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Title Date Added Company

Master Data and Master Data Management: An Introduction 2007-01-23 01:00:26 SAS Institute

Master Data Management is more than just an application. It is a composition of tools, methods and policies that will mold the future of exploiting the value of the corporate information asset. The secrets to success lie in understanding how MDM will transition the organization into one with a strong data governance framework, articulating the roles and responsibilities for data stewardship and accountability, and creating a culture of proactive data quality assurance. A successful master data management implementation will lead to more effective integration of business and technology, better organizational collaboration and productivity, and will ultimately result in increased competitive advantage.

Data Profiling: The Diagnosis for Better Enterprise Information 2007-01-23 01:00:26 SAS Institute

The very foundation of CRM and ERP systems is the data that drives these implementations. Beginning a data-driven initiative without first understanding the existing, underlying data is like repairing an automobile without first understanding the problems inside the engine. To repair the engine, the mechanic first has to understand the breadth and depth of the problem. Successful data quality begins with a clear understanding of the integrity of the current data. Data profiling, also called data discovery, gives the diagnosis of the existing data to begin building a successful data improvement and integration effort through consistent, accurate and reliable data throughout the organization.

Master Data Management: Challenges to Success 2007-01-23 01:00:26 SAS Institute

Introducing a Master Data Management (MDM) program is intended to generate a number of benefits to enterprise information and data management. By creating an environment guided by data governance policies and procedures to consolidate replicated versions of data into a single version of the truth (shared by both analytical and operational applications), MDM can alleviate problems related to the consistency, completeness and accuracy that have limited the potential of other strategic initiatives. MDM, however, is sometimes viewed as a disruptive technology. Indeed, opting for an MDM solution introduces organizational challenges that need to be addressed as a prelude to a successful implementation.

The Challenges of Customer Data Integration (CDI) 2007-01-23 01:00:26 SAS Institute

There are a dozen parables that illustrate the wise advice of the slow and deliberate approach to a difficult task. CRM projects have embraced the "Hurry up and deploy" rule of thumb and the results have been less than stellar. The result is usually a long-awaited customer dashboard that displays inaccurate information. With the advent of analytical CRM and Customer Data Integration (CDI) solutions, companies have realized that customer information is not a burning bush, but rather many small campfires flaring up across departments and desktops. However, as with other enterprise initiatives, CDI programs are unique and require specialized skills, technologies, and implementation processes. And accompanying them is a distinct set of challenges.

DataFlux Version 7 Technology: The Convergence of Data Quality and Data Integration 2007-01-23 01:00:26 SAS Institute

With its Version 7 technology release, DataFlux fuses elements of data quality and data integration to allow companies to intelligently build a single, unified view of the enterprise. This white paper explores the DataFlux Data Quality Integration Solution - and the technology components needed to turn disparate data into usable information.

Using Data Integration to Build a Single, Accurate and Consistent Customer View 2007-01-23 01:00:26 SAS Institute

One of the major challenges facing organizations is the need to create a single, accurate, consistent, and timely view of their customers - a view that cuts across all of their applications, systems, business units, and customer touch points. While many organizations have some sense of the value of such a view, far too many organizations fail to grasp the ramifications of not having an accurate, complete view of their customers. Surprisingly, more than a few organizations feel that they can operate efficiently and competitively without taking extra steps to obtain such a view of their customers. However, there is an approach that is specifically designed to help organizations get a complete view of their customer - Customer Data Integration (CDI).

Customer Data Integration: Creating One True View of the Customer 2007-01-23 01:00:26 SAS Institute

To maintain, manage, and track the critically important relationships and the associated customer activity, corporations are investing valuable time and resources into managing customer data with Customer Data Integration (CDI) systems. CDI is a combination of technologies and processes that manage the integration held within customer information systems so that interactions can be managed for the mutual benefit of both the customer and the business. After all, the ultimate success of a relationship between a business and a customer is determined by the quality of the interaction. CDI systems are complex puzzles with many interlocking pieces, where each individual piece serves a purpose.

