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Documents related to » epicor data general


Four Critical Success Factors to Cleansing Data
Four Critical Success Factors to Cleansing Data. Find Guides, Case Studies, and Other Resources Linked to Four Critical Success Factors to Cleansing Data. Quality data in the supply chain is essential in when information is automated and shared with internal and external customers. Dirty data is a huge impediment to businesses. In this article, learn about the four critical success factors to clean data: 1- scope, 2- team, 3- process, and 4- technology.

EPICOR DATA GENERAL: Four Critical Success Factors to Cleansing Data Four Critical Success Factors to Cleansing Data Source: PM ATLAS Business Group, LLC Document Type: White Paper Description: Quality data in the supply chain is essential in when information is automated and shared with internal and external customers. Dirty data is a huge impediment to businesses. In this article, learn about the four critical success factors to clean data: 1- scope, 2- team, 3- process, and 4- technology. Four Critical Success Factors to
1/14/2006 9:29:00 AM

Epicor to Acquire Spectrum HR » The TEC Blog
Epicor to Acquire Spectrum HR » The TEC Blog TEC Blog     TEC Home     About TEC     Contact Us     About the Bloggers     Follow TEC on Twitter    RSS   Discussing Enterprise Software and Selection --> Fast, Accurate Software Evaluations TEC helps enterprises evaluate and select software solutions that meet their exacting needs by empowering purchasers with the tools, research, and expertise to make an ideal decision. Your software selection starts here. Learn more about TEC s software

EPICOR DATA GENERAL: acquistion, Epicor, hr, human resource management, Spectrum Human Resource Systems Corporation, TEC, Technology Evaluation, Technology Evaluation Centers, Technology Evaluation Centers Inc., blog, analyst, enterprise software, decision support.
14-12-2010

Developing a Universal Approach to Cleansing Customer and Product Data
Developing a Universal Approach to Cleansing Customer and Product Data. Find Free Proposal and Other Solutions to Define Your Acquisition In Relation To Cleansing Customer and Product Data. Data quality has always been an important issue for companies, and today it’s even more so. But are you up-to-date on current industry problems concerning data quality? Do you know how to address quality problems with customer, product, and other types of corporate data? Discover how data cleansing tools help improve data constancy and accuracy, and find out why you need an enterprise-wide approach to data management.

EPICOR DATA GENERAL: Developing a Universal Approach to Cleansing Customer and Product Data Developing a Universal Approach to Cleansing Customer and Product Data Source: SAP Document Type: White Paper Description: Data quality has always been an important issue for companies, and today it’s even more so. But are you up-to-date on current industry problems concerning data quality? Do you know how to address quality problems with customer, product, and other types of corporate data? Discover how data cleansing tools help
6/1/2009 5:10:00 PM

Scalable Data Quality: A Seven-step Plan for Any Size Organization
Scalable Data Quality: a Seven-step Plan for Any Size Organization. Read IT Reports In Relation To Data Quality. Every record that fails to meet standards of quality can lead to lost revenue or unnecessary costs. A well-executed data quality initiative isn’t difficult, but it is crucial to getting maximum value out of your data. In small companies, for which every sales lead, order, or potential customer is valuable, poor data quality isn’t an option—implementing a thorough data quality solution is key to your success. Find out how.

EPICOR DATA GENERAL: Scalable Data Quality: A Seven-step Plan for Any Size Organization Scalable Data Quality: A Seven-step Plan for Any Size Organization Source: Melissa Data Document Type: White Paper Description: Every record that fails to meet standards of quality can lead to lost revenue or unnecessary costs. A well-executed data quality initiative isn’t difficult, but it is crucial to getting maximum value out of your data. In small companies, for which every sales lead, order, or potential customer is valuable, poor
9/9/2009 2:36:00 PM

The Data Warehouse RFP
If you don’t have a data warehouse, you’re probably considering drafting a request for proposal (RFP) to screen vendors and ensure that your receive satisfactory information about the features of various hardware and software and their price. Writing an effective RFP that is well structured and includes different metrics will ensure that you receive the information you need and will make you look good.

EPICOR DATA GENERAL: The Data Warehouse RFP The Data Warehouse RFP Source: Baseline Consulting Document Type: White Paper Description: If you don’t have a data warehouse, you’re probably considering drafting a request for proposal (RFP) to screen vendors and ensure that your receive satisfactory information about the features of various hardware and software and their price. Writing an effective RFP that is well structured and includes different metrics will ensure that you receive the information you need and will make
12/12/2005 5:10:00 PM

Data Quality Strategy: A Step-by-Step Approach
To realize the benefits of their investments in enterprise computing systems, organizations must have a detailed understanding of the quality of their data—how to clean it and how to keep it clean. Those organizations that approach this issue strategically will be successful. But what goes into a data quality strategy? This paper from Business Objects, an SAP company, explores the strategy in the context of data quality.

