Data Scrubbing

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Looking for content related to Data Scrubbing

Data Quality: Cost or Profit?

by Kevin Ramesan

... Data scrubbing is the process of fixing or eliminating individual pieces of data
that are incorrect, incomplete or duplicated before the data is passed to a ...

http:/.../Research/ResearchHighlights/Crm/2004/03/research_notes/MI_CR_KR_03_08_04_1.asp - 14k

Summary: 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 negat

A Definition of Data Warehousing

by M. Reed

... others to match the rule. This process is also referred to as "data scrubbing"
or "data quality assurance". It can be an extremely ...

http:/.../Research/ResearchHighlights/DataWarehousing/2002/08/research_notes/TU_DW_MFR_08_18_02_1.asp - 16k

Summary: 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 ext

N-Tier Demand Management

by Bill McBeath

... This data is factored into the forecasting scrubbing process to eliminate duplicate
demand (see sidebar "Rationalizing Demand Across Channels"). ...

http:/.../Research/ResearchHighlights/Scm/2004/12/research_notes/TN_SC_XBM_12_23_04_1.asp - 13k

Summary: The classic bull-whip effect means that the further a supplier is removed from the end consumer, the worse are the fluctuations in demand that they see. This has led many to recommend an n-tier approach to demand management, where everyone gets visibility to the end-customer demand at the same time. In p

SCM System/SCM Software Comparison and Evaluation

... support DDE DES data Data Integration Technologies Data Management Options Data
cleansing and normalization (scrubbing, mapping, and aliasing) Data cleansing ...

http://scm.technologyevaluation.com/supply-chain-optimization// - 71k - 2009-03-09

SCM System/SCM Software Comparison and Evaluation

... Data capture refers to capturing information contained in a bar code or RFID Data
cleansing and normalization (scrubbing, mapping, and aliasing) Data cleansing ...

http://scm.technologyevaluation.com/ - 101k - 2009-03-09

Compare CRM (Financial/Insurance Market) Software

... channels Java Manages incoming e-mails from customers with automatic guided-through
questionnaires and outbound e-mails combined with customer's data. ...

http://crm.technologyevaluation.com/financial-insurance/ - 101k - 2009-04-21

Summary: Compare CRM software: Discover the 10 key modules to base your selection on and tools you must use in order to save up to 85% of time and money you'd spend gathering information on your own : Insurance and Investment , Marketing Automation, Sales Force Automation (SFA), CRM Analytics, Call Center and Cu

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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.
2.	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.
3.	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.
4.	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.
5.	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.
6.	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.
7.	The Truth about Data Mining (0 Pages) by Anna Mallikarjunan Jun 19, 2009 Abstract : It is now imperative that businesses be prudent. With rising volumes of data, traditional analytical techniques may not be able to discover valuable data. Consequently, data mining technology becomes important. Here is a framework to help understand the data mining process.
8.	A CRM System Needs A Data Strategy (7 Pages) by David McNamara Jan 18, 2001 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.
9.	A CRM System Needs A Data Strategy (7 Pages) by David McNamara Jan 3, 2001 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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