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Core PLM Product Data and Recipe Management--Process RFP Templates
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Six Steps to Manage Data Quality with SQL Server Integration Services
Six Steps to Manage Data Quality with SQL Server Integration Services. Read IT Reports Associated with Data quality. Without data that is reliable, accurate, and updated, organizations can’t confidently distribute that data across the enterprise, leading to bad business decisions. Faulty data also hinders the successful integration of data from a variety of data sources. But with a sound data quality methodology in place, you can integrate data while improving its quality and facilitate a master data management application—at low cost.
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Oracle Database 11g for Data Warehousing and Business Intelligence
Oracle Database 11g for Data Warehousing and Business Intelligence. Find RFP Templates and Other Solutions to Define Your Project In Relation To Oracle Database, Data Warehousing and Business Intelligence. Oracle Database 11g is a database platform for data warehousing and business intelligence (BI) that includes integrated analytics, and embedded integration and data-quality. Get an overview of Oracle Database 11g’s capabilities for data warehousing, and learn how Oracle-based BI and data warehouse systems can integrate information, perform fast queries, scale to very large data volumes, and analyze any data.
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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.
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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.
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1/14/2006 9:29:00 AM
Overall Approach to Data Quality ROI
Organizations are beginning to wake up to the fact that the data they collect and manage should be viewed as a corporate asset. Data is the one thing that separates you from your competitors—and the quality of your data can be your competitive advantage or disadvantage. Discover six key steps you can take and put into effect to help you realize a tangible return on investment (ROI) on your data quality initiative.
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Data-driven Design
Creating and maintaining a successful digital experience that drives business results requires the right research insight, design, technology, and ongoing optimization. Forrester conducted an online survey of 209 digital experience professionals in the US to evaluate current practices around Web site monitoring and digital experiences. Read about the adoption, benefits, and challenges of current data-driven design processes.
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Logs: Data Warehouse Style
Once a revolutionary concept, data warehouses are now the status quo—enabling IT professionals to manage and report on data originating from diverse sources. But where does log data fit in? Historically, log data was reported on through slow legacy applications. But with today’s log data warehouse solutions, data is centralized, allowing users to analyze and report on it with unparalleled speed and efficiency.
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2/8/2008 1:14:00 PM
Data Security, Governance, and Privacy
To meet regulatory requirements such as the US Sarbanes-Oxley Act (SOX), IT managers are now receiving the board-level visibility and budgets required to improve enterprise data security. For example, companies need secure and verifiable audit trails that track the activities of database administrators (DBAs) and other privileged insiders. But have you considered the seven essential elements of database security?
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3/30/2007 4:54:00 PM
New Data Protection Strategies
One of the greatest challenges facing organizations is the protection of corporate data. The issues complicating data protection are compounded by increased demand for data capacity and higher service levels. Often these demands are coupled with regulatory requirements and a shifting business environment. Learn about data protection strategies that can help organizations meet these demands while maintaining flat budgets.
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4/29/2010 4:10:00 PM
TDWI Vegas: How to Build an Enterprise Data Strategy » The TEC Blog
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04-02-2011
Freeing Six Sigma from the “Data Shuffle”
Paying skilled professionals to massage, scrub, and manipulate data is a huge waste of valuable resources. And while having clean data is essential to driving Six Sigma projects, the act of getting that data adds absolutely no business value. That’s why organizations that focus first on making accurate, actionable data available in real time have more effective Six Sigma programs.
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