2. Consul- ting Services Outsour- cing Services Techno- logy Services Local Profes- sional Services Competence Centers Business Intelligence WebTech SAP Infrastructure Management Application Services Audit, Consulting Project Management Dvlpt, Migration ImplementationERPTesting Application Mngmt High Tech Consulting Engineering Science Engineering R&D Embedeed Software Scientific Calculations Infrastructure Services AuditConsulting Project Mngmt InsourcingSecurityHelpdesk Roll out

3. What is Data Mining? Data Mining with SQL Server 2008 Demo Conclusion

5. Exploring and analysing big volumes of data using statistical techniques and computing in order to transform raw data into valuable information Data Mining is also known as: Machine Learning Predictive Analytics

6. Customer Lifetime value Predict customer purchasing or behaviour (churn, migration to other products…) Promotion and sale of additional products Fraud detection Financial risk assessment (loans etc.) Segmentation and clustering of customers to understand them better Better advertising Income and profit forecasting

7. TimeTime Business Value How many customer did we lose ? What was their age? Which customer types are at risk and why ? What should we offer this customer right now ? Measurement (historical) Prediction(future)

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

10. Business Understanding Define Business Objectives Putting Data Mining to Work Making Changes to the Business Data Understanding Data Collection Data Preparation Cleansing & Transformation Modeling Mining Task Evaluation Mining Model Assessment Prediction (Scoring) Deployment Application Integration Data

11. Business Understanding Define Business Objectives Putting Data Mining to Work Making Changes to the Business Data Understanding Data Collection Data Preparation Cleansing & Transformation Modeling Mining Task Evaluation Mining Model Assessment Prediction (Scoring) Deployment Application Integration Data Analysis Services Integration Services Excel Analysis Services Integration Services Excel Analysis Services (Data Mining) Analysis Services (Data Mining) Analysis Services Integration Services Reporting Services Analysis Services Integration Services Reporting Services Analysis Services (Data Mining) Analysis Services (Data Mining)

12. Analysis Services Server Mining Model Data Mining Algorithm Deploy BI Dev Studio (Visual Studio) App Data DataSourceDataSource

13. Data Mining SQL extensions (DMX) (DMX) Application Application Developer Developer Application Application Developer Developer Data Mining Data Mining Specialist Specialist Data Mining Data Mining Specialist Specialist Microsoft Dynamics CRM Analytics Foundation Microsoft Dynamics CRM Analytics Foundation SQL Server 2008 Business Intelligence Development Studio SQL Server 2008 Business Intelligence Development Studio Microsoft SQL Server 2008 Analysis Services Information Information Worker Worker Information Information Worker Worker Data Mining Add-ins for the 2007 Microsoft Office system Microsoft SQL Server 2008 Data Mining BI Analyst BI Analyst Custom Algorithms

14. Association rules Clustering Decision Trees Linear regression Logistic regression Naïve Bayes Neural nets Sequence clustering Time series Decision Trees Time Series Association Naïve Bayes Clustering Neural Networks Sequence Clustering

15. 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, [Products] TABLE ( [Product Name] TEXT KEY )… ) USING Microsoft_Decision_Trees Possibility of nested case: a table instead of a unique value

16. Data Preparation Data Modeling Accuracy and Validation Model Usage and Management “What Microsoft has done is to make data mining available on the desktop to everyone” - David Norris, Associate Analyst, Bloor Research

18. Date : March,13 th 2008 Id : F-B109 Track : DataBase Level : 300

20. Enhancement of Microsoft Time series algorithm with ARIMA Easy partition of your data into training and test sets Possibility to build mining models on filtered subsets (e.g just male customers) Access to all mining structure columns, not just columns included in the model (drillthrough functionality) Cross-validation feature added

21. Nine Data Mining algorithms + viewers BI Dev Studio for developers and analysts Integration with SSIS, SSAS, and SSRS New world of “smart applications” Complete platform for all levels of data mining experience (via interface or programming)

22. “Data Mining with SQL Server 2005” Book by Jamie MacLennan and ZhaoHui Tang Wiley 2005, ISBN 0-471-46261-6 SQL Server Data Mining www.SQLServerDataMining.com SQL Server Developer Center http://msdn.microsoft.com/sql SQL Server Forums http://forums.microsoft.com/msdn Trial Software and Virtual Labs http://www.microsoft.com/technet/downloads/trials/default.mspx

23. Come and visit us at our stand

24. © 2007 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.