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.

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.

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.

The Data Quality Business Case
by David Loshin
Projecting Return on Investment.

Using Master Reference Data Management to Unify Your Business
by David Loshin
This white paper examines how companies assign and improve value to enterprise information using master data management strategies – an excellent resource for people wondering how to manage and aggregate data.

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.

Data Warehouse Mistakes to Avoid
by Larry P. English
Ten Mistakes to Avoid if Your Data Warehouse is to Deliver Quality Information.

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Blog: Mike Ferguson
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