| 1. |
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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| 2. |
Bolstering the Call Center with Service Resolution Management Processes (3 Pages)
by P.J. Jakovljevic
Sep 4, 2009
Abstract : Integrated customer relationship management and call center solutions (sometimes referred to as service resolution management) have, despite initial glitches, reportedly helped some service companies realize remarkable returns on investment in addition to improved customer satisfaction rates.
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| 3. |
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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| 4. |
Bolstering the Call Center with Service Resolution Management Processes ( Pages)
by P.J. Jakovljevic
Dec 14, 2007
Abstract : Integrated customer relationship management and call center solutions (sometimes referred to as service resolution management) have, despite initial glitches, reportedly helped some service companies realize remarkable returns on investment in addition to improved customer satisfaction rates.
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| 5. |
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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| 6. |
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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| 7. |
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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| 8. |
Candle Releases New Command Center App for IBM MQSI 2 ( Pages)
by M. Reed
Oct 11, 2000
Abstract : IBM has announced a four-year, $200 million investment to attempt to make it more cost effective and easier for companies to manage data on IBM S/390 enterprise servers. The proposed solution is a new Candle Corporation product with a GUI front-end that can track message flow, queue times, and other metrics. Is this yet another example of IBM leveraging technology through partnerships instead of always trying to roll their own, as Oracle has done?
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| 9. |
A CRM System Needs A Data Strategy ( Pages)
by David McNamara
Jul 3, 2003
Abstract : A customer relationship management (CRM) system is inherently valuable for supporting customer acquisition and retention by gathering data from each contact with customers and prospects. Collecting data, however, cannot be isolated from a strategy for actually using that data. Here is an overview of how to evolve the focus of a data strategy to specifically suit both the acquisition and retention phases.
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