Data Monitoring: Add Controls to Your Data Governance Program 2007-01-23 01:00:26 SAS Institute

Data, in essence, reflects the changing world around. Customer records become obsolete as people move or switch jobs. Catalogs for products and supplies become outdated. Without a commitment to ongoing data quality, the integrity of an organization's data quickly becomes incorrect or invalid as it reaches core applications. Data monitoring has become a key component of a complete data quality integration practice, giving the tools needed to understand how and when the data strays from its intended purpose. Monitoring also helps to identify and correct these inefficiencies through the automated, ongoing enforcement of customizable business rules. Data monitoring ensures that once the data becomes consistent, accurate and reliable, it remains that way over time, giving the confidence when making information-based decisions for the organization.

Five Steps to More Valuable Enterprise Data 2007-01-23 01:00:26 SAS Institute

In competitive marketplace, many organizations are implementing bold and innovative new strategies to get ahead. Sales, marketing, customer support and other initiatives require a reliable source of data about customers, products and other entities. This data forms the basis of both operational decisions (Does this customer own a certain product?) and customer analytics (Which customers are potential opportunities for up-sell). However, organizations frequently ignore the quality of the underlying data, leading to poor decisions, bad strategies and insufficient customer service. This paper shows how five-phase process can help companies analyze, improve and control corporate data. These five factors provide a technological framework that helps improve the quality of corporate information.

Data Quality in the Corporate Information Factory 2007-01-23 01:00:26 SAS Institute

The Corporate Information Factory is the structure that generates and harnesses the power of information and data is the fuel that feeds that engine. Like any engine, the Corporate Information Factory consists of a series of interacting components that work in concert with each other to provide a robust infrastructure. In order for the Corporate Information Factory engine to run smoothly, each component in the engine must execute at peak efficiency. The goal of this paper is to detail the data quality and data augmentation processes that need to be in place to obtain the strategic information yield. Data Quality is instrumental in ensuring that the components of the Corporate Information Factory can be successfully integrated and can operate at optimal efficiency.

