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Distilling Data: The Importance of Data Quality in Business Intelligence
As an enterprise’s data grows in volume and complexity, a comprehensive data quality strategy is imperative to providing a reliable business intelligence

data mining etl  that are not obvious. Data mining is the process of discovering information from enterprise data that is otherwise hidden. For instance, an online bookstore suggests additional books based on what a user adds to his or her cart by examining evidence from other comparable buyers. This is done through the use of association rules applied to historical sales data. Although the primary purpose of mining data is to gain business insight, it can be applied to discover anomalies in data. Consider a Web-based Read More...
Business Intelligence in SAP Environments
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

data mining etl  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. Read More...
Best Practices for a Data Warehouse on Oracle Database 11g
Companies are recognizing the value of an enterprise data warehouse (EDW) that provides a single 360-degree view of the business. But to ensure that your EDW

data mining etl  Warehouse Web Site , Data Mining , Data Mart , Data Warehouse Architecture , Data Warehouse Concepts , Data Warehouse Tutorial , Data Warehouse Definition , OLAP , Business Intelligence , Huge Data Warehouse , Data Warehouse Appliance . NOTE: The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in Read More...
The Necessity of Data Warehousing
An explanation of the origins of data warehousing and why it is a crucial technology that allows businesses to gain competitive advantage. Issues regarding

data mining etl  analysis techniques such as data mining (statistical analysis to discover trends in the data), data visualization (graphical display of query results), or multi-dimensional analysis (the so called slice and dice ). Will the architecture be two-tiered or three-tiered? Three-tiered architectures offload some of the processing to an application server which sits between the database server and the end-user. Will the tool employ a push or a pull technology? ( Push technology publishes the queries to Read More...
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

data mining etl  a result set back. Data Mining Tools: Tools that automatically search for patterns in data. These tools are usually driven by complex statistical formulas. The easiest way to distinguish data mining from the various forms of OLAP is that OLAP can only answer questions you know to ask, data mining answers questions you didn''t necessarily know to ask. Data Visualization Tools: Tools that show graphical representations of data, including complex three-dimensional data pictures. The theory is that the user Read More...
The Use of Ceiling-Ducted Air Containment in Data Centers
Ducting hot IT-equipment exhaust to a drop ceiling can be an effective air management strategy, improving the reliability and energy efficiency of a data center

data mining etl  Ceiling-Ducted Air Containment in Data Centers Ducting hot IT-equipment exhaust to a drop ceiling can be an effective air management strategy, improving the reliability and energy efficiency of a data center. Typical approaches include ducting either individual racks or entire hot aisles and may be passive (ducting only) or active (include fans). This paper examines available ducting options and explains how such systems should be deployed and operated. Practical cooling limits are established and Read More...
Big Data Comes of Age: Shifting to a Real-time Data Platform
New data sources are fueling innovation while stretching the limitations of traditional data management strategies and structures. Data warehouses are giving

data mining etl  Data Comes of Age: Shifting to a Real-time Data Platform New data sources are fueling innovation while stretching the limitations of traditional data management strategies and structures. Data warehouses are giving way to purpose-built platforms more capable of meeting the real-time needs of more demanding end users and the opportunities presented by big data. Read this white paper to learn more about the significant strategy shifts underway to transform traditional data ecosystems by creating a unified Read More...
Data Quality: A Survival Guide for Marketing
Even with the finest marketing organizations, the success of marketing comes down to the data. Ensuring data quality can be a significant challenge

data mining etl  to the data. Ensuring data quality can be a significant challenge, particularly when you have thousands or even millions of prospect records in your CRM system and you are trying to target the right prospect. Data quality, data integration, and other functions of enterprise information management (EIM) are crucial to this endeavor. Read more. Read More...
Eliminating Mining Industry Profitability Killers through a Common Decision Environment
Mining corporations and their operating units are under constant pressure to improve financial performance. Efforts to become financially healthy are hampered

data mining etl  Mining Industry Profitability Killers through a Common Decision Environment Mining corporations and their operating units are under constant pressure to improve financial performance. Efforts to become financially healthy are hampered by the operational profitability killers embedded in traditional processes. Find out how technology-supported collaborative decision making drives key efficiencies at all stages of the asset lifecycle, from planning and construction through production and into Read More...
Enterprise Data Management: Migration without Migraines
Moving an organization’s critical data from a legacy system promises numerous benefits, but only if the migration is handled correctly. In practice, it takes an

data mining etl  Data Management: Migration without Migraines Moving an organization’s critical data from a legacy system promises numerous benefits, but only if the migration is handled correctly. In practice, it takes an understanding of the entire ERP data lifecycle combined with industry-specific experience, knowledge, and skills to drive the process through the required steps accurately, efficiently, and in the right order. Read this white paper to learn more. Read More...
Data Quality Strategy: A Step-by-Step Approach
To realize the benefits of their investments in enterprise computing systems, organizations must have a detailed understanding of the quality of their data—how

data mining etl  what goes into a data quality strategy? This paper from Business Objects, an SAP company, explores the strategy in the context of data quality. Read More...
Data Migration Best Practices
Large-scale data migrations can be challenging, but with the appropriate planning—and through careful execution of that plan—organizations can greatly reduce

data mining etl  Migration Best Practices Large-scale data migrations can be challenging, but with the appropriate planning—and through careful execution of that plan—organizations can greatly reduce the risks and costs associated with these projects. This paper offers a handy checklist of issues to consider before, during, and after migration. Read More...
Tailoring SAP Data Management for Companies of All Sizes
The need for accurate data management such as upload or download of data between a company’s data sources and SAP systems is more critical than ever. Users are

data mining etl  SAP Data Management for Companies of All Sizes The need for accurate data management such as upload or download of data between a company’s data sources and SAP systems is more critical than ever. Users are relying on manual operations, which are inherently error-prone, and time- and resource-intensive. Today''s environment requires enterprise-class automation to overcome these challenges of data management. Learn about one solution that can help improve SAP data management. Read More...

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