1. BIN06-IS Understanding the Data Mining Add-Ins for Excel 2007 Lynn Langit MSDN Developer Evangelist – Southern California http://blogs.msdn.com/SoCalDevGal

4. What and Why Data Mining? Predictive Analytics Presentation Exploration Discovery Passive Interactive Proactive Role of Software Business Insight Canned reporting Ad-hoc reporting OLAP Data mining

5. Data Mining Problems

6. From Scenarios to Tasks

7. From Tasks to Techniques

8. Microsoft’s Predictive Analytics Data Mining SQL extensions (DMX) Application Developer Data Mining Specialist Microsoft Dynamics CRM Analytics Foundation SQL Server 2005 Business Intelligence Development Studio Microsoft SQL Server 2005 Analysis Services Information Worker Data Mining Add-ins for the 2007 Microsoft Office system Microsoft SQL Server 2005 Data Mining BI Analyst Custom Algorithms

9. Data Mining Add-ins for Office 2007 Table Analysis Tools for Excel 2007 Data Mining Template for Visio 2007 Data Mining Client for Excel 2007

10. Microsoft Data Mining Lifecycle CRISP-DM SSAS (Data Mining) Excel SSAS (DSV) Query Excel SSIS SSAS SSRS Excel Your Apps SSIS SSAS Excel Data www.crisp-dm.org Business Understanding Data Understanding Data Preparation Modeling Evaluation Deployment

11. Lifecycle – Understand & Prepare Excel Explore Data Clean Data Add-ins

12. Lifecycle - Prepare Excel Partition Data ADOMD.Net Add-ins SQL Server Analysis Services Data Source Data

13. Understand & Prepare specifics

14. Demo 1 – Explore / Clean / Partition Data

15. Lifecycle – Model & Evaluate Excel Modeling & Evaluation Add-ins SQL Server Analysis Services Data Source Data Mining Models

16. Modeling Specifics

17. Demo 3 – Modeling

18. Evaluation Specifics

19. Demo 4 – Evaluation

20. Data Mining – Logical Model Mining Model Mining Model Training Data DB data Client data Application data Data Mining Engine Data To Predict Predicted Data Mining Model DB data Client data Application data “ Just one row ” Data Mining Engine

21. Data Mining - Physical Model Analysis Services Server Mining Model Data Mining Algorithm Data Source Your Application OLE DB/ ADOMD/ XMLA Deploy BI Dev Studio (Visual Studio) App Data

22. Architecture Excel Modeling Query Add-ins SQL Server Analysis Services Data Source Data Mining Models

23. Architecture Excel Modeling Query Add-ins SQL Server Analysis Services Data Source Data Mining Models

24. Architecture Excel Modeling Query Add-ins SQL Server Analysis Services Data Source Data Mining Models

27. Advanced Techniques - DMX

29. Data Mining Extensions (DMX) CREATE MINING MODEL CreditRisk (CustID LONG KEY, Gender TEXT DISCRETE, Income LONG CONTINUOUS, Profession TEXT DISCRETE, Risk TEXT DISCRETE PREDICT) USING Microsoft_Decision_Trees INSERT INTO CreditRisk (CustId, Gender, Income, Profession, Risk) Select CustomerID, Gender, Income, Profession,Risk From Customers Select NewCustomers.CustomerID, CreditRisk.Risk, PredictProbability(CreditRisk.Risk) FROM CreditRisk PREDICTION JOIN NewCustomers ON CreditRisk.Gender=NewCustomer.Gender AND CreditRisk.Income=NewCustomer.Income AND CreditRisk.Profession=NewCustomer.Profession

30. CREATE MINING MODEL CREATE MINING MODEL MyModel ( [CustID] LONG KEY, [Gender] TEXT DISCRETE, [Marital Status] TEXT DISCRETE, [Education] TEXT DISCRETE, [Home Ownership] TEXT DISCRETE PREDICT, [Age] LONG CONTINUOUS, [Income] DOUBLE CONTINUOUS ) USING Microsoft_Decision_Trees

32. Data Mining Interfaces – XMLA ++ XMLA Over TCP/IP XMLA Over HTTP Analysis Server (msmdsrv.exe) OLAP Data Mining Server ADOMD.NET .Net Stored Procedures Microsoft Algorithms Third Party Algorithms OLEDB for OLAP/DM ADO/DSO Any Platform, Any Device C++ App VB App .Net App AMO Any App ADOMD.NET WAN DM Interfaces

34. Resources Technical Communities, Webcasts, Blogs, Chats & User Groups http://www.microsoft.com/communities/default.mspx Microsoft Developer Network (MSDN) & TechNet http://microsoft.com/msdn http://microsoft.com/technet Trial Software and Virtual Labs http://www.microsoft.com/technet/downloads/trials/default.mspx Microsoft Learning and Certification http://www.microsoft.com/learning/default.mspx SQL Server Data Mining http://www.sqlserverdatamining.com http://www.microsoft.com/bi/bicapabilities/data-mining.aspx

35. BI Resources from Lynn Langit Foundations of SQL Server 2005 Business Intelligence published by Apress in April 2007 Blog: http://blogs.msdn.com/SoCalDevGal

36. Q&A

38. © 2007 Microsoft Corporation. All rights reserved. This presentation is for informational purposes only. MICROSOFT MAKES NO WARRANTIES, EXPRESS OR IMPLIED, IN THIS SUMMARY.