Chapter 9. Business Intelligence Systems

Transcription

1 Chapter 9 Business Intelligence Systems

2 We Can Make the Bits Produce Any Report You Want, But You ve Got to Pay for It. Need to monitor patient workout data. Spending too many hours each day looking at patient workout data. Great use for exception reporting. Animation & new types of reporting creates innovative and motivating reports. Eliminating silos enables everyone to gain more information from PRIDE data. 9-2

3 Study Questions Q1: How do organizations use business intelligence (BI) systems? Q2: What are the three primary activities in the BI process? Q3: How do organizations use data warehouses and data marts to acquire data? Q4: How do organizations use reporting applications? Q5: How do organizations use data mining applications? Q6: How do organizations use BigData applications? Q7: What is the role of knowledge management systems? Q8: What are the alternatives for publishing BI? Q9: 2024? 9-3

4 Q1: How Do Organizations Use Business Intelligence (BI) Systems? Components of Business Intelligence System 9-4

5 Example Uses of Business Intelligence 9-5

6 What Are Typical Uses for BI? Identifying changes in purchasing patterns Important life events cause customers to change what they buy. BI for entertainment Netflix has data on watching, listening, and rental habits, however, determines what people actually want, not what they say. Predictive policing Analyze data on past crimes, including location, date, time, day of week, type of crime, and related data, to predict where crimes are likely to occur. 9-6

7 Q2: What Are the Three Primary Activities in the BI Process? 9-7

8 Using Business Intelligence to Find Candidate Parts at AllRoad Identified criteria for parts customers might want to print themselves. Provided by vendors who already agree to make part design files available for sale. Purchased by larger customers. Frequently ordered parts. Ordered in small quantities. Simple in design. 9-8

9 Acquire Data: Extracted Order Data 9-9

10 Extracted Part Data 9-10

11 Analyze Data: Access Query 9-11

12 Query Result 9-12

13 Joining Order Extract and Filtered Parts Tables 9-13

14 Sample Orders and Parts View Data 9-14

15 Customer Summary 9-15

16 Qualifying Parts Query Design 9-16

17 Qualifying Parts Query Results Figure 9-17

18 Publish Results: Sales History for Selected Parts 9-18

19 Q3: How Do Organizations Use Data Warehouses and Data Marts to Acquire Data? Functions of a Data Warehouse Extract data from operational, internal and external databases. Cleanse data. Organize, relate data warehouse. Catalog data using metadata. 9-19

20 Components of a Data Warehouse 9-20

21 Examples of Consumer Data That Can Be Purchased 9-21

22 Possible Problems with Source Data Curse of dimensionality 9-22

23 Data Warehouses Versus Data Marts 9-23

24 Q4: How Do Organizations Use Reporting Applications? Create meaningful information from disparate data sources. Deliver information to user on time. Basic operations: 1. Sorting 2. Filtering 3. Grouping 4. Calculating 5. Formatting 9-24

25 How Does RFM Analysis Classify Customers? Recently Frequently Money 9-25

26 RFM Analysis Classifies Customers 9-26

27 Typical OLAP Report OLAP Product Family by Store Type 9-27

28 Example of Expanded Grocery Sales OLAP Report Drill down into the data 9-28

29 OLAP Product Family and Store Location by Store Type, Showing Sales Data for Four Cities 9-29

30 Q5: How Do Organizations Use Data Mining Applications? 9-30

31 Unsupervised Data Mining Analyst does not start with a priori hypothesis or model. Hypothesized model created based on analytical results to explain patterns found. Example: Cluster analysis. 9-31

32 Supervised Data Mining Uses a priori model to compute outcome of model Prediction, such as regression analysis Ex: CellPhoneWeekendMinutes = (12 + (17.5*CustomerAge)+(23.7*NumberMonthsOfAccount) = * *6 =

33 Market-Basket Analysis Market-basket analysis a data-mining technique for determining sales patterns. Statistical methods to identify sales patterns in large volumes of data. Products customers tend to buy together. Probabilities of customer purchases. Identify cross-selling opportunities. Customers who bought fins also bought a mask. 9-33

34 Market-Basket Example: Dive Shop Transactions =

35 Decision Trees Hierarchical arrangement of criteria to predict a classification or value. Unsupervised data mining technique. Basic idea of a decision tree Select attributes most useful for classifying something on some criteria to create pure groups. 9-35

