Good quality data is essential to a successful business intelligence application. You are probably aware that Microsoft SQL Server includes some useful data quality tools such as Fuzzy Grouping or Fuzzy Lookup. However, there is one tool you may have overlooked—SQL Server Data Mining. When used operationally, SQL Server Data Mining is extremely useful for finding data that lies outside the boundaries of known good data, and it finds these outliers inductively rather than relying on exhaustively hard-coded rules. In this webcast, we introduce this new, adaptive approach to data quality and we show how adaptive quality can be applied at many phases of the business intelligence project—whether data entry, during warehouse loading, or during analysis.
Presenter:
Donald Farmer, Principal Program Manager, Microsoft Corporation
Donald Farmer has worked in the Microsoft Business Intelligence team since 2001. He has worked on both the Microsoft SQL Server Analysis Services and Integration Services product teams. Donald is now a principal program manager for SQL Server Analysis Services, working to build a seamless integration of predictive and exploratory analytics with the Microsoft business intelligence offering. Donald is a popular speaker at international events for both business and technical audiences on a wide range of topics, including data integration, information quality, metadata intelligence, master data management, and predictive analytics. He is the author of a number of books and articles. Prior to joining Microsoft, Donald worked not only in business intelligence, but also in fields as varied as medieval archaeology and fish farming.
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