1. 1 1 The Analyst's Perspective: Advanced BI with PowerPivot DAX, SharePoint Dashboards, and SQL Data Mining Rafal Lukawiecki Strategic Consultant, Project Botticelli Ltd rafal@projectbotticelli.com

2. 2 2 Objectives Introduce more advanced BI analytics from Microsoft Discuss using SharePoint 2010 as a BI Dashboard environment 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 DAX

4. 4 4 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

5. 5 5 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

6. 6 6 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) 6

7. 7 7 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

8. 8 8 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])

9. 9 1. Simplicity of DAX to Relate and Analyse Data

10. 10 SharePoint 2010 BI Dashboards: PerformancePoint Services

11. 11 PPS in SharePoint 2010 PerformancePoint Services in SharePoint 2010 improve over PerformancePoint Server 2007: SharePoint does all security, management, backup, respository of dashboard Decomposition Tree KPI Details Scorecard drilldown, dynamic hierarchies, calculated KPIs Dynamic, up-to-date filters for time intelligence SharePoint Dashboard Designer is smoother Better accessibility Analytic charts with value filtering and server-based conditional formatting

12. 12 Monitoring with PPS Business users can build performance dashboards easily

13. 13 Analytics with PPS Integration of KPIs and analytics Multidimensional slice and dice, drill-across, drill-to-detail, root-cause analysis, prediction and centralized business logic definitions No coding

14. 14 Reporting and Consolidation in PPS Combine operational and financial data into one report No need to reconsolidate manually Dynamic and standard reports Consistent live reports published from Excel to Reporting Services and SharePoint

15. 15 Dashboard Designer Details pane Workspace Browser Workspace

16. 16 Developing a Dashboard Choose a dashboard layout Assign elements to a dashboard zone Add filters Preview the dashboard Deploy to SharePoint

17. 17 1. Building a Dashboard, Scorecard, and a KPI Using SharePoint Server PerformancePoint Services

18. 18 Visualising BI with Microsoft Visio and SharePoint 2010

19. 19 Two Trends that Lead to… The Messy Diagram

20. 20 Data Visualization Fault Analysis Tree Status Indicators Color By Value Text Callouts Data Bars

21. 21 Data Visualization Manufacturing Specialized Shapes

22. 22 Strategy Maps Visualize PPS Scorecard data in context

23. 23 Data Mining with SQL Server

24. 24 What does Data Mining Do? Explores Your Data Finds Patterns Performs Predictions

25. 25 Typical Uses

26. 26 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

27. 27 Mining Model Mining Process DM Engine Training data Data to be predicted Mining Model With predictions

28. 28 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

29. 29 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

30. 30 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

31. 31 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

32. 32 Time Series Uses: Forecast sales Inventory prediction Web hits prediction Stock value estimation Regression trees with extras

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 1. Association Rules for Market Basket Analysis 2. Automatic recommendation engine using DMX queries

35. 35 Summary Advanced self-service analysis requires a rich expression language: DAX Team, and organisational BI dashboards and scorecards are easy to build using SharePoint 2010 Data Mining enables advanced pattern (correlation) discovery in your data

36. 36 © 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.