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A Definition of Data Warehousing
There is a great deal of confusion over the meaning of data warehousing. Simply defined, a data warehouse is a place for data, whereas data warehousing describes the process of defining, populating, and using a data warehouse. Creating, populating, and querying a data warehouse typically carries an extremely high price tag, but the return on investment can be substantial. Over 95% of the Fortune 1000 have a data warehouse initiative underway in some form.
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Who Needs Warehousing Management and How Much Thereof?
The warehouse is no longer merely a static storage facility. It now has to use virtually real-time data to closely match supply to demand, eliminate the need to hold excess inventory, and increase the flow of goods throughout the supply chain.
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Oracle Database 11g for Data Warehousing and Business Intelligence. Find RFP Templates and Other Solutions to Define Your Project In Relation To Oracle Database, Data Warehousing and Business Intelligence. Oracle Database 11g is a database platform for data warehousing and business intelligence (BI) that includes integrated analytics, and embedded integration and data-quality. Get an overview of Oracle Database 11g’s capabilities for data warehousing, and learn how Oracle-based BI and data warehouse systems can integrate information, perform fast queries, scale to very large data volumes, and analyze any data.
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Business Process Overview: Warehousing Solutions. Search for Templates and Other Software to Delineate Your Evaluation Concerning the Business Process and Warehousing Solutions Warehousing processes are critical for the success of a distribution business. These processes are highly visible to your customers, and have a direct impact on their opinion of your company. If you cannot get the goods to your customers in the most efficient way, you risk losing their business. Your warehousing and logistics operations must translate into reliability and service.
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11/16/2006 2:23:00 PM
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Increased global competition, pricing pressure, and the need to rationalize resources have made business and operational measurement vital on all organizational levels. That’s why business intelligence and data warehousing tools are no longer only for the exclusive use of financial controllers. Instead, they are everyday tools across company divisions, giving relevant and efficient information overviews of all areas of responsibility.
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11/16/2006 2:21:00 PM
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Microsoft released a new version of OLE DB (Object Linking and Embedding Database, based on Microsoft’s Component Object Model or COM) which supports a proprietary data mining specification. It is purported to extend the Structured Query Language (SQL) to allow easier and faster incorporation of data mining queries into existing data warehouse solutions.
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As a consequence of the acquisition of Business Objects, SAP has shifted its SAP business warehouse (BW) strategy to a more open data warehousing approach and is now focusing on the former Business Objects portfolio. This guide is designed to help existing SAP BW customers to plan to move to the new business intelligence (BI) environment, and outlines most important architecture options for a data warehouse strategy.
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Metagenix Reverse Engineers Data Into Information
Metagenix’ MetaRecon reverse engineers metadata information by examining the raw data contained in the source(s) rather than depending on the data dictionaries of the existing legacy systems (which are often incorrect). Other unique Metagenix approaches include an
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Case Study: Baltika Breweries
Baltika Breweries was on the rise when it faced data warehousing challenges after merging with three other Russian breweries in 2006. Learn how implementing a new business intelligence (BI) infrastructure helped the company improve collaboration for its logistics department across the country, as well as successfully track key performance indicators (KPIs), which streamlined the entire company's productivity.
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