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Business Intelligence Systems Chapter 9. 9-2 “We Can Produce Any Report You Want, But You’ve Got to Pay for It.” Different expectations about what is.

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Presentation on theme: "Business Intelligence Systems Chapter 9. 9-2 “We Can Produce Any Report You Want, But You’ve Got to Pay for It.” Different expectations about what is."— Presentation transcript:

1 Business Intelligence Systems Chapter 9

2 9-2 “We Can Produce Any Report You Want, But You’ve Got to Pay for It.” Different expectations about what is a report Great use for exception reporting Feature PRIDE prototype and supporting data are stored in profile, profileworkout, and equipment tables Need legal advice on system Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

3 9-3 Study Questions Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall 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: 2023?

4 9-4 Q1: How Do Organizations Use Business Intelligence (BI) Systems? Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

5 9-5 Example Uses of Business Intelligence Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

6 9-6 Q2: What Are the Three Primary Activities in the BI Process? Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

7 9-7 Using BI for Problem-solving at GearUp: Process and Potential Problems Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall 1.Obtain commitment from vendor 2.Run sales event 3.Sell as many items as it can 4.Order amount actually sold 5.Receive partial order and damaged items 6.If receive less than ordered, ship partial order to customers 7.Some customers cancel orders

8 9-8 Tables Used for BI Analysis at GearUp Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

9 9-9 Extract of ITEM_SUMMARY_DATA Table Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

10 9-10 Lost Sales Summary Report Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

11 9-11 Lost Sales Details Report Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

12 9-12 Event Data Spreadsheet Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

13 9-13 Short and Damaged Shipments Summary Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

14 9-14 Short and Damaged Shipments Details Report Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

15 9-15 Publish Results Options Print and distribute via or collaboration tool Publish on web server or SharePoint Publish on a BI server Automate results via web service Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

16 9-16 Q3: How Do Organizations Use Data Warehouses and Data Marts to Acquire Data? Why extract operational data for BI processing?  Security and control  Operational not structured for BI analysis  BI analysis degrades operational server performance Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

17 9-17 Functions of a Data Warehouse Obtain or extract data Cleanse data Organize and relate data Create and maintain catalog Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

18 9-18 Components of a Data Warehouse Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

19 9-19 Examples of Consumer Data that Can Be Purchased Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

20 9-20 Possible Problems with Source Data Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

21 9-21 Data Marts Examples Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

22 9-22 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 Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

23 9-23 How Does RFM Analysis Classify Customers? Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall Recently Frequently Money

24 9-24 RFM Analysis Classifies Customers Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

25 9-25 Typical OLAP Report Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall OLAP Product Family by Store Type

26 9-26 OLAP Product Family and Store Location by Store Type Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

27 9-27 OLAP Product Family and Store Location by Store Type, Showing Sales Data for Four Cities Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

28 9-28 Q5: How Do Organizations Use Data Mining Applications? Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

29 9-29 Unsupervised Data Mining Analyst does not create a priori hypothesis or model Hypotheses created afterward to explain patterns found Example: Cluster analysis Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

30 9-30 Supervised Data Mining Develop a priori model to compute estimated parameters of model Used for prediction, such as regression analysis Ex: CellPhoneWeekendMinutes = (12 + (17.5 X CustomerAge) + (23.7 X NumberMonthsOfAccount) = * *6 = Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

31 9-31 Market-Basket Analysis Market-basket analysis – a data-mining technique for determining sales patternsMarket-basket analysis –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. Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

32 9-32 Market-Basket Example: Dive Shop Transactions = 400 Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

33 9-33 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” Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

34 9-34 Credit Score Decision Tree Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

35 9-35 Ethics Guide: The Ethics of Classification Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall Classifying applicants for college admission Collects demographics and performance data of all its students Uses decision tree program Statistically valid measures to obtain statistically valid results No human judgment involved

36 9-36 The Ethics of Classification: Resulting Decision Tree Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

37 9-37 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 Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

38 9-38 MapReduce Processing Summary Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall Google search logs broken into pieces

39 9-39 Google Trends on the Term Web 2.0 Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

40 9-40 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 Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

41 9-41 Using MIS InClass 9: What Wonder Have We Wrought? Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

42 9-42 Q7: What Is the Role of Knowledge Management Systems? 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 Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

43 9-43 Benefits of Knowledge Management Improve process quality Increase team strength Goal: Enable employees to use organization’s collective knowledge Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

44 9-44 What Are Expert Systems? Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall Expert systems Rule-based IF/THEN Encode human knowledge Process IF side of rules Report values of all variables Knowledge gathered from human experts Expert systems shells

45 9-45 Example of IF/THEN Rules Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

46 9-46 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 Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

47 9-47 What Are Content Management Systems (CMS)? Support management and delivery of documents, other expressions of employee knowledge Challenges –Databases are huge –Content dynamic –Documents do not exist in isolation –Contents are perishable –In many languages Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

48 9-48 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 Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

49 9-49 How Do Hyper-Social Organizations Manage Knowledge? Hyper- Social KM Media Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

50 9-50 Resistance to Hyper-Social Knowledge- Sharing Reluctance to exhibit ignorance Employee competition Solution –Strong management endorsement –Strong positive feedback and rewards Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

51 9-51 Q8: What Are the Alternatives for Publishing BI? Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

52 9-52 What Are the Two Functions of a BI Server? Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

53 9-53 Q9: 2023? 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? Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

54 9-54 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 & documents 3.What, if anything, can be done to prevent what Megan did? Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

55 9-55 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 Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

56 9-56 Active Review Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall 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: 2023?

57 9-57 Case Study 9: Hadoop the Cookie Cutter Third-party cookie created by site other than one you visited Generated in several ways, mostly occurs when a Web page includes content from multiple sources DoubleClick –IP address where content was delivered –Records data in a log Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

58 9-58 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 Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

59 9-59 FireFox Collusion Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

60 9-60 Ghostery in Use (ghostery.com) Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

61 9-61 Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall


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