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Overview: DataFlux offers complete, end-to-end data management solutions for organizations that want to realize more tangible, immediate value from their information assets.
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A Guide to the Value of Reliable Data in Insurance by DataFlux Corporation
November 02, 2009 - Only when data is trusted can it be used in confidence in all insurance operational and analytical process activities. This paper examines the impact of unreliable data on insurance companies. It then defines the requirements needed to guarantee data reliability in insurance and offers a practical approach to creating and governing that data.
A Guide to the Value of Reliable Data in Retail Banking by DataFlux Corporation
November 02, 2009 - Banks need to get smarter, attract the right customers, implement customer level risk management, and implement risk adjusted customer relationship pricing and all of this is dependent on trusted data. This paper examines the impact of unreliable data on retail banks. It then defines the requirements needed to guarantee data reliability in banking.
Commodity Coding: Code It or Buy (Too Much of) It by DataFlux Corporation
November 02, 2009 - This paper will present you with all the information needed to make an educated decision on whether to commodity coding is right for your organization and it will help with choosing the commodity coding that fits your company.
Getting Started with Master Data Management by DataFlux Corporation
December 2008 - This paper focuses on what is master data, why it is needed, how to get started in managing it, and methodologies for implementing master data management.
Implementing a Data Quality Strategy by DataFlux Corporation
January 06, 2009 - In this program Gartner Research Vice President Ted Fredman and DataFlux President and CEO Tony Fisher discuss the data quality issues facing business today as well as the solutions that can solve these problems.
Populating a Data Quality Scorecard with Relevant Metrics by DataFlux Corporation
June 2008 - Too often, data governance teams rely on existing measurements as the metrics used to populate a data quality scorecard. But without a defined understanding of the relationship between specific measurement scores and the business's success criteria..
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