1. Business Intelligence Systems
Chapter 9
Business Intelligence Systems
This chapter considers business intelligence (BI) systems: information systems that can produce patterns, relationships, and other information from organizational structured and unstructured social data as well as from external, purchased data.

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“Data Analysis, Where You Don’t Know the Second Question to Ask Until You See the Answer to the First One.”
Tracking race competitors from each of event, and having unbelievable success selling products to them.
Want to match competitors to personal trainers in same locale.
Earn referral fee.
How to track them? Mailing address? IP address?
Got data and Excel to start.
Serious data mining needs a data mart.
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.
Illustrate advantages of storing data in the cloud.
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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: 2026?
This chapter considers BI systems to identify patterns, relationships, and other information in organizational structured and unstructured social data, as well as purchased external data. In addition to this data, another rich source of knowledge is employees themselves.
Vast amounts of collective knowledge exist in every organization’s employees. How can that knowledge be shared?
As you will learn, business intelligence is the key technology supporting such marketing technology.
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4. Q1: How Do Organizations Use Business Intelligence (BI) Systems?
Components of Business Intelligence System
BI systems are information systems that process operational and other data to identify patterns, relationships, and trends for use by business professionals and other knowledge workers.
Five standard IS components are present in BI systems: hardware, software, data, procedures, and people.
The boundaries of BI systems are blurry.
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5. How Do Organizations Use BI?
Use BI for all four of the collaborative tasks described in Chapter 2.
Falcon Security could use BI to determine whether it could save costs by rerouting its drone flights.
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6. What Are Typical Uses for BI?
Identifying changes in purchasing patterns
Important life events change what customers buy.
Entertainment
Netflix has data on watching, listening, and rental habits.
Classify customers by viewing patterns.
Predictive policing
Analyze data on past crimes - location, date, time, day of week, type of crime, and related data.
Typical uses involve classification or prediction.
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7. Just-in-Time Medical Reporting
Example of real time data mining and reporting.
Injection notification services
Software analyzes patient’s records, if injections needed, recommends as exam progresses.
Blurry edge of medical ethics.
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8. Q2: What Are the Three Primary Activities in the BI Process?
These activities directly correspond to the BI elements in Figure 9-1.
The four fundamental categories of BI analysis are reporting, data mining, BigData, and knowledge management.
Push publishing delivers business intelligence to users without any request from the users; the BI results are delivered according to a schedule or as a result of an event or particular data condition. Pull publishing requires the user to request BI results.
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9. Using Business Intelligence to Find Candidate Parts at Falcon Security
Identify parts that might qualify.
Provided by vendors who make part design files available for sale.
Purchased by larger customers.
Frequently ordered parts.
Ordered in small quantities.
Used part weight and price surrogates for simplicity.
Obtained an extract of sales data from its IS department and stored it in Microsoft Access.
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10. Acquire Data: Extracted Order Data
Query
Sales (CustomerName, Contact, Title, Bill Year, Number Orders, Units, Revenue, Source, PartNumber)
Part (PartNumber, Shipping Weight, Vendor)
IS department extracted the data.
Actually wouldn’t need all of the data columns in the Sales table.
Data was divided into different billing years, which wouldn’t affect analysis.
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11. Sample Extracted Data: Part Data Table
Actually wouldn’t need all of the data columns in the Sales table.
Data was divided into different billing years, that division wouldn’t affect analysis.
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Analyze Data
First step was to combine the data in the two tables into a single table that contained both the sales and part data.
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13. Sample Orders and Parts View Data
Data in Figure 9-6 has been filtered for their first criterion, to consider parts only from particular vendors.
Notice some missing and questionable values. Numerous rows have missing values of Contact and Title, and a few rows have value of zero for Units. Missing contact and title data isn’t a problem.
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14. Creating Customer Summary Query
Sums the revenue, units, and average price for each customer.
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Customer Summary
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16. Qualifying Parts Query Design
Filtered by customers having more than $200,000 in total revenue.
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17. Publish Results: Qualifying Parts Query Results
Publish results is the last activity in the BI process.
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18. Publish Results: Sales History for Selected Parts
Judging just by the results, there seems to be little revenue potential in selling designs for these parts.
It is possible they chose the wrong criteria.
Might find themselves changing criteria until they obtain a result they want, which results in a very biased study.
Importance of the human component of an IS. Business intelligence is only as intelligent as the people creating it!
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19. Ethics Guide: Unseen Cyberazzi
Data broker or Data aggregator
Acquires and purchases consumer and other data from public records, retailers, Internet cookie vendors, social media trackers, and other sources.
Data for business intelligence to sell to companies and governments.
GOAL
Sensitize students to the use of data generated and processed about them.
Illustrate some of the uses of frequent-buyer data.
