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Documents related to » plm technical data specialist


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.

PLM TECHNICAL DATA SPECIALIST:   Product Lifecycle Management (PLM),   Project and Process Management,   Supply Chain Management (SCM) Related Industries:   Information Source: PM ATLAS Business Group, LLC Learn more about PM ATLAS Business Group, LLC Readers who downloaded this white paper also read these popular documents! Best Practices for ERP Implementation Sales Process Map The Importance of Data Representation: Best Practices in Creating a Usable Report 3 Key Areas to Reduce Costs with Lean Techniques Talent Management for
1/14/2006 9:29:00 AM

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.

PLM TECHNICAL DATA SPECIALIST: 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

iWeb Agilewiz BPMS - iWeb Technical Framework
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PLM TECHNICAL DATA SPECIALIST: iWeb Agilewiz BPMS - iWeb Technical Framework iWeb Agilewiz BPMS - iWeb Technical Framework Source: iWeb Technology Solutions Document Type: Brochure Description: ... iWeb Agilewiz BPMS - iWeb Technical Framework style= border-width:0px; />   comments powered by Disqus Source: iWeb Technology Solutions Learn more about iWeb Technology Solutions
7/10/2013 5:00:00 PM

iWeb Agilewiz BPMS - iWeb Technical Framework
...

PLM TECHNICAL DATA SPECIALIST: iWeb Agilewiz BPMS - iWeb Technical Framework iWeb Agilewiz BPMS - iWeb Technical Framework Source: iWeb Technology Solutions Document Type: Brochure Description: ... iWeb Agilewiz BPMS - iWeb Technical Framework style= border-width:0px; />   comments powered by Disqus Source: iWeb Technology Solutions Learn more about iWeb Technology Solutions
7/10/2013 5:00:00 PM

Curing the Data Integration Migraine
The potential value of centralized data integration is enormous. Once implemented, integration systems promise to deliver more accurate and higher quality data. However, for those who venture into the world of implementation, the promise rarely matches the reality. Avoiding the “data integration migraine” requires careful planning to reduce the risks associated with data relationship, transformation, and map discovery.

PLM TECHNICAL DATA SPECIALIST:
10/27/2006 4:30:00 PM

Customer Data Integration: A Primer
Customer data integration (CDI) involves consolidation of customer information for a centralized view of the customer experience. Implementing CDI within a customer relationship management initiative can help provide organizations with a successful framework to manage data on a continuous basis.

PLM TECHNICAL DATA SPECIALIST: Master Data Management, MDM, Customer Data Integration, CDI, Data Integration, CRM, BI, business intelligence, data standardization, data consolidation, Customer Relationship Management.
9/11/2009

A CRM System Needs A Data Strategy
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.

PLM TECHNICAL DATA SPECIALIST: A CRM System Needs A Data Strategy A CRM System Needs A Data Strategy David McNamara - July 3, 2003 Read Comments Introduction An underutilized customer relationship management (CRM) system - or one that cannot match its owner s expectations - will reflect poorly on both the vendor who sold it and the IT manager who authorized the purchase and installed it. Both, however, can help successfully manage such expectations (and add value to their respective roles) by wisely counseling about the strategic
7/3/2003

Data Center Projects: System Planning
System planning is the Achilles’ heel of a data center physical infrastructure project. Planning mistakes can propagate through later deployment phases, resulting in delays, cost overruns, wasted time, and a compromised system. These troubles can be eliminated by viewing system planning as a data flow model, with sequenced tasks that progressively transform and refine data from initial concept to final design. Learn more.

PLM TECHNICAL DATA SPECIALIST:
12/10/2008 9:35:00 AM

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.

PLM TECHNICAL DATA SPECIALIST: big data, analytics, Unisphere Research, big data survey, data manager survey, big data challenges, big data initiatives.
7/5/2013 2:25:00 PM

Data Mart Consolidation and Business Intelligence Standardization
Improve data mart and business intelligence (BI) consolidation with Teradata and SAP BusinessObjects platforms.Download free white papers! Making information broadly and easily available to more users throughout an organization—and beyond the organization to customers, partners, and stakeholders—has never been more imperative. More enterprises are coming to understand the value of placing consistent, integrated data into the hands of everyone who needs it. Learn how a data mart consolidation program can help you improve decision making while cutting costs.

PLM TECHNICAL DATA SPECIALIST: Now Economic Benefits of PLM-enabled Collaboration Data Mart Consolidation and Business Intelligence Standardization If you receive errors when attempting to view this white paper, please install the latest version of Adobe Reader. SAP BusinessObjects Edge Business Intelligence (BI) is a powerful business intelligence choice for midsize companies that want to improve business processes, discover new opportunities, and gain a competitive advantage. This comprehensive, versatile midmarket software
3/2/2010 10:32:00 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.

PLM TECHNICAL DATA SPECIALIST:
4/5/2007 1:58:00 PM


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