Enterprise Modeling White Papers

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Ten Things to Avoid in a Data Model
sponsored by CA ERwin from CA Technologies
WHITE PAPER: 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.
Posted: 19 Oct 2010 | Published: 01 Aug 2010

CA ERwin from CA Technologies

Sybase PowerDesigner for information architecture
sponsored by Sybase, an SAP company
WHITE PAPER: This paper explains how modeling information architecture (IA) can help reduce the costs associated with data management. Read this now and learn about the benefits of implementing IA and how Sybase's option offers modeling support for database design and enterprise architecture.  
Posted: 16 Apr 2012 | Published: 16 Apr 2012

Sybase, an SAP company

Choosing visual properties for successful visualizations
sponsored by IBM
WHITE PAPER: In the following article, IBM experts address a key aspect in the design process.
Posted: 09 Oct 2013 | Published: 09 Oct 2013

IBM

Fast-Tracking Data Warehousing & Business Intelligence Projects via Intelligent Data Modeling
sponsored by Embarcadero Technologies, Inc.
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.
Posted: 15 Jun 2011 | Published: 01 Jan 2010

Embarcadero Technologies, Inc.

Rapid-deployment Solution for Mobile Analytics Visualization
sponsored by Hewlett-Packard Enterprise
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.
Posted: 24 Feb 2014 | Published: 31 Oct 2013

Hewlett-Packard Enterprise

Business Oriented Information Architecture
sponsored by Sybase, an SAP company
WHITE PAPER: This paper outlines an information architecture (IA) and why it is useful in determining everything from the robustness of IT projects to testing business process scenarios to see if they satisfy the wide-ranging needs of your business. Read this now to learn more about IA, including its basic design, how to start your program and more.
Posted: 17 Apr 2012 | Published: 16 Apr 2012

Sybase, an SAP company

IBM Information Server FastTrack
sponsored by IBM
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.
Posted: 13 Aug 2008 | Published: 13 Aug 2008

IBM

Harvard Business Review's Guide to Visualizing Data
sponsored by SAS
WHITE PAPER: The following Harvard Business Review report explores the current state of data visualisation. Hear from many leading authors on how to leverage data visualisation, and the right times to use it.
Posted: 24 Apr 2014 | Published: 24 Apr 2014

SAS

Fast-Tracking Data Warehousing & BI Projects via Intelligent Data Modeling
sponsored by Embarcadero Technologies, Inc.
WHITE PAPER: At the core of any BI should be the ability to align business needs with the data infrastructure supporting them. This is almost impossible to do without a data model. Yet many BI implementers do not understand the need for these design components. This paper will examine the major benefits that data models have on BI environments.
Posted: 11 Feb 2010 | Published: 11 Feb 2010

Embarcadero Technologies, Inc.

Top 10 Data Mining Mistakes
sponsored by SAS
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.
Posted: 07 Apr 2010 | Published: 07 Apr 2010

SAS