1. 1 1 The Analyst’s Perspective: Ad-hoc Analysis with Microsoft PowerPivot and Office 2010 Excel Rafal Lukawiecki Strategic Consultant, Project Botticelli Ltd rafal@projectbotticelli.com

2. 2 2 Objectives Introduce powerful self-service analysis with PowerPivot Show use of Microsoft SQL Server 2008 Analysis Services Data Mining The information herein is for informational purposes only and represents the opinions and views of Project Botticelli and/or Rafal Lukawiecki. The material presented is not certain and may vary based on several factors. Microsoft makes no warranties, express, implied or statutory, as to the information in this presentation. Portions © 2010 Project Botticelli Ltd & entire material © 2010 Microsoft Corp. Some slides contain quotations from copyrighted materials by other authors, as individually attributed or as already covered by Microsoft Copyright ownerships. 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 Project Botticelli Ltd as of the date of this presentation. Because Project Botticelli & Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft and Project Botticelli cannot guarantee the accuracy of any information provided after the date of this presentation. Project Botticelli makes no warranties, express, implied or statutory, as to the information in this presentation. E&OE. This seminar is based on a number of sources including a few dozen of Microsoft-owned presentations, used with permission. Thank you to Chris Dial, Tara Seppa, Aydin Gencler, Ivan Kosyakov, Bryan Bredehoeft, Marin Bezic, and Donald Farmer with his entire team for all the support.

3. 3 PowerPivot

4. 4 4 Massive Data Volumes With a few mouse clicks, a user can create and publish intuitive and interactive self-service analysis solutions

5. 5 5 Excel 2010 and Excel Services Interactive slicers enable users to look at the data from various directions in Excel 2010 and in the browser through PowerPivot for SharePoint and Excel Services. Interactive Slicing and Dicing

6. 6 1. Analysing Massive Data Volumes Using PowerPivot 2. Slicer as a Better Filter

7. 7 7 Published Reports SharePoint Farm Report-Based Data Feeds OLTP and OLAP Data Sources Reporting Services as a Data Source

8. 8 1. Report as a Data Source for Analysis

9. 9 9 Share and Collaborate With SharePoint: Publish your PowerPivots as Web applications for your team Schedule data refreshes to keep your analysis up-to- date Manage security just like a document

10. 10 PowerPivot Infrastructure Overview SharePoint Farm WFE App Servers Content dBs Excel, RB, PerfPoint Power User Data Sources Excel Services PowerPivot Mid-Tier AS Engine Browser Standard User PowerPivot Add-In

11. 11 PowerPivot Infrastructure: Excel SharePoint Farm WFE App Servers Content dBs Excel Services Gemini Mid-Tier Gemini Engine Browser Standard User Excel, RB, PerfPoint Power User Data Sources Use of IMBI Engine: In-Memory Column- Based store Once data is imported, all calculations are performed on client Excel now has it’s own local SSAS engine Added Excel power functions for Gemini called DAX (Data Analysis eXpressions) Use of new compression algorithm to significantly compress the data ~ 10:1 Added slicer functionality: not just for UI but for smoother SharePoint integration PowerPivot Add-In

12. 12 Excel, RB, PerfPoint Power User Data Sources Browser Standard User SharePoint Farm WFE App Servers Content dBs Excel Services PowerPivot Mid- Tier AS Engine PowerPivot SharePoint Integration: ECS Viewing Excel Web Access

13. 13 Excel, RB, PerfPoint Power User Data Sources Browser Standard User SharePoint Farm WFE App Servers Content dBs Excel Services PowerPivot Mid- Tier AS Engine PowerPivot SharePoint Integration: Server Action Excel Web Access

14. 14 Data Analysis Expressions (DAX) Simple Excel-style formulas Define new fields in the PivotTable field list Enable Excel users to perform powerful data analysis using the skills they already have Has elements of MDX but does not replace MDX

15. 15 Data Analysis Expressions (DAX) No notion of addressing individual cells or ranges DAX functions refer to columns in the data Sample DAX expressionMeans: = [First Name] &“ ”& [Last Name] String concatenation just like Excel =SUM(Sales[Amount]) SUM function takes a column name instead of a range of cells =RELATED (Product[Cost]) new RELATED function follows relationship between tables

16. 16 DAX Aggregation Functions DAX implements aggregation functions from Excel including SUM, AVERAGE, MIN, MAX, COUNT, but instead of taking multiple arguments (a list of ranges,) they take a reference to a column DAX also adds some new aggregation functions which aggregate any expression over the rows of a table SUMX (Table, Expression) AVERAGEX (Table, Expression) COUNTAX (Table, Expression) MINX (Table, Expression) MAXX (Table, Expression) 16

