SQL Server 2008 Data Mining with PowerPivot and Excel 2010

•

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Mark Tabladillo

Presentation delivered at SQL Saturday Atlanta GA -- April 22, 2010

Business

SS2008 Data Mining with
Excel 2010 and PowerPivot

Mark Tabladillo Ph.D.
http://marktab.net
April 22, 2010

W. Edwards Deming

© 2010 Mark Tabladillo Ph.D. 2

W. Edwards Deming

Design and
Redesign
Consumer
research

Suppliers of
Receipt and Consumers
materials and
equipment test of materials
Distribution
A
Production, assembly, inspection
B

C

D Tests of processes,
machines, methods,
costs

© 2010 Mark Tabladillo Ph.D. 3

CRISP-DM Version 1.0

© 2010 Mark Tabladillo Ph.D. 4

Jeff Hawkins

© 2010 Mark Tabladillo Ph.D. 5

Outline

What is What is
Demos
Data Mining PowerPivot

© 2010 Mark Tabladillo Ph.D. 6

Technology

© 2010 Mark Tabladillo Ph.D. 7

Outline

What is What is
Demos
Data Mining PowerPivot

© 2010 Mark Tabladillo Ph.D. 8

Data Mining Definitions
• Data mining
• Machine Learning
• Data mining algorithms typically use
estimation or optimization to achieve
results (as opposed to only calculations).

© 2010 Mark Tabladillo Ph.D. 9

Data Mining Tasks
• Supervised
– Answer known, what is correlated?
• Unsupervised
– Answer unknown (unspecified), what are the
groups?
• Forecasting
– Given a trend, what is next? Value
Slide

© 2010 Mark Tabladillo Ph.D. 10

Data Mining Add-In for Excel
• Version 10.00.2531.00 (April 2009)
• 32-Bit Add-In
• Microsoft .NET Framework 2.0 (32-bit)
• Office 2007 (Professional, Professional
Plus, Ultimate, Enterprise)
• SQL Server Enterprise or Standard (or
Developer) 2008 or higher

© 2010 Mark Tabladillo Ph.D. 11

The Analyze Tab

© 2010 Mark Tabladillo Ph.D. 12

The Analyze Tab

Menu Option Data Mining Algorithm
Analyze Key Influencers Naïve Bayes
Detect Categories Clustering
Fill from Example Logistic Regression
Forecast Time Series
Highlight Exceptions Clustering
Scenario Analysis (Goal Seek) Logistic Regression
Scenario Analysis (What If) Logistic Regression
Prediction Calculator Logistic Regression
Shopping Basket Analysis Association Rules
© 2010 Mark Tabladillo Ph.D. 13

Data Mining Tab

© 2010 Mark Tabladillo Ph.D. 14

Data Mining Tab

∞

© 2010 Mark Tabladillo Ph.D. 15

Data Mining Tab

© 2010 Mark Tabladillo Ph.D. 16

Outline

What is What is
Demos
Data Mining PowerPivot

© 2010 Mark Tabladillo Ph.D. 17

PowerPivot for Excel
• Take advantage of familiar Excel tools and
features
• Process massive amounts of data in seconds
• Load even the largest data sets from virtually
any source
• Use powerful new analytical capabilities, such as
Data Analysis Expressions (DAX)
• Make the most of multi-core processors and
gigabytes of memory

© 2010 Mark Tabladillo Ph.D. 18

PowerPivot for Excel
• SQL Server
• SQL Azure
• Oracle, Teradata, Sybase, Informix, IBM DB2
• OLEDB/ODBC
• Analysis Services (SSAS)
• Reporting Services (SSRS)
• Excel, Text File

© 2010 Mark Tabladillo Ph.D. 19

What is it?

© 2010 Mark Tabladillo Ph.D. 20

What is it?