EPICOR DATA GENERAL: Data Quality Strategy: A Step-by-Step Approach Data Quality Strategy: A Step-by-Step Approach Source: SAP Document Type: White Paper Description: To realize the benefits of their investments in enterprise computing systems, organizations must have a detailed understanding of the quality of their data—how to clean it and how to keep it clean. Those organizations that approach this issue strategically will be successful. But what goes into a data quality strategy? This paper from Business Objects, an SAP
3/16/2011 2:03:00 PM

Microsoft says OLE for Data Mining: Is it Bull?
Microsoft released a new version of OLE DB (Object Linking and Embedding Database, based on Microsoft’s Component Object Model or COM) which supports a proprietary data mining specification. It is purported to extend the Structured Query Language (SQL) to allow easier and faster incorporation of data mining queries into existing data warehouse solutions.

EPICOR DATA GENERAL: Microsoft says OLE for Data Mining: Is it Bull? Microsoft says OLE for Data Mining: Is it Bull? M. Reed - March 28, 2000 Read Comments Event Summary REDMOND, Wash., March 7 /PRNewswire/ -- Microsoft Corp. (Nasdaq: MSFT) announced the beta release of the OLE DB for Data Mining specification, a protocol based on the SQL language, that provides software vendors and application developers with an open interface to more efficiently integrate data mining tools and capabilities into line-of-business and
3/28/2000

Data Quality Trends and Adoptions


EPICOR DATA GENERAL: Data Quality Trends and Adoptions Data Quality Trends and Adoptions In today s highly competitive and volatile business climate, data quality (DQ) software is no longer perceived as a nice-to-have. As business pressures increase, more and more midsized and large organizations are looking to DQ solutions to improve operational efficiencies reduce costs and risks improve customer experience and loyalty increase revenue provide data transparency and audit ability achieve greater regulatory compliance In
8/23/2011 11:02:00 AM

Epicor Reaches Better Vista From This Vantage Point
By harnessing Microsoft .NET possibly more keenly than its creator and by figuring out its bread-winning product groups, amid difficult market conditions notwithstanding, Epicor might be showing us that ‘a clearer vision' can keep it in the mid-market leadership race amongst a plethora of formidable opponents. This note’s spotlight is on Epicor’s recently unveiled manufacturing strategy.

EPICOR DATA GENERAL: Epicor Reaches Better Vista From This Vantage Point Epicor Reaches Better Vista From This Vantage Point P.J. Jakovljevic - June 12, 2003 Read Comments Event Summary A true mid-market incumbent vendor that has not had much good news for last several years following up on its progenitors merger and subsequent name change in 1999, seems to finally have been disseminating upbeat news both in terms of its financial performance and of its strategy clarity. Namely, on April 23, Epicor Software Corporation
6/12/2003

Six Misconceptions about Data Migration
A truly successful data migration project involves not only an understanding of how to migrate the data from a technical standpoint, but an understanding of how that data will be used and its importance to the operation of the enterprise.

EPICOR DATA GENERAL: Six Misconceptions about Data Migration Six Misconceptions about Data Migration Norma L. Davis - June 23, 2008 Read Comments Originally published February 4th, 2008 As organizations embark on a system implementation, one of the most important and riskiest activities is data migration—the movement of data from the old legacy system to the new system. While the selection, implementation, and operation of an enterprise application for enterprise resource planning (ERP), enterprise asset management (EAM),
6/23/2008

Spend Data Warehouse “On Steroids”
It’s only lately that people have been questioning the value of information they’re able to garner from within “spend data” warehouses. Why can t we leverage traditional tools to give the sourcing and purchasing community what they want? To understand the limitations of traditional data-cleansing technology, and why spend data necessitates special algorithms, we need to start with the basics.

EPICOR DATA GENERAL: Spend Data Warehouse “On Steroids” Spend Data Warehouse “On Steroids” Source: Zycus Document Type: White Paper Description: It’s only lately that people have been questioning the value of information they’re able to garner from within “spend data” warehouses. Why can t we leverage traditional tools to give the sourcing and purchasing community what they want? To understand the limitations of traditional data-cleansing technology, and why spend data necessitates special algorithms, we need
4/5/2007 1:58:00 PM


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