WHITE PAPER:
This white paper discusses: What is an industry model? What is the value of industry models? Considerations for building or buying data models IBM Industry Models—business and technical blueprints Reducing time to value with IBM Industry Models
WHITE PAPER:
By using the Oracle Exadata Database Machine as a data warehouse platform you have a balanced, high performance hardware configuration. This paper focuses on the other two corner stones, data modeling and data loading, providing a set of best practices and examples for deploying a data warehouse on the Oracle Exadata Database Machine.
WHITE PAPER:
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.
WHITE PAPER:
In this white paper, discover a rapid-deployment technology for mobile analytics visualization, so you can take full advantage of data visualization capabilities anywhere, at any time. Explore the benefits you'll get with rapid-deployment, and learn how easy it is to get a mobile analytics strategy up and running within two weeks.
WHITE PAPER:
Read this white paper and learn how the data warehouse, metadata and modeling environment will be transformed in the next few years — and what you need to do to leverage it for your business, the major components of DW 2.0 architectures, and key modeling and metadata management strategies for DW 2.0.
WHITE PAPER:
Read on to find details about SAPs BusinessObjects Predictive Analysis, including how the NBA used HANA to help cater to stat-hungry fans.
WHITE PAPER:
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.
WHITE PAPER:
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.
WHITE PAPER:
This paper analyzes the issues of conventional data warehouse design process and explains how this practice can be improved using a business-model-driven process in support of effective Business Intelligence.
