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Title Date Added Company

Performance-Driven Software Development 2006-09-22 01:00:14 Compuware

When software automates business processes, software performance is the limiting factor for business performance. A slow order processing engine necessarily means slowly processed orders. Even though software performance matters to the business, it's low on the priority list for most application development organizations. To meet business needs — and to improve the efficiency of their own operations — development shops must mature their performance practices from pure firefighting to performance-driven development.

Data Quality and Regulatory Compliance: Watching Your Watchlists 2007-01-23 01:00:26 SAS Institute

This paper offers a complete approach to watchlist compliance. There are other approaches, of course, approaches that leverage matching to the exclusion of other technologies and preparations. While matching is perhaps the focal technology in watchlist compliance, it might not produce - in isolation - the results needed for complete, accurate compliance, especially for large, geographically-dispersed organizations. In these instances, the complexity of their data and data sources is such that matching alone should not be replied on for effective compliance.

Data Quality by the Numbers: Best Practices for Managing Business Data 2007-01-23 01:00:26 SAS Institute

The use of data quality technology for customer data is far more prevalent than the use of the technology for business data. The reason for this imbalance has as much to do with the organizations that are acquiring data quality solutions as it does with data quality technology itself. Many data quality companies simply cannot support business data to any significant degree. Even some of the companies that profess to support business data can only do so to a limited degree. This paper will consider the issue of business data and look at some of the most common types of business data, as well as at some of the more esoteric kinds of this data.

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.

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Title	Date Added	Company
Performance-Driven Software Development	2006-09-22 01:00:14	Compuware
When software automates business processes, software performance is the limiting factor for business performance. A slow order processing engine necessarily means slowly processed orders. Even though software performance matters to the business, it's low on the priority list for most application development organizations. To meet business needs — and to improve the efficiency of their own operations — development shops must mature their performance practices from pure firefighting to performance-driven development.
Data Quality and Regulatory Compliance: Watching Your Watchlists	2007-01-23 01:00:26	SAS Institute
This paper offers a complete approach to watchlist compliance. There are other approaches, of course, approaches that leverage matching to the exclusion of other technologies and preparations. While matching is perhaps the focal technology in watchlist compliance, it might not produce - in isolation - the results needed for complete, accurate compliance, especially for large, geographically-dispersed organizations. In these instances, the complexity of their data and data sources is such that matching alone should not be replied on for effective compliance.
Data Quality by the Numbers: Best Practices for Managing Business Data	2007-01-23 01:00:26	SAS Institute
The use of data quality technology for customer data is far more prevalent than the use of the technology for business data. The reason for this imbalance has as much to do with the organizations that are acquiring data quality solutions as it does with data quality technology itself. Many data quality companies simply cannot support business data to any significant degree. Even some of the companies that profess to support business data can only do so to a limited degree. This paper will consider the issue of business data and look at some of the most common types of business data, as well as at some of the more esoteric kinds of this data.
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.