Chapter 9 – Business Intelligence

1. Chapter 9 – Business Intelligence

2. Announcement • Thursday Night we will begin at 5:30

3. Why do organizations need BI?

4. Why do organizations need BI? • Tons of data out there! • In 2002, 2 exabytes were created • In 2008, 70 exabytes • 14x words spoken by human beings ever • Business Intelligence – information containing patterns, relationships, and trends • How do you get it out???? BI Systems

5. What BI Systems are available?

6. What BI Systems are available? • BI System – Information system that employs BI tools to produce and deliver information • Type of systems depend on tools in use • Categories of tools • Reporting - Simple • read, process, format, deliver • Used to assess results – What happened? • Data mining - Sophisticated • Searching for patterns or relationships • Used to make predictions – What will happen? • Knowledge management • Used to store employee knowledge and make it available to others • Source of data – humans • How do you handle what is happening?

7. Tools vs. Applications vs. Systems • Tool – one or more computer programs that implement the logic of a particular procedure • Example: Decision tree analysis • Application – use of a tool on a particular type of data for a particular purpose • Example: Assess risk for a loan to default • System – has all 5 components (hardware, software, data, people, procedures) delivering results of a BI application • Example: delivers results to loan officer who makes final decision

8. Reporting Applications • Reporting application inputs data from one or more sources and applies a reporting tool to that data to produce information. This is then delivered to users by reporting system. • Operations commonly used: • Sorting • Grouping • Calculating • Filtering • Formatting

9. Some Dashboards to see • http://dashboard.virginiadot.org/ • http://dashboard.imamuseum.org/ • http://buildingdashboard.com/clients/jmu/

10. Analytical Tools • RFM Analysis – ranks information according to purchasing behavior – gives customers an RFM Score (1 – 5, 1 being the top 20%) • How Recently? • How Frequently? • How much Money?

11. In Class Exercise • Review the data. • Sort the data • Split into 20% increments for R, F, and M • 1 for Most Recent, 5 for Least Recent • 1 for Most Frequent, 5 for Least Frequent • 1 for Most Money, 5 for Least Money • Assign scores to each customer

12. What would you do with each?

13. OLAP – Online Analytical Processing • More generic than RFM • Dynamic – viewer can change the format • Measures and Dimensions • Measures – data item of interest • Total sales, average sales, average cost, etc. • Dimension – characteristic of a measure • Purchase date, customer location, etc.

14. Example – An OLAP Cube or report • Users can alter the format • Possible to drill down into the data • Requirements • Computing power • Tools may be costly Measure Dimension

15. A Demo of a Tool • http://www.tableausoftware.com/products/tour

16. Data Mining • Statistical techniques to find patterns and relationships among data and use it for classification and prediction • Data mining techniques are a blend of statistics and mathematics, and artificial intelligence and machine-learning

17. What’s the difference between supervised and unsupervised data mining?

18. Supervised vs. Unsupervised data mining • Unsupervised data-mining characteristics: • No model or hypothesis exists before running the analysis • Analysts apply data-mining techniques and then observe the results • Analysts create a hypothesis after analysis is completed • Cluster analysis, a common technique in this category groups entities together that have similar characteristics • Supervised data-mining characteristics: • Analysts develop a model prior to their analysis • Apply statistical techniques to estimate parameters of a model • Regression analysis is a technique in this category that measures the impact of a set of variables on another variable • Neural networks predict values and make classifications

19. Market-Basket Analysis • Data mining tool for determining sales patterns • Helps businesses create cross-selling opportunities • Terms used with this type of analysis • Support—the probability that two items will be purchased together • Confidence—a conditional probability estimate • Lift – ratio of confidence to support • Complex, requires analytical tools

20. Market-Basket Example: Transactions = 400

21. Decision Trees • Hierarchical arrangement of criteria that predicts a classification or value • Unsupervised data-mining technique that selects the most useful attributes for classifying entities on some criterion • If…then rules

22. Example • Select attributes that are most useful for classifying • Predicting Grades for Students in COB 204 • What are some attributes/characteristics we should consider? How do businesses use decision trees?

23. College Admissions Decision TreeGroup Assignment – Ethics p.303

24. Data Warehouses and Data Marts • Address the problems companies have with missing data values and inconsistent data • Help standardize data formats between operational data and data purchased from third-party vendors • Prepare, store, and manage data specifically for data mining and analyses.

25. Problems with Operational data

26. The Curse of Dimensionality • The more attributes there are, the easier it is to build a model that is worthless

27. Data Marts vs. Data Warehouses • Data mart is smaller than a warehouse • Data mart addresses a particular component or function

28. Knowledge Management Applications • KM – process of creating value from intellectual capital and sharing with others who need it • Data mining and reporting create new information • KM shares known information

29. What are the benefits of KM?

30. Benefits of KM • Fosters innovation – free flow of ideas • Improves customer service – faster response time • Boosts revenues – get product to market faster • Enhances retention – recognize/reward knowledge • Streamlines operations – eliminates/reduces redundant or unnecessary operations • Preserves organizational memory

31. Sharing Document Content • Indexing • Need to be able to easily access information • Need keyword searchability • Need quick response • RSS – Real simple syndication • Think of it as an email system for content • Subscribe to magazines, blogs, websites, and other sources – RSS Feeds

32. Example

33. Expert Systems • Rule based systems using if…then logic • Created by interviewing experts and codifying their decisioning (vs. decision trees that review past data and performance) • Can have hundreds of thousands of rules (vs. <12 in decision trees)

34. Expert System Problems • Difficult and expensive to manage • Difficult to maintain • Implications of rule changes • Difficult to perform at same level as real experts • Example - medicine

35. How are BI applications delivered?

36. Delivery of Business Intelligence Applications

37. Mgt Functions of BI Servers • Maintains metadata about the authorized allocation of BI results to users • Tracks what results are available, who is authorized to view them, and when the results are provided to users • Options for managing results • Users can pull their results from a Web site using a portal server with a customizable user interface • A server can automatically push information to users through alerts which are messages announcing events as they occur • Portal servers – allow for customization of the interface • A report server, a special server dedicated to reports, can supply users with information.

38. Delivery Functions • Characteristics of the delivery function of a BI server: • Tracks authorized users. • Tracks the schedule for providing results to users. • Uses exception alerts that notify users of an exceptional event. • Procedures used depends on the nature of the BI system. • Procedures tend to be more flexible than those in an operational system because users of a BI system tend to be engaged in work that is neither structured nor routine. • Procedures are determined by unique requirements of users.