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Documents related to » jde data model


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.

JDE DATA MODEL: 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

About Big Data
TEC analyst Jorge Garcia discusses the key issues surrounding big data, the different ways to manage it, and the major vendors offering big data solutions. There may not be a consensus with respect to just how big

JDE DATA MODEL: About Big Data About Big Data Jorge García - November 18, 2011 Read Comments There is no general consensus with respect to how big big data is—some companies deal with data volumes in the order of terabytes or even petabytes—but not many people will disagree that managing these huge amounts of data represents a challenge. It’s fair to say that we’re dealing with big data when traditional relational databases and systems are no longer sufficient. Things as simple as data storage and movement
11/18/2011 2:08:00 PM

Achieving a Successful Data Migration
Achieving a Successful Data Migration. Solutions and Other Software to Delineate Your System and for Achieving a Successful Data Migration. The data migration phase can consume up to 40 percent of the budget for an application implementation or upgrade. Without separate metrics for migration, data migration problems can lead an organization to judge the entire project a failure, with the conclusion that the new package or upgrade is faulty--when in fact, the problem lies in the data migration process.

JDE DATA MODEL: Achieving a Successful Data Migration Achieving a Successful Data Migration Source: Informatica Document Type: White Paper Description: The data migration phase can consume up to 40 percent of the budget for an application implementation or upgrade. Without separate metrics for migration, data migration problems can lead an organization to judge the entire project a failure, with the conclusion that the new package or upgrade is faulty--when in fact, the problem lies in the data migration process.
10/27/2006 4:30:00 PM

Energy Innovator Implements a New Business Model with mySAP
Energy Innovator Implements a New Business Model with mySAP.Secure Documents and Other Package to Use In Your Dynamic System for Energy Innovator Implements. Capstone Turbine Corporation, an energy innovator, needed to implement management, business model, and strategy changes. It also needed to improve product reliability, modularize configurations, and provide visibility. By using SAP products, such as SAP BI, SAP EP, and SAP Best Practices, it realized performance measures for key business activities, and improved sales and inventory management.

JDE DATA MODEL: Energy Innovator Implements a New Business Model with mySAP Energy Innovator Implements a New Business Model with mySAP Source: SAP Document Type: Case Study Description: Capstone Turbine Corporation, an energy innovator, needed to implement management, business model, and strategy changes. It also needed to improve product reliability, modularize configurations, and provide visibility. By using SAP products, such as SAP BI, SAP EP, and SAP Best Practices, it realized performance measures for key business
5/5/2006 10:25:00 AM

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.

JDE DATA MODEL: 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

Value-selling Maturity Model: Key to Sales in Tough Times
Showing value has always been key to greater sales and higher prices. However, the bar has been raised. It’s often no longer enough to claim value—now the customer has to see, believe, and care about the value. Sales organizations must get their eyes back on the value ball, with five value-selling maturity levels that tie in to sales effectiveness. Learn how to improve your revenues while reducing sales effort and costs.

JDE DATA MODEL:
6/24/2009 11:30:00 AM

Why Systems Fail - The Dead-end of Dirty Data
If your data does not reflect reality, the system can never be effective. In today’s world of collaboration, showing a trading partner dirty data is giving them the wrong message and tearing down the trust called for in a collaborating partnership.

JDE DATA MODEL: Why Systems Fail - The Dead-end of Dirty Data Why Systems Fail - The Dead-end of Dirty Data Olin Thompson - July 4, 2003 Read Comments Introduction If your data does not reflect reality, the system can never be effective. In today s world of collaboration, showing a trading partner dirty data is giving them the wrong message and tearing down the trust called for in a collaborating partnership. What is dirty data? When reality and the data in your system do not agree, you have dirty data. It may be as
7/4/2003

The New Virtual Data Centre
Old-style, one application per physical server data centers are not only nearing the end of their useful lives, but are also becoming barriers to a business’ future success. Virtualization has come to the foreground, yet it also creates headaches for data center and facilities managers. Read about aspects of creating a strategy for a flexible and effective data center aimed to carry your business forward.

JDE DATA MODEL: The New Virtual Data Centre The New Virtual Data Centre Source: Quocirca Ltd Document Type: White Paper Description: Old-style, one application per physical server data centers are not only nearing the end of their useful lives, but are also becoming barriers to a business’ future success. Virtualization has come to the foreground, yet it also creates headaches for data center and facilities managers. Read about aspects of creating a strategy for a flexible and effective data center aimed to carry your
2/3/2011 8:58:00 AM

Metagenix Reverse Engineers Data Into Information
Metagenix’ MetaRecon reverse engineers metadata information by examining the raw data contained in the source(s) rather than depending on the data dictionaries of the existing legacy systems (which are often incorrect). Other unique Metagenix approaches include an

JDE DATA MODEL: Metagenix Reverse Engineers Data Into Information Metagenix Reverse Engineers Data Into Information M. Reed - February 15, 2001 Read Comments M. Reed - February 15, 2001 Event Summary Metagenix, Inc. has designed its flagship product, MetaRecon to, as they put it, Decipher Your Data Genome . The product reverse engineers all of the metadata ( data about data ) from data sources and generates information that is very helpful to developers in designing specifications for a new data store, and assists
2/15/2001

Manufacturers and the SaaS Delivery Model
Find out about the advantages of SaaS inreport, Manufacturers and the SaaS Delivery Model.

JDE DATA MODEL: manufacturers saas delivery model, manufacturers, saas, delivery, model, saas delivery model, manufacturers delivery model, manufacturers saas model, manufacturers saas delivery..
12/14/2010 5:00:00 PM

2013 Big Data Opportunities Survey
While big companies such as Google, Facebook, eBay, and Yahoo! were the first to harness the analytic power of big data, organizations of all sizes and industry groups are now leveraging big data. A survey of 304 data managers and professionals was conducted by Unispere Research in April 2013 to assess the enterprise big data landscape, the types of big data initiatives being invested in by companies today, and big data challenges. Read this report for survey responses and a discussion of the results.

JDE DATA MODEL: 2013 Big Data Opportunities Survey 2013 Big Data Opportunities Survey Source: SAP Document Type: White Paper Description: While big companies such as Google, Facebook, eBay, and Yahoo! were the first to harness the analytic power of big data, organizations of all sizes and industry groups are now leveraging big data. A survey of 304 data managers and professionals was conducted by Unispere Research in April 2013 to assess the enterprise big data landscape, the types of big data initiatives being invested
7/5/2013 2:25:00 PM


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