At the heart of any enterprise data environment lies its Master Data, consisting of entities such as “customers,” “products,” “locations,” etc. that must be populated to ensure the smooth operation of the organization’s operational transactions as well as its business intelligence and analytical processing. Yet, the traditional approach to vertical application architecture has led to distribution and replication of master reference data across different databases and systems, which introduces a number of challenges to effective master data management, such as:

• complexity introduced due to local vs. centralized update strategies
• variations in entity life cycles by different applications that change the ways that individual instances are identified
• Different ways of categorizing and classifying entities are obscured through clusters of code tables and mappings
• Limited governance of change control leads to problems in historical reporting

Not only that, but inadequate Master Data management is both impacted by data quality problems, and can cause issues that permeate the enterprise, reducing efficiency and limiting the scope of the enterprise’s operations. These kinds of business impacts are becoming more acute today, and enterprises are realizing that they need to manage their Master Data if they are to take full advantage of their information assets.

• What Master Data is, and why it represents a special class of data with unique management needs.
• The problems that can arise from inadequately managed Master Data.
• How to detect and mitigate data quality issues that originate in Master Data.
• The role of data quality technology in Master Data management.
• Enterprise architecture designs that address the management needs of Master Data.
• Strategies for the consolidation of Master Data.
• Management strategies for governance of Master Data, especially local versus central updating.
• Life cycle management for Master Data.
• Management of changes to reference data that categorizes Master Data, especially in the context of historical reporting.

David Loshin is an internationally recognized as an expert in Information Quality and the author of the books “Enterprise Knowledge Management – The Data Quality Approach,” (Morgan Kaufmann, 2001) and “Business Intelligence: The Savvy Manager’s Guide” (Morgan Kaufmann, 2003). He hosts an expert channel at the Business Intelligence Network, serves on the Editorial Board of, and provides a monthly column for DM Review magazine, and is a quarterly featured columnist for the Data Administration Newsletter.

Malcolm Chisholm is an independent consultant focusing on metadata engineering and data management. Over the past 25 years he has worked in a wide variety of sectors and IT environments. His focus has been to find ways to leverage metadata assets to improve business efficiency and effectiveness. He is the author of “How to Build a Business Rules Engine” and “Managing Reference Data in Enterprise Databases” and frequently writes and speaks on these topics.