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20. Ethics Guide: Unseen Cyberazzi (cont'd)
Cheap cloud processing of consumer data easier, less expensive.
Processing happens in secret.
Data brokers enable you to view data stored about you, but ...
Difficult to learn how to request your data,
Torturous process to file for it,
Limited data usefulness.
Help students understand the importance not just of what data is being gathered about them, but what analyses and conclusions are being made from that data, behind their back.
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21. Ethics Guide: Unseen Cyberazzi (cont'd)
Do you know what data is gathered about you? What is done with it?
Have you thought about conclusions data aggregators, or their clients, could make based on your use of frequent buyer cards?
Concerned about what federal government might do with data it gets from data aggregators?
Where does all of this end?
What will life be like for your children or grandchildren?
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Q3: How Do Organizations Use Data Warehouses and Data Marts to Acquire Data?
Functions of a data warehouse
Obtain data from operational, internal and external databases.
Cleanse data.
Organize and relate data.
Catalog data using metadata.
For a small organization, the extraction may be as simple as an Access database. Larger organizations, however, typically create and staff a group of people who manage and run a data
warehouse, which is a facility for managing an organization’s BI data.
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23. Components of a Data Warehouse
This figure shows the components of a data warehouse. Programs read operational and other data and extract, clean, and prepare that data for BI processing.
An organization might use Oracle for its operational processing, but use SQL Server for its data warehouse. Other organizations use SQL Server for operational processing, but use DBMSs from statistical package vendors such as SAS or SPSS in the data warehouse.
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24. Examples of Consumer Data That Can Be Purchased
Purchase of data about other organizations is not unusual or particularly concerning from a privacy standpoint.
However, some companies choose to buy personal, consumer data (like marital status) from data vendors like Acxiom Corporation.
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25. Possible Problems with Source Data
Curse of dimensionality
Most operational and purchased data have problems that inhibit their usefulness for BI analysis.
Problematic data is termed dirty data. Examples are a value of B for customer gender and of 213 for customer age. Other examples are a value of 999–999–9999 for a U.S. phone number, a part color of “gren,” and an address of The value of zero for Units in Figure 9-6 is dirty data.
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26. Data Warehouses Versus Data Marts
The data analysts who work with a data warehouse are experts at data management, data cleaning, data transformation, data relationships, and the like. However, they are not usually experts in a given business function.
A data mart is a subset of a data warehouse. A date mart addresses a particular component or functional area of the business.
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27. 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
A reporting application is a BI application that inputs data from one or more sources and applies reporting operations to that data to produce business intelligence.
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28. RFM Analysis: Example RFM Scores
Recently
Frequently
Money
RFM considers how recently (R) a customer has ordered, how frequently (F) a customer ordered, and how much money (M)
the customer has spent.
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29. RFM Analysis RFM Analysis Classification Scheme
Recent orders
Frequent orders
Money (amount) of money spent
Top 20% 1 2 3
Middle 20% 4
To produce an RFM score, a program sorts customer purchase records by date of most recent (R) purchase, divides sorts into quintiles, and gives customers a score of 1 to 5. Process is repeated for Frequently and Money. 5
Bottom 20%
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30. Example of Grocery Sales OLAP Report
OLAP cube
Two 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
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31. Example of Expanded Grocery Sales OLAP Report
Drill down
Four dimensions.
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.
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32. Example of Drilling Down into Expanded Grocery Sales OLAP Report
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.
All this flexibility comes at a cost. If the database is large, doing the necessary calculating, grouping, and sorting for such dynamic displays will require substantial computing power.
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33. Q5: How Do Organizations Use Data Mining Applications?
Source disciplines
Sometimes people use the term knowledge discovery in databases (KDD) as a synonym for data mining.
There are many interesting and rewarding careers for business professionals who are knowledgeable about data mining techniques.
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34. Unsupervised Data Mining
No a priori hypothesis or model.
Findings obtained solely by data analysis.
Hypothesized model created 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
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35. Supervised Data Mining
Uses a priori model.
Prediction, such as regression analysis.
Ex: CellPhoneWeekendMinutes
= (12 + (17.5*CustomerAge)+(23.7*NumberMonthsOfAccount)
= * *6 = minutes
Predict number of minutes of weekend cell phone use.
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36. Market-Basket Analysis
Identify sales patterns in large volumes of data.
Identify what products customers tend to buy together.
Computes probabilities of purchases.
Identify cross-selling opportunities.
Customers who bought fins also bought a mask.
Association analysis important part in shopping basket data analysis.
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37. 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.
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Decision Trees
Unsupervised data mining technique.
Hierarchical arrangement of criteria to predict a value or classification.
Basic idea
Select attributes most useful for classifying “pure groups.”
Creates decision rules.
Basic idea of a decision tree is to select attributes most useful for classifying entities.