© 2010 Mark Tabladillo Ph.D. 21

PowerPivot Reference
• http://www.powerpivot.com (Product Site)
• http://www.powerpivotpro.com (Blog Site)

© 2010 Mark Tabladillo Ph.D. 22

Outline

What is What is
Demos
Data Mining PowerPivot

© 2010 Mark Tabladillo Ph.D. 23

W. Edwards Deming

© 2010 Mark Tabladillo Ph.D. 24

Resources
• MarkTab.NET
Links, video resources and information for data mining

© 2010 Mark Tabladillo Ph.D. 25

Regroup and Conclusion
• Main Points from this Presentation

© 2010 Mark Tabladillo Ph.D. 26

Contact Information
• Mark Tabladillo
Twitter @marktabnet

• Also on:
Linked In
Facebook

© 2010 Mark Tabladillo Ph.D. 27

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Insurance IoT is a social good! Because: 🔵 Expected losses can be reduced structured programs with both real-time mitigation actions to solve a specific situation, and behavioral change mechanisms to promote safer behaviors (both in personal and commercial lines) 🔵 Rate can be matched to risks better: - Smaller and more accurate pricing clusters allow a large part of profiles to receive a lower rate - Even with the ability to price the individual probability of loss (cluster of one) the insurance foundation isn’t canceled (The risk of each policy will contribute to the expected losses of the portfolio; The premiums paid by many lucky policyholders will be used to pay the claims of the few unlucky policyholders who had an accident in a period of coverage) 🔵 IoT data can be used to increase the effectiveness and efficiency of the claim process, so improving the combined ratio ceteris paribus ⏩ Large part of the policyholders will pay less (and portfolios currently difficult to insure will become more insurable) Insurance IoT (and telematics) is not about providing a discount Insurance IoT (and telematics) is about creating economic value using the data and sharing part of this value with policyholders (and agents & brokers) An integrated holistic adoption of the IoT paradigm is a game changer for your insurance business and a social good: ◾ it allows to improve the availability and affordability of insurance coverages ◾while allowing the sector to write a profitable business Below you can enjoy a selection of best practices in both personal and commercial lines - written with some of the IoT Insurance Observatory members - that have already started to obtain results from their IoT business transformation journeys

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SQL Server 2008 Data Mining with PowerPivot and Excel 2010

1. SS2008 Data Mining with Excel 2010 and PowerPivot Mark Tabladillo Ph.D. http://marktab.net April 22, 2010

3. W. Edwards Deming Design and Redesign Consumer research Suppliers of Receipt and Consumers materials and equipment test of materials Distribution A Production, assembly, inspection B C D Tests of processes, machines, methods, costs © 2010 Mark Tabladillo Ph.D. 3

9. Data Mining Definitions • Data mining • Machine Learning • Data mining algorithms typically use estimation or optimization to achieve results (as opposed to only calculations). © 2010 Mark Tabladillo Ph.D. 9

10. Data Mining Tasks • Supervised – Answer known, what is correlated? • Unsupervised – Answer unknown (unspecified), what are the groups? • Forecasting – Given a trend, what is next? Value Slide © 2010 Mark Tabladillo Ph.D. 10

11. Data Mining Add-In for Excel • Version 10.00.2531.00 (April 2009) • 32-Bit Add-In • Microsoft .NET Framework 2.0 (32-bit) • Office 2007 (Professional, Professional Plus, Ultimate, Enterprise) • SQL Server Enterprise or Standard (or Developer) 2008 or higher © 2010 Mark Tabladillo Ph.D. 11

13. The Analyze Tab Menu Option Data Mining Algorithm Analyze Key Influencers Naïve Bayes Detect Categories Clustering Fill from Example Logistic Regression Forecast Time Series Highlight Exceptions Clustering Scenario Analysis (Goal Seek) Logistic Regression Scenario Analysis (What If) Logistic Regression Prediction Calculator Logistic Regression Shopping Basket Analysis Association Rules © 2010 Mark Tabladillo Ph.D. 13

18. PowerPivot for Excel • Take advantage of familiar Excel tools and features • Process massive amounts of data in seconds • Load even the largest data sets from virtually any source • Use powerful new analytical capabilities, such as Data Analysis Expressions (DAX) • Make the most of multi-core processors and gigabytes of memory © 2010 Mark Tabladillo Ph.D. 18

19. PowerPivot for Excel • SQL Server • SQL Azure • Oracle, Teradata, Sybase, Informix, IBM DB2 • OLEDB/ODBC • Analysis Services (SSAS) • Reporting Services (SSRS) • Excel, Text File © 2010 Mark Tabladillo Ph.D. 19

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