meta data, incorporating company culture into implementation strategy, and even training and support. In the process, you will learn about the different types of data mart designs and architectures, virtual data warehouses, data islands, decision dupport system tools, and much more. Acknowledging that plans don't always proceed smoothly, the author "reality checks"--highlighted sections that discuss what happens when data mart theory meets the reality of the workplace, where sponsors disappear, budgets shrink, and specifications change midstream. 0201183803B04062001

System and method for integrated data mart defining start schema model processing business contact data from multiple contact channels

Abstract: Disclosed is a data collection and analysis system that is capable of extracting data from various disparate sources, i.e., contact channels, storing the data and analyzing the data to show trends in the business operation. The data is stored in a data model that uses a star schema approach to providing a unified data source. Analyzed data can be made available to users on a nearly real time basis that allows the users to view trends in business operation and plan accordingly.

Quality in Data Warehousing

Abstract: Data warehousing is a new technology which provides a software infrastructure for decision support systems and OLAP applications. Data warehouse systems collect data from heterogeneous and distributed sources, transform and reconcile this data in order to aggregate it and customize it with respect to business and organizational criteria required by decision makers. High level aggregated data is organized by subjects and stored as a multidimensional structure into a data mart. Data quality is very important in database applications in general and very crucial in data warehousing in particular. Indeed, data warehouse systems provide aggregated data to decision makers whose actions and decisions should be very strategic to the enterprise. Providing dirty data, imprecise data or non coherent data may lead to the rejection of the decision support system or may result into non productive decisions. This chapter provides a general framework for data warehouse design based on quality.

System and method for fleet card management

Abstract: System and method for managing purchase data of a credit card account associated with a vehicle through the use of a management system. The management system downloads data regarding a vehicle from vehicle master system, and data regarding the U.S. Postal Service organizational hierarchy structure from financial data mart system. The management system receives transaction data from a credit card system, summarizes the data by vehicle, driver, product group and supplier at eight (8) summary levels of organizational structure, analyzes the transaction data for instances of possible fraudulent use of the credit card, and creates exception records to indicate possibility of fraud. On behalf of the credit card provider, the management system creates an invoice and a control report, and sends the invoice to the financial department for payment. The management system produces spreadsheets and reports used for quarterly or annual filing with States to recoup exempt tax not taken off at the pump. The management system organizes the transaction, summary, invoice and exception data into sorted listings for display over one or more intranet web pages. The web pages on which transactions are shown provide the capability for the user to reconcile the transactions. The sorted listings may indicate a probability of fraud.