WEBCAST:
Learn how to easily gain profitable data mining results in less time and improve accuracy and usefulness of analytical models during this Webinar. You’ll find out how to deliver precise and confident results, manage and improve model performance, and derive and share insights to improve the quality and precision of your decisions.
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.
EBOOK:
Master data management (MDM) can help companies make better use of their corporate data by ensuring that iformation is accurate and consistent across all systems. In this e-book, gain best practices and expert tips and advice on how to successfully structure and manage MDM programs.
TRIAL SOFTWARE:
IBM InfoSphere Data Architect is a collaborative data design solution to help you discover, model, relate, and standardize diverse and distributed data assets. It supports dimensional modeling. This page contains more information about the product and access to a 30-day trial.
PRODUCT OVERVIEW:
Complete capabilities in one product, on a single architecture. See key features for both business and IT users, including Framework Manager, the Administration Console, and integration with Microsoft Excel.
WHITE PAPER:
Often times Business Intelligence (BI) projects miss the mark with their business users because the proper documenting of required data and related business rules is not executed. This paper looks at fast-tracking data warehousing and BI projects using data modeling.
WHITE PAPER:
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.
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.
