SQL Server 2008 for Business Intelligence

1. SQL Server 2008 for Business Intelligence UTS Short Course

2. Recap • Other cube browsers • Microsoft Data Analyzer • Proclarity • Excel 2003/2007/2010 • Excel services • Thinslicer • Performance Point • Power Pivot

3. The plan

4. Step by step to BI • Create Data Warehouse • Copy data to data warehouse • Create OLAP Cubes • Create Reports • Browse the cube • Do some Data Mining • Discovering relationships • Predict future events

5. Agenda • What is Data Mining? • Why? • Uses • Algorithms • Demo • Hands on Lab

6. What is Data Mining? “Data mining is the use of powerful software tools to discover significant traits or relationships,from databases or data warehouses and often used to predict future events”

7. What is Data Mining? • It exploits statistical algorithms • Once the “knowledge” is extracted it: • Can be used to discover • Can be used to predict values of other cases

8. Why Data Mining? • Marketing • Who picks the movie? The kids, the wife, me • Who are our Customers and what sort of films do they hire? • Is a 30 year old woman with 2 children going to hire Arnie’s latest film • Validation • Is this data sensible? Terminator 2 and Toy Story • Prediction • Sales Next Year

9. Why? Its all about money • Get new information from data, future trends, past trends, outlier, maximums, minimums • Analyse data from different perspectives and summarizing it into useful information • New information to • increase revenue • cuts costs • or both :-)

10. Which Questions are Data Mining? • Who are our biggest customers? • What are customers buying with cigars? • What are the customer retention levels of our branches? • Which customers have bought olives, feta cheese but no ciabatta bread? • Which regions have the highest male/female ratio of single 20 somethings? • Which region has lowest customer retention levels and list out lost customers?

11. What’s not data mining • Ad hoc query • Drill through to details • Business Intelligence tool

12. Data - Uncover patterns in samples • Huge amount of data • Good raw material  good data mining • Samples should be representative • Samples "similar" to domain • Not all-seeing crystal ball • Verify and Validate!

13. OLAP versus Data Mining • OLAP • Is about fast ad hoc querying • Analysis by dimensions and measures • Gives precise answers • Data Mining • May use RDBMS or OLAP source • Is about discovering and predicting • Gives imprecise answers • OLAP is not a prerequisite for data mining, but it almost always comes first (learning to ride a bike before a car)

14. Types of Data Mining Algorithms • Classification algorithms • predictone or more discrete variables, based on the other attributes in the dataset • Regression algorithms • predictone or more continuous variables, such as profit or loss, based on other attributes in the dataset • Segmentation algorithms • dividedata into groups, or clusters, of items that have similar properties • Association algorithms • find correlations between different attributes in a dataset • Sequence analysis algorithms • summarize frequent sequences or episodes in data, such as a Web path flow

15. Complete Set Of AlgorithmsWays to analyze your data Clustering Time Series Decision Trees Naïve Bayes Association Linear Regression Neural Network Sequence Clustering Logistic Regression

16. Decision trees • Split data • Each of branch is like an attribute • Brightness = amount of data

17. Decision Trees (1) • Decision Trees assign (classify) each case to one of a few (discrete) broad categories of selected attribute (variable) and explains the classification with few selected input variables • The process of building is recursive partitioning – splitting data into partitions and then splitting it up more • Initially all cases are in one big box

18. Decision Trees (2) • The algorithm tries all possible breaks in classes using all possible values of each input attribute; it then selects the split that partitions data to the purest classes of the searched variable • Several measures of purity • Then it repeats splitting for each new class • Again testing all possible breaks • Unuseful branches of the tree can be pre-pruned or post-pruned

19. Decision Trees (3) • Decision trees are used for classification and prediction • Typical questions: • Predict which customers will leave • Help in mailing and promotion campaigns • Explain reasons for a decision • What are the movies young female customers like to buy?

20. Decision Trees – Who Decides

21. Naïve Bayes • Bayes Formula • Uses statistics to say falls into certain category or not with probability • Spam filtering: score of spam (Bayes) • Testing only a particular attribute

22. Naïve Bayes • Quickly builds mining models that can be used for classification and prediction • It calculates probabilities for each possible state of the input attribute, given each state of the predictable attribute • This can later be used to predict an outcome of the predicted attribute based on the known input attributes • This makes the model a good option for exploring the data

23. Cluster Analysis (1) • Grouping data into clusters • Objects within a cluster have high similarity based on the attribute values • The class label of each object is not known • Several techniques • Partitioning methods • Hierarchical methods • Density based methods • Model based methods • And more…

24. Cluster Analysis (2) • Segments a heterogeneous population into a number of more homogenous subgroups or clusters • Some typical questions: • Discover distinct groups of customers • Identification of groups of houses in a city • In biology, derive animal and plant taxonomies • Find outliers

25. Clustering Annual Income Age

26. Time series • Timebaseddata  prediction

27. Sequence clustering • Numbers orders stronger associations • Direction of association (not necessary the other direction)

28. Association • If you own certain stocks ' you own maybe other ones as well • Probability = thickness of line

29. Neural Nets • Let system learn how to classify data • Neural Network adapts to the new data • Formulate statement/hypothesis • Outcome is know • (Data / Surveys) • 1. 70% data to train network (outcome is known) • 2. 30% of data to test network (outcome is known) • 3. New data (no survey needed, predict from network) • Other example: OCR

30. Conclusion: When To Use What

31. There is more... Visual Numerics • 3rd party algorithms http://www.vni.com/company/whitepapers/ MicrosoftBIwithNumericalLibraries.pdf

32. Excel Data Mining • Microsoft SQL Server 2008 Data Mining Add-ins for Microsoft Office 2007 • http://www.microsoft.com/downloads/en/details.aspx?familyid=896A493A-2502-4795-94AE-E00632BA6DE7&displaylang=en

33. Other usages of data miningFind patterns - Profiling • Train station / airport • Who is the bad guy • Farmers • Find the best crops • Supermarket • Find to figure out how to get you to buy more, where the expensive items

34. Tip • SSIS 2008 - Data profiling task • Get a profile of the data in a table • potential candidate keys • length of data values in columns • Null percentage of rows • distribution of values • ....

35. Resources 1 • Video: Simple data mining model http://www.sqlservercentral.com/articles/Video/65055/ • Video: Data mining and Reporting Services http://www.sqlservercentral.com/articles/Video/64190/ • Data Mining Algorithms http://msdn.microsoft.com/en-us/library/ms175595.aspx

36. Resources 2 • Jamie MacLennan http://blogs.msdn.com/b/jamiemac/ • Richard Lees on BI http://richardlees.blogspot.com/ Book Data Mining with Microsoft SQL Server 2008 http://www.amazon.com/gp/product/0470277742?ie=UTF8&tag=sqlserverda09-20&linkCode=as2&camp=1789&creative=9325&creativeASIN=0470277742

37. Summary • Why Data Mining? • Uses • Algorithms • Demo • Hands on Lab

38. 3things… • EricPhan@ssw.com.au • http://ericphan.info/ • twitter.com/ericphan

39. Thank You! Gateway Court Suite 10 81 - 91 Military Road Neutral Bay, Sydney NSW 2089 AUSTRALIA ABN: 21 069 371 900 Phone: + 61 2 9953 3000 Fax: + 61 2 9953 3105 info@ssw.com.auwww.ssw.com.au