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Read this paper to learn all the factors you need to consider when choosing a data modeling tool. You will learn about the different model types and how each tool measures up to the demanding needs of your company today and in the future. This paper will lay out all the information you need to make a clear decision on data modeling today.
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This white paper discusses Oracle Business Intelligence Standard Edition One, a powerful, integrated and comprehensive business intelligence system. Learn how to provide business insight, value, and ease of use for both, end users and administrators.
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This white paper describes how IBM's Information Server FastTrack accelerates the translation of business requirements into data integration projects. Data integration projects require collaboration across analysts, data modelers and developers.
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Whether or not analytics should become an integral part of an organization’s planning and decision-making seems to be beyond question However, at what level, for what purpose and how to go about deploying analytics are questions that each organization needs to answer for itself. These questions are the focus of this paper.
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Enterprise data is largely falling short of a standard that makes it possible to utilize in how business will be conducted in the next decade. Most enterprise data is “adequate” for basic operational needs today. Download this paper to learn how to get the most out of your enterprise data.
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In this white paper, you will learn intelligent data modeling practices to design and deliver superior business intelligence (BI) faster, the characteristics and benefits of intelligent data modeling and how to promote the use of data models to fast-track data warehouse and BI projects.
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The construction of a data model is one of the more difficult tasks of software engineering and is often pivotal to the success or failure of a project. Many factors determine the effectiveness of a data model. In this white paper, industry expert Michael Blaha covers the Top 10 pitfalls to avoid — from both the strategy and detail perspective.
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In the following paper, we briefly describe, and illustrate from examples, what we believe are the “Top 10” mistakes of data mining, in terms of frequency and seriousness. Most are basic, though a few are subtle. All have, when undetected, left analysts worse off than if they’d never looked at their data.
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The following white paper explores the top 7 considerations for implementing a data visualization solution in your enterprise. Learn how data visualization can give way to far deeper insights, better decision making, and much more.
