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Techniques and Tools for Data Quality Management
by David Loshin
In an important new whitepaper, David Loshin examines how data quality technology, tools and techniques support best practices in data quality management. Data Warehousing, Next-Generation Business Intelligence and the Evolution of Data Quality
by David Loshin
David Loshin explains how embedding data quality services directly into applications can help meet key challenges involving: unstructured data; globalization; the explosion of raw data and the need
for real-time synchronization between data sources. Hit the Ground Running with Operational Master Data Management
by David Loshin
Jump-start your master data management (MDM) initiative with the efficient Operational MDM approach. The next generation of data integration and master data management systems employ in-line data
services based on self-learning semantic technology. The Hidden Challenge of Product Information Management
by Martin Boyd
This paper discusses product how automating product data quality is the key to maximizing product information management performance. Automating Product Data Quality for Trouble-Free PIM and MDM
This paper discusses product data quality & governance and how to create PIM-perfect data to maximize PIM-MDM performance. Great E-Commerce Needs Great Data
This paper illustrates that smarter data means smarter E-commerce by discussing the unique nature of product data, its role in effective merchandising and breakthrough technology that automates
product data quality. Total Data Quality; A Business Solution for Today and Tomorrow
From a business perspective, learn how Total Data Quality offers a comprehensive strategy and a full range of tactics for turning raw data into meaningful corporate data assets. Data Quality Essentials
This new white paper describes the techniques used by successful companies to plan and implement data quality processes as part of a point solution or enterprise-wide initiative. Enterprise Information Management: Tentative Past, Promising Future
by Alan Simon
This white paper explains why EIM is finally becoming a reality. Data Quality First: It’s Just Logical
by Michael Schiff
The importance of trusted information to successful data integration and business intelligence. Data Quality: Driving Single View of Customer
Outlining the importance of data quality with reference to single view of customer (SVC). Monitoring Data Quality Performance - Using Data Quality Metrics
Introducing data quality monitoring and reporting policies and protocols, the decisions to acquire and integrate data quality technology become much simpler. The Data Quality Business Case: Projecting Return on Investment
Describing how an organization should tackle a data quality improvement process. The Challenge of Global Data Synchronization
Describing the coming paradigm shift in DQM and explains how CPG enterprises can empower themselves to improve and maintain their critical master data in the era of global data standards. Boosting Data Quality for Business Success
Outlining how an organization should tackle a data quality improvement process. Addressing Data Quality at the Enterprise Level
Six questions your organization should ask to ensure high-quality data. Accelerate Application Data Migration with Informatica
Enterprises should look to proven migration solutions to ensure projects stay on budget and are successful. Data Profiling: The Diagnosis for Better Enterprise Information
Data profiling gives you the thorough diagnosis you need to begin building a credible foundation of high-quality data from throughout your organization. Master Data Management: Challenges to Success
by David Loshin
Exploring some expected challenges in implementing an MDM program, and provide some suggestions that can ease the transition to the MDM environment. Data Quality and Regulatory Compliance: Watching Your Watchlists
by Robert Lerner
Compliance is not going away any time soon, and it should therefore be approached as a long-term, organization-wide requirement. Data Monitoring: Add Controls to your Data Governance Program
Data monitoring has become a key component of a complete data quality integration practice, giving you the tools you need to understand how and when your data strays from its intended purpose. The Data Quality Business Case
by David Loshin
Projecting Return on Investment. Five Steps to More Valuable Enterprise Data
Companies worldwide struggle with inconsistent and unreliable data. This white paper examines a methodology for improving your data, using a five-step data quality integration approach to finding and fixing bad data. The CIO's Guide to Taking Data Asset Metrics to the Boardroom
Data quality can affect all aspects of your organization. This white paper by explains what executives and CIOs need to know to foster an environment of continual data improvement throughout the company. Data Quality by the Numbers: Best Practices for Managing Business Data
by Robert Lerner
Discussing the impact of data quality on non-name-and-address data (i.e., product data). It outlines how the DataFlux approach helps improve the quality of product, numeric or other “business” data within an organization. DataFlux Version 7 Technology: The Convergence of Data Quality and Data Integration
Elements of data quality and data integration are fused to build a unified view of the enterprise. DataFlux Data Quality Integration Solution helps turn disparate data into useful information. Using Data Integration to Build a Single, Accurate and Consistent Customer View
by Robert Lerner
This paper examines the challenges that companies face in creating a unified view of its customers, and discusses how customer data integration (CDI) provides a complete and accurate customer view. Master Data Management: An Introduction
by David Loshin
This white paper helps you define and characterize master data management and discusses what comprises a successful MDM program, organizational challenges, and the importance of data quality. Putting Quality Process in Place to Exploit Technology
by Larry P. English
This paper discusses the issue of corporate information quality in the context of a fusion of technology and organizational processes. Data Warehouse Mistakes to Avoid
by Larry P. English
Ten Mistakes to Avoid if Your Data Warehouse is to Deliver Quality Information. Implementing Data Quality as a Corporate Service
by Colin White
A guide to improve or implement a data quality solution. Making the Most of Your Data Assets
Customer-centric companies will enjoy a significant distinct competitive advantage over traditional companies. Data Quality In The Corporate Information Factory
by Bill Inmon
Companies with the most complete, accurate and reliable information will retain and gain market share. Putting Data Quality Solutions to Work for You
Using DataFlux technology to achieve business success.
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