36 Credit Score Decision Tree 9-36

37 Decision Rules for Accepting or Rejecting Offer to Purchase Loans If percent past due is less than 50 percent, then accept loan. If percent past due is greater than 50 percent and If CreditScore is greater than and If CurrentLTV is less than.94, then accept loan. Otherwise, reject loan. 9-37

38 Using MIS InClass Exercise 9: What Singularity Have We Wrought? Trends in the Computing Industry 9-38

39 Q6: How Do Organizations Use BigData Applications? Huge volume petabyte and larger. Rapid velocity generated rapidly. Great variety Structured data, free-form text, log files, possibly graphics, audio, and video. 9-39

40 MapReduce Processing Summary Google search log broken into pieces 9-40

41 Google Trends on the Term Web

42 Hadoop Open-source program supported by Apache Foundation2. Manages thousands of computers. Implements MapReduce Written in Java Amazon.com supports Hadoop as part of EC3 cloud offering. Query language entitled Pig. 9-42

43 Q7: What Is the Role of Knowledge Management Systems? Knowledge Management Creating value from intellectual capital and sharing that knowledge with those who need that capital. Preserving organizational memory by capturing and storing lessons learned and best practices of key employees. 9-43

44 Benefits of Knowledge Management Improve process quality. Increase team strength. Goal: Enable employees to use organization s collective knowledge. 9-44

45 What Are Expert Systems? Expert systems Expert systems shells Rule-based IF/THEN Encode human knowledge Process IF side of rules Report values of all variables Knowledge gathered from human experts 9-45

46 Example of IF/THEN Rules 9-46

47 Drawbacks of Expert Systems 1. Difficult and expensive to develop Labor intensive Ties up domain experts 2. Difficult to maintain Changes cause unpredictable outcomes Constantly need expensive changes 3. Don t live up to expectations Can t duplicate diagnostic abilities of humans 9-47

48 What Are Content Management Systems (CMS)? Support management and delivery of documents, other expressions of employee knowledge Challenges of Content Management Databases are huge Content dynamic Documents do not exist in isolation Contents are perishable In many languages 9-48

49 What are CMS Application Alternatives? In-house custom Customer support department develops in-house database applications to track customer problems Off-the-shelf Horizontal market products (SharePoint) Vertical market applications Public search engine Google 9-49

50 How Do Hyper-Social Organizations Manage Knowledge? Hyper-social knowledge management Application of social media and related applications for management and delivery of organizational knowledge resources. Hyper-organization theory Framework for understanding this new direction in KM. Focus moves from knowledge and content per se to fostering authentic relationships among creators and users of knowledge. 9-50

51 Hyper-Social KM Alternative Media 9-51

52 Q8: What Are the Alternatives for Publishing BI? 9-52

53 Elements of a BI System 9-53

54 Q9: 2024? World generating and storing exponentially more information. Information about customers, and data mining techniques going to get better. Companies will know more about your purchasing habits and psyche. Social singularity Machines will build their own information systems. Will machines possess and create information for themselves? 9-54

55 Guide: Semantic Security 1. Unauthorized access to protected data and information Physical security Passwords and permissions Delivery system must be secure 2. Unintended release of protected information through reports and documents. 3. What, if anything, can be done to prevent what Megan did? 9-55

56 Guide: Data Mining in the Real World Problems: Dirty data Missing values Lack of knowledge at start of project Over fitting Probabilistic Seasonality High risk unknown outcome 9-56

57 Active Review Q1: How do organizations use business intelligence (BI) systems? Q2: What are the three primary activities in the BI process? Q3: How do organizations use data warehouses and data marts to acquire data? Q4: How do organizations use reporting applications? Q5: How do organizations use data mining applications? Q6: How do organizations use BigData applications? Q7: What is the role of knowledge management systems? Q8: What are the alternatives for publishing BI? Q9: 2024? 9-57

58 Case Study 9: Hadoop the Cookie Cutter Third-party cookie created by a site other than one you visited. Generated in several ways, most common occurs when a Web page includes content from multiple sources. DoubleClick IP address where content was delivered. Records data in cookie log. 9-58

59 Case Study 9: Hadoop the Cookie Cutter (cont'd) Third-party cookie owner has history of what was shown, what ads clicked, and intervals between interactions. Cookie log contains data to show how you respond to ads and your pattern of visiting various Web sites where ads placed. 9-59

60 FireFox Collusion 9-60

61 Ghostery in Use (ghostery.com) 9-61

62 9-62