| 1. |
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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| 2. |
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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| 3. |
Syncsort Sigma Manages Database Aggregates ( Pages)
by M. Reed
Jul 25, 2000
Abstract : Syncsort, a software vendor with years of experience in the sorting of data, has released a new product called Sigma™, which is designed to produce aggregated data within data warehouses to improve response time and reduce database load. The importance of aggregation in a data warehouse cannot be underestimated.
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| 4. |
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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| 5. |
The Necessity of Data Warehousing ( Pages)
by M. Reed
Aug 2, 2000
Abstract : An explanation of the origins of data warehousing and why it is a crucial technology that allows businesses to gain competitive advantage. Issues regarding technology selection and access to historical 'legacy' data are also discussed.
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| 6. |
The Necessity of Data Warehousing (5 Pages)
by M. Reed
Sep 1, 1999
Abstract : An explanation of the origins of data warehousing and why it is a crucial technology that allows businesses to gain competitive advantage. Issues regarding technology selection and access to historical 'legacy' data are also discussed.
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| 7. |
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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| 8. |
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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| 9. |
Metadata Standards in the Marketplace – Why Do I Care? (And Where Does Godzilla Fit In?) ( Pages)
by M. Reed
May 16, 2000
Abstract : Metadata (“data about data”) is essential for data warehousing. Metadata standards allow different products to interact. Without standards, different vendors’ tools cannot work together seamlessly and the customer’s warehousing effort is greatly complicated.
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