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Business Intelligence Systems

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1 Business Intelligence Systems
Chapter 9 Business Intelligence Systems This chapter considers BI systems: information systems that identify patterns, relationships, and other information from organizational structured and unstructured social data, external and purchased data.

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 GOALS: Use the PRIDE system to: Illustrate a practical application for business intelligence systems, specifically reporting. Show the use of animation for reporting on a mobile device. Provide a setting to teach standard reporting terminology. Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

3 Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall
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: 2023? Chapter begins by summarizing reasons organizations use business intelligence. Then, describes three basic activities in business intelligence process and illustrates those activities using GearUp. Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

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

5 Example Uses of Business Intelligence
Note hierarchical nature of tasks. Business intelligence used for all four of collaborative tasks. Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

6 Q2: What Are the Three Primary Activities in the BI Process?
> Publish results: Push publishing delivers BI according to a schedule, or due to an event or particular condition without any request from users. Pull publishing requires users to request BI results. Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

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

8 Tables Used for BI Analysis at GearUp
Top section shows three of tables in GearUp’s operational database used to produce the data extract. Lucas uses these data to create Item_Shipped, Item_Not_ Shipped, and Quantity_Received tables. Addison summed quantities from tables to create Item_Summary_Data table. Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

9 Extract of ITEM_SUMMARY_DATA Table
To determine orders lost to damage and those lost to cancellations, GearUp indirectly computes TotalCancelled. Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

10 Lost Sales Summary Report
To determine extent of sales lost due to short shipments or damage, Addison created an Access report to sum data from Item_Summary_Data table From this report, vendors 5000 and 2000 have never had a shortage or quality problem. Vendor 4000 has a modest problem, vendors 1000 and 3000 have caused numerous lost sales, due to shortages or damaged goods. 55.5% of sales of vendor 3000’s items have been lost (19,450/35,000). Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

11 Lost Sales Details Report
Items shown by EventItemNumber, not by item name, event date, and event date Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

12 Event Data Spreadsheet
With a worksheet in tabular format, it would be easy to import this data from Excel to Access. Someone must put it into tabular format or extract the data and enter it manually. Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

13 Short and Damaged Shipments Summary
All vendor 1000 problems are caused by damage. Vendor 1000 always shipped appropriate number. Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

14 Short and Damaged Shipments Details Report
Report shows vendor 1000 has persistent damage problems and vendor 3000's shipment are short. Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

15 Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall
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 These options discussed in detail in Q5. GearUp would choose among these alternatives according to its needs. Most likely, they will print results and or share via a collaboration tool. Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

16 Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall
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 IS professionals do not want business analysts processing operational data, because if they make an error it could severely disrupt operations. Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

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

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

20 Possible Problems with Source Data
Most operational and purchased data have problems that inhibit usefulness for business intelligence. Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

21 Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall
Data Marts Examples A data mart is a subset of a data warehouse. It addresses a particular component or functional area. Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

22 Q4: How Do Organizations Use Reporting Applications?
Create meaningful information from disparate data sources Deliver information to user on time Basic operations: Sorting   Filtering Grouping   Calculating Formatting Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

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

24 RFM Analysis Classifies Customers
To produce an RFM score, sort customer purchase records by date from most recent (R) purchase, divide into quintiles, and assign a score from 1 to 5 for each customer by quintile. Repeat for Frequently and Money. Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

25 OLAP Product Family by Store Type
Typical OLAP Report Dimensions Product Family and Store Type Report shows how net store sales vary by product family and store type. OLAP Product Family by Store Type Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

26 OLAP Product Family and Store Location by Store Type
User added dimensions Store (Country) and State. Product-family sales broken out by location of stores. Sample data include only stores in US western states of California, Oregon, and Washington. Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

27 Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall
OLAP Product Family and Store Location by Store Type, Showing Sales Data for Four Cities User drilled down into stores located in California. Report shows sales data for four cities in California that have stores. User also changed the order of the dimensions. Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

28 Q5: How Do Organizations Use Data Mining Applications?
Source disciplines of data mining Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

29 Unsupervised Data Mining
Analyst does not create a priori hypothesis or model Hypotheses created afterward to explain patterns found Example: Cluster analysis Cluster analysis: Statistical technique to identify groups of entities with similar characteristics; used to find groups of similar customers from customer order and demographic data Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

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 = 521.7 Predict number of minutes of weekend cell phone use. Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

31 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. Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

32 Market-Basket Example: Dive Shop Transactions = 400
Hypothetical sales data First row of numbers under each column is total number of times an item sold. For example, 270 in third row under Mask means that 270 of the 400 (.67) transactions included masks. 280 under Fins means that 280 of 400 (.700) transactions included fins. Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

33 Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall
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” Basic idea of a decision tree is to select attributes most useful for classifying entities. Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

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

35 Ethics Guide: The Ethics of Classification
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 GOAL Explore difficult ethical issues about using decision trees for classifying people. Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

36 The Ethics of Classification: Resulting Decision Tree
Classifying people can raise serious ethical issues because: Statistically valid measures without human judgment involved Important data might not be included Results could reinforce social stereotypes Might not be organizationally or socially feasible, or legal Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

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 MapReduce Processing Summary
Technique for harnessing power of thousands of computers working in parallel Google search logs broken into pieces Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

39 Google Trends on the Term Web 2.0
This trend line supports contention that “Web 2.0” is fading from use. Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

40 Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall
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 Using MIS InClass 9: What Wonder Have We Wrought?
Data aggregator is a company that obtains data from public and private sources, and stores, combines, publishes it in sophisticated ways. See Instructor’s Manual for example answers to questions. Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

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 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 What Are 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 Expert systems are rule-based systems that encode human knowledge as If/Then rules. Expert systems shells – programs that process a set of rules Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

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

46 Drawbacks of Expert Systems
Difficult and expensive to develop Labor intensive Ties up domain experts Difficult to maintain Changes cause unpredictable outcomes Constantly need expensive changes Don’t live up to expectations Can’t duplicate diagnostic abilities of humans Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

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 Content management system functions are huge and complex. Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

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 How Do Hyper-Social Organizations Manage Knowledge?
Hyper- Social KM Media Social media, and related applications, for management and delivery of organizational knowledge resources Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

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 Q8: What Are the Alternatives for Publishing BI?
Table lists four server alternatives for BI publishing. Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

52 What Are the Two Functions of a BI Server?
Components of a Generic Business Intelligence System Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

53 Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall
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 Guide: Semantic Security
Unauthorized access to protected data and information Physical security Passwords and permissions Delivery system must be secure Unintended release of protected information through reports & documents What, if anything, can be done to prevent what Megan did? GOALS Discuss trade-off between information availability and security. Introduce, explain, and discuss ways to respond to semantic security. Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

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 GOALS Teach real-world issues and limitations of data mining. Investigate ethics of working on projects of doubtful or harmful utility to sponsoring organization. Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

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

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 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 Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall
FireFox Collusion FireFox has an optional feature called Collusion that tracks and graphs all cookies on your computer. Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

60 Ghostery in Use (ghostery.com)
Who are these companies that are gathering my browser behavior data? You can find out using ghostery, another useful browser add-in feature (www.ghostery.com). Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

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


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