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Title	Date Added	Company
Master Data and Master Data Management: An Introduction	2007-01-23 01:00:26	SAS Institute
Master Data Management is more than just an application. It is a composition of tools, methods and policies that will mold the future of exploiting the value of the corporate information asset. The secrets to success lie in understanding how MDM will transition the organization into one with a strong data governance framework, articulating the roles and responsibilities for data stewardship and accountability, and creating a culture of proactive data quality assurance. A successful master data management implementation will lead to more effective integration of business and technology, better organizational collaboration and productivity, and will ultimately result in increased competitive advantage.
Data Profiling: The Diagnosis for Better Enterprise Information	2007-01-23 01:00:26	SAS Institute
The very foundation of CRM and ERP systems is the data that drives these implementations. Beginning a data-driven initiative without first understanding the existing, underlying data is like repairing an automobile without first understanding the problems inside the engine. To repair the engine, the mechanic first has to understand the breadth and depth of the problem. Successful data quality begins with a clear understanding of the integrity of the current data. Data profiling, also called data discovery, gives the diagnosis of the existing data to begin building a successful data improvement and integration effort through consistent, accurate and reliable data throughout the organization.
Master Data Management: Challenges to Success	2007-01-23 01:00:26	SAS Institute
Introducing a Master Data Management (MDM) program is intended to generate a number of benefits to enterprise information and data management. By creating an environment guided by data governance policies and procedures to consolidate replicated versions of data into a single version of the truth (shared by both analytical and operational applications), MDM can alleviate problems related to the consistency, completeness and accuracy that have limited the potential of other strategic initiatives. MDM, however, is sometimes viewed as a disruptive technology. Indeed, opting for an MDM solution introduces organizational challenges that need to be addressed as a prelude to a successful implementation.
The Challenges of Customer Data Integration (CDI)	2007-01-23 01:00:26	SAS Institute
There are a dozen parables that illustrate the wise advice of the slow and deliberate approach to a difficult task. CRM projects have embraced the "Hurry up and deploy" rule of thumb and the results have been less than stellar. The result is usually a long-awaited customer dashboard that displays inaccurate information. With the advent of analytical CRM and Customer Data Integration (CDI) solutions, companies have realized that customer information is not a burning bush, but rather many small campfires flaring up across departments and desktops. However, as with other enterprise initiatives, CDI programs are unique and require specialized skills, technologies, and implementation processes. And accompanying them is a distinct set of challenges.
DataFlux Version 7 Technology: The Convergence of Data Quality and Data Integration	2007-01-23 01:00:26	SAS Institute
With its Version 7 technology release, DataFlux fuses elements of data quality and data integration to allow companies to intelligently build a single, unified view of the enterprise. This white paper explores the DataFlux Data Quality Integration Solution - and the technology components needed to turn disparate data into usable information.
Using Data Integration to Build a Single, Accurate and Consistent Customer View	2007-01-23 01:00:26	SAS Institute
One of the major challenges facing organizations is the need to create a single, accurate, consistent, and timely view of their customers - a view that cuts across all of their applications, systems, business units, and customer touch points. While many organizations have some sense of the value of such a view, far too many organizations fail to grasp the ramifications of not having an accurate, complete view of their customers. Surprisingly, more than a few organizations feel that they can operate efficiently and competitively without taking extra steps to obtain such a view of their customers. However, there is an approach that is specifically designed to help organizations get a complete view of their customer - Customer Data Integration (CDI).
Customer Data Integration: Creating One True View of the Customer	2007-01-23 01:00:26	SAS Institute
To maintain, manage, and track the critically important relationships and the associated customer activity, corporations are investing valuable time and resources into managing customer data with Customer Data Integration (CDI) systems. CDI is a combination of technologies and processes that manage the integration held within customer information systems so that interactions can be managed for the mutual benefit of both the customer and the business. After all, the ultimate success of a relationship between a business and a customer is determined by the quality of the interaction. CDI systems are complex puzzles with many interlocking pieces, where each individual piece serves a purpose.
Data Monitoring: Add Controls to Your Data Governance Program	2007-01-23 01:00:26	SAS Institute
Data, in essence, reflects the changing world around. Customer records become obsolete as people move or switch jobs. Catalogs for products and supplies become outdated. Without a commitment to ongoing data quality, the integrity of an organization's data quickly becomes incorrect or invalid as it reaches core applications. Data monitoring has become a key component of a complete data quality integration practice, giving the tools needed to understand how and when the data strays from its intended purpose. Monitoring also helps to identify and correct these inefficiencies through the automated, ongoing enforcement of customizable business rules. Data monitoring ensures that once the data becomes consistent, accurate and reliable, it remains that way over time, giving the confidence when making information-based decisions for the organization.
Five Steps to More Valuable Enterprise Data	2007-01-23 01:00:26	SAS Institute
In competitive marketplace, many organizations are implementing bold and innovative new strategies to get ahead. Sales, marketing, customer support and other initiatives require a reliable source of data about customers, products and other entities. This data forms the basis of both operational decisions (Does this customer own a certain product?) and customer analytics (Which customers are potential opportunities for up-sell). However, organizations frequently ignore the quality of the underlying data, leading to poor decisions, bad strategies and insufficient customer service. This paper shows how five-phase process can help companies analyze, improve and control corporate data. These five factors provide a technological framework that helps improve the quality of corporate information.
Data Quality in the Corporate Information Factory	2007-01-23 01:00:26	SAS Institute
The Corporate Information Factory is the structure that generates and harnesses the power of information and data is the fuel that feeds that engine. Like any engine, the Corporate Information Factory consists of a series of interacting components that work in concert with each other to provide a robust infrastructure. In order for the Corporate Information Factory engine to run smoothly, each component in the engine must execute at peak efficiency. The goal of this paper is to detail the data quality and data augmentation processes that need to be in place to obtain the strategic information yield. Data Quality is instrumental in ensuring that the components of the Corporate Information Factory can be successfully integrated and can operate at optimal efficiency.