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PLM Evaluation Center
Nov 25, 2009
Today's usage of Decision Support Systems (DSS), combined with vetted PLM knowledge bases, allows organizations to save time and money, achieving better and more reliable/fully-documented decisions, a quantum improvement over the widely-used subjective process of selecting complex enterprise software...
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| 2. |
U.S. Crypto Laws Relaxed, but Not Enough to Enable Commerce ( Pages)
by L. Taylor
Feb 9, 2000
Abstract : The revisions the Department of Commerce has announced for U.S. export controls on encryption products will not be enough for U.S. crypto products to gain International acceptance. The U.S. crypto market will fail to reach its revenue potential, and overseas companies and International crypto markets will continue to bask lavishly from bungled U.S. export policy.
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A Definition of Data Warehousing ( Pages)
by M. Reed
Aug 18, 2002
Abstract : 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 describes the process of defining, populating, and using a data warehouse. Creating, populating, and querying a data warehouse typically carries an extremely high price tag, but the return on investment can be substantial. Over 95% of the Fortune 1000 have a data warehouse initiative underway in some form.
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Product Lifecycle Management: Expediting Product Innovation ( Pages)
by R. Nagarajan
Aug 11, 2008
Abstract : The highly competitive product manufacturing market makes true product lifecycle management (PLM) inevitable. PLM helps companies map product requirements to features, obtain control over product data, preserve product knowledge assets, and enter into the new paradigm of modular product development.
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| 5. |
Product Lifecycle Management: Expediting Product Innovation (0 Pages)
by R. Nagarajan
Sep 2, 2009
Abstract : The highly competitive product manufacturing market makes true product lifecycle management (PLM) inevitable. PLM helps companies map product requirements to features, obtain control over product data, preserve product knowledge assets, and enter into the new paradigm of modular product development.
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| 6. |
A Definition of Data Warehousing (6 Pages)
by M. Reed
Aug 24, 2000
Abstract : 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 describes the process of defining, populating, and using a data warehouse. Creating, populating, and querying a data warehouse typically carries an extremely high price tag, but the return on investment can be substantial. Over 95% of the Fortune 1000 have a data warehouse initiative underway in some form.
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| 7. |
Product Lifecycle Management: Expediting Product Innovation ( Pages)
by R. Nagarajan
Oct 20, 2006
Abstract : The highly competitive product manufacturing market makes true product lifecycle management (PLM) inevitable. PLM helps companies map product requirements to features, obtain control over product data, preserve product knowledge assets, and enter into the new paradigm of modular product development.
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| 8. |
Distilling Data: The Importance of Data Quality in Business Intelligence (0 Pages)
by Anna Mallikarjunan
Jul 17, 2009
Abstract : As an enterprise’s data grows in volume and complexity, a comprehensive data quality strategy is imperative to providing a reliable business intelligence environment. This article looks at issues in data quality and how they can be addressed.
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| 9. |
Distilling Data: The Importance of Data Quality in Business Intelligence (0 Pages)
by Anna Mallikarjunan
Oct 20, 2008
Abstract : As an enterprise’s data grows in volume and complexity, a comprehensive data quality strategy is imperative to providing a reliable business intelligence environment. This article looks at issues in data quality and how they can be addressed.
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| 10. |
Data Quality: Cost or Profit? ( Pages)
by Kevin Ramesan
Mar 8, 2004
Abstract : Data quality has direct consequences on a company's bottom-line and its customer relationship management (CRM) strategy. Looking beyond general approaches and company policies that set expectations and establish data management procedures, we will explore applications and tools that help reduce the negative impact of poor data quality. Some CRM application providers like Interface Software have definitely taken data quality seriously and are contributing to solving some data quality issues.
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