Many users who struggle with data mining do not realize that their problems start with badly prepared data. This webcast is a very practical introduction to some of the important topics that you should understand when preparing your data for a data mining project with Microsoft SQL Server . Issues include finding the right data, handling missing values, identifying and fixing overloaded fields, and deriving useful variables.

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.

Time series data is one of the most useful sources for data mining. Whether it be estimating future gasoline prices or understanding the relationship between the weather and sales, there is a role for time series analysis. In the new release of Microsoft SQL Server 2008 Data Mining, Microsoft has introduced some important enhancements to our support in this area, making powerful analysis both more effective and easier to use. In this webcast, we not only introduce these new features, but we also cover many of the business scenarios for which time series prediction is invaluable.
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Microsoft SQL Server 2008 includes several important enhancements for data mining. One area will be of particular interest to the user who has progressed beyond simple models—there are new features for building many and varied models over common mining structures, and also for validating the accuracy of these models. In this webcast, you learn how to use these new techniques, not only through the user interface (UI), but also programmatically from within your own applications.

The key performance indicator (KPI) is an essential tool in building business intelligence applications for executive and strategic use. However, traditional KPIs are built over historical data, showing what has happened in the past and the current state of the business. There is increasing demand for "predictive KPIs," which show not only current status but project future status. For example, rather than knowing how many customers churned last quarter, would it not be useful to know how many customers are in danger of churning next quarter? This webcast demonstrates how to design, build, and deploy predictive KPIs using Microsoft SQL Server Data Mining. Although there are useful worked examples, even non-technical users can benefit from this webcast by finding new possibilities for KPIs.

Microsoft SQL Server Data Mining Add-ins for Excel empower information workers with an easy-to-use, yet remarkably complete, set of tools for predictive analysis, directly within their tool of choice – Microsoft Office Excel.  Attend this webcast to understand how add-ins can enable your employees to utilize this simple and powerful technology to perform advanced analysis.

Data mining is well-known for empowering users with insightful analyses from vast quantities of information. In many cases, the best way to present these analyses is visually. Microsoft SQL Server Data Mining includes useful visualization tools that can be embedded in your own applications. However, it is also possible to write your own visualizations, which can be useful for your specific needs. In this webcast, we show how to embed the existing visualization tools, but we also describe how to you can create your own . Examples include both rich and thin client visualizations.

Microsoft SQL Server Data Mining is not just a powerful application—it is a complete platform that customers and partners can embed, extend, and customize. In this webcast, we look at various ways in which this can be achieved. In particular, we examine the custom algorithms and stored procedures that can extend the range of the analytic technology. Join this session to learn techniques for designing, building, and testing these interesting and useful extensions.

There are many interesting and well-known uses of data mining for business analysis. However, for the application developer, data mining can also be useful for building adaptive, "intelligent" applications. In this webcast, we demonstrate how to build a simple application with Microsoft SQL Server Data Mining  that collects data about usage as it goes along, and then uses that data to offer smart defaults—a truly adaptive application.

Web applications typically generate large quantities of data, often capturing important aspects of your customers' behavior. What you may not realize is that this data can be excellent for data mining, and the results can be extremely useful in building out smarter applications and Web interfaces. In this session, we introduce the concept of a "smart application" and walk through specific examples of how to build these compelling technologies in for your own Web audience.