17. 17 More than 80 Excel Functions in DAX Date and TimeInformationMath and TrigStatisticalText DATEISBLANKABSAVERAGECONCATENATE DATEVALUEISERRORCEILING, ISO.CEILINGAVERAGEAEXACT DAYISLOGICALEXPCOUNTFIND EDATEISNONTEXTFACTCOUNTAFIXED EOMONTHISNUMBERFLOORCOUNTBLANKLEFT HOURISTEXTINTMAXLEN MINUTELNMAXALOWER MONTH Logical LOGMINMID NOWANDLOG10MINAREPLACE SECONDIFMODREPT TIMEIFERRORMROUNDRIGHT TIMEVALUENOTPISEARCH TODAYORPOWERSUBSTITUTE WEEKDAYFALSEQUOTIENTTRIM WEEKNUMTRUERANDUPPER YEARRANDBETWEENVALUE YEARFRACROUND ROUNDDOWN ROUNDUP SIGN SQRT SUM SUMSQ TRUNC

18. 18 Example: Functions over a Time Period TotalMTD (Expression, Date_Column [, SetFilter]) TotalQTD (Expression, Date_Column [, SetFilter]) TotalYTD (Expression, Date_Column [, SetFilter] [,YE_Date]) OpeningBalanceMonth (Expression, Date_Column [,SetFilter]) OpeningBalanceQuarter (Expression, Date_Column [,SetFilter]) OpeningBalanceYear (Expression, Date_Column [,SetFilter] [,YE_Date]) ClosingBalanceMonth (Expression, Date_Column [,SetFilter]) ClosingBalanceQuarter (Expression, Date_Column [,SetFilter]) ClosingBalanceYear (Expression, Date_Column [,SetFilter] [,YE_Date])

19. 19 1. Simplicity of DAX to Relate and Analyse Data

20. 20 Data Mining

21. 21 What does Data Mining Do? Explores Your Data Finds Patterns Performs Predictions

22. 22 Typical Uses Data Mining Seek Profitable Customers Understand Customer Needs Anticipate Customer Churn Predict Sales & Inventory Build Effective Marketing Campaigns Detect and Prevent Fraud Correct Data During ETL

23. 23 Analysis Services Server Mining Model Data Mining Algorithm Data Source Server Mining Architecture Excel/Visio/SSRS/Your App OLE DB/ADOMD/XMLA Deploy BIDS Excel Visio SSMS App Data

24. 24 Mining Model Mining Process DM Engine Training data Data to be predicted Mining Model With predictions

25. 25 Microsoft Decision Trees Use for: Classification: churn and risk analysis Regression: predict profit or income Association analysis based on multiple predictable variable Builds one tree for each predictable attribute Fast

26. 26 1. Decision Trees for Classification of Customers’ Buying Potential

27. 27 Profitability and Risk Finding what makes a customer profitable is also classification or regression Typically solved with: Decision Trees (Regression), Linear Regression, and Neural Networks or Logistic Regression Often used for prediction Important to predict probability of the predicted, or expected profit Risk scoring Logistic Regression and Neural Networks

28. 28 Neural Network & Logistic Regression Applied to Classification Regression Great for finding complicated relationship among attributes Difficult to interpret results Gradient Descent method LR is NNet with no hidden layers AgeEducationSexIncome Input Layer Hidden Layers Output Layer Loyalty

29. 29 1. Neural Networks for Predicting Lending Risk

30. 30 Time Series Uses: Forecast sales Inventory prediction Web hits prediction Stock value estimation Regression trees with extras

31. 31 1. Foerecasting Sales with Time Series

32. 32 Data Mining Techniques AlgorithmDescription Decision Trees Finds the odds of an outcome based on values in a training set Association Rules Identifies relationships between cases Clustering Classifies cases into distinctive groups based on any attribute sets Naïve Bayes Clearly shows the differences in a particular variable for various data elements Sequence Clustering Groups or clusters data based on a sequence of previous events Time Series Analyzes and forecasts time-based data combining the powerof ARTXP (developed by Microsoft Research) for short-term predictionswith ARIMA (in SQL 2008) for long-term accuracy. Neural Nets Seeks to uncover non-intuitive relationships in data Linear Regression Determines the relationship between columns in order to predict an outcome Logistic Regression Determines the relationship between columns in order to evaluate the probability that a column will contain a specific state

33. 33 Time Series Sequence Clustering Neural Nets Naïve Bayes Logistic Regression Linear Regression Decision Trees Clustering Association Rules Classification Estimation Segmentation Association Forecasting Text Analysis Advanced Data Exploration

34. 34 Summary Self-service analysis is now very powerful Works with huge data sets PowerPivot for columnar and multidimensional analysis Data Mining for pattern discover To start, all you need is PowerPivot, Excel 2010, and perhaps SQL Analysis Services

35. 35 © 2010 Microsoft Corporation & Project Botticelli Ltd. All rights reserved. The information herein is for informational purposes only and represents the opinions and views of Project Botticelli and/or Rafal Lukawiecki. The material presented is not certain and may vary based on several factors. Microsoft makes no warranties, express, implied or statutory, as to the information in this presentation. Portions © 2010 Project Botticelli Ltd & entire material © 2010 Microsoft Corp. Some slides contain quotations from copyrighted materials by other authors, as individually attributed or as already covered by Microsoft Copyright ownerships. 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 Project Botticelli Ltd as of the date of this presentation. Because Project Botticelli & Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft and Project Botticelli cannot guarantee the accuracy of any information provided after the date of this presentation. Project Botticelli makes no warranties, express, implied or statutory, as to the information in this presentation. E&OE.