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39. Credit Score Decision Tree
An institution considering the purchase of a loan portfolio can use the results of a decision tree program to evaluate the risk of a given portfolio.
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40. 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.
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41. So What? BI for Securities Trading?
Quantitative applications using BigData and BI.
Analyze immense amounts of data over a broad spectrum of sources.
Build and evaluate investment strategies.
Two Sigma (
Analyzes financial statements, developing news, Twitter activity, weather reports, other sources.
Develops and tests investment strategies.
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42. Two Sigma’s Five-step Process
Acquire data
Create models
Evaluate models
Analyze risks
Place trades
Does it work? Two Sigma and other firms claim it does.
Individual investors competing in the stock markets against Two Sigma, with is hundreds of PhDs and massive computing power, and with a slew of similar companies.
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43. 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, graphics, audio, and video.
Big Data is a term used to describe data collections that are characterized by huge volume, rapid velocity, and great variety.
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44. MapReduce Processing Summary
Map Phase: Google search log broken into thousands of pieces
Technique for harnessing power of thousands of computers working in parallel.
BigData collection is broken into pieces, and hundreds or thousands of independent processors search these pieces for something of interest.
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45. Google Trends on the Term Web 2.0
Reduce phase: results combined
This trend line supports contention that “Web 2.0” is fading from use.
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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.
Query language entitled Pig (platform for large dataset analysis).
Easy to master.
Extensible.
Automatically optimizes queries on map-reduce level.
BigData has volume, velocity, and variation characteristics that far exceed those of traditional reporting and data mining.
Experts are required to use it; you may be involved, however, in planning a BigData study or in interpreting results.
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47. Q7: What Is the Role of Knowledge Management Systems?
Knowledge Management (KM)
Creating value from intellectual capital and sharing knowledge with those who need that capital.
Preserving organizational memory
Capturing and storing lessons learned and best practices of key employees.
Scope of KM same as SM in hyper-social organizations.
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48. Benefits of Knowledge Management
Improve process quality.
Increase team strength.
Goal:
Enable employees to use organization’s collective knowledge.
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49. 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
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50. Example of IF/THEN Rules
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51. 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.
The few expert systems that have been successful have addressed more restricted problems than duplicating a doctor’s diagnostic ability. They address problems such as checking for harmful prescription drug interactions and configuring products to meet customer specifications.
These systems require many fewer rules and are therefore more manageable to maintain.
However, unless expert systems technology gets a boost from massively parallel computing (think MapReduce and Hadoop), their problems will cause them to fade from use.
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52. What Are Content Management Systems (CMS)?
Support management and delivery of documents, other expressions of employee knowledge.
Challenges of Content Management
Huge databases.
Dynamic content.
Documents refer to one another.
Perishable contents.
In many languages.
Content management system functions are huge and complex.
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53. What are CMS Application Alternatives?
In-house custom development
Customer support develops in-house database applications to track customer problems.
Off-the-shelf
Horizontal market products (SharePoint).
Vertical market applications.
Public search engine
Google, Bing.
Horizontal market: An accounting firm, for example, may license a vertical market application to manage document flow for the processing of tax returns or the management of audit documents.
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54. How Do Hyper-Social Organizations Manage Knowledge?
Hyper-social knowledge management
Social media, and related applications, for management and delivery of organizational knowledge resources.
Hyper-organization theory
Framework for understanding KM.
Focus shifts from knowledge and content to fostering authentic relationships among knowledge creators and users.
Progressive organizations encourage their employees to Tweet, post on Facebook or other social media sites, write blogs, and post videos on YouTube and any of the other sites.
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Hyper-Social KM Media
A rich directory is an employee directory that includes not only the standard name, , phone, and address but also organizational structure and expertise.
Rich directories are particularly useful in large organizations where people with particular expertise are unknown.
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56. Resistance to Knowledge Sharing
Employees reluctant to exhibit their ignorance.
Employee competition.
Remedy
Strong management endorsement.
Strong positive feedback.
“Nothing wrong with praise or cash especially cash.”
Strong management endorsement can be effective in encouraging knowledge sharing, especially if that endorsement is followed by strong positive feedback.
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57. Q8: What Are the Alternatives for Publishing BI?
In the BI context, most static reports are published as PDF documents. Dynamic reports are BI documents that are updated at the time they are requested. A sales report that is current at the time the user accessed it on a Web server is a dynamic report.
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58. What Are the Two Functions of a BI Server?
A BI server extends alert/RSS functionality to support user subscriptions, which are user requests for particular BI results on a particular schedule or in response to particular events. For example, a user can subscribe to a daily sales report, requesting that it be delivered each morning.
Management and delivery. The management function maintains metadata about the authorized allocation of BI results to users. The BI server tracks what results are available, what users are authorized to view those results, and the schedule upon which the results are provided to the authorized users. It adjusts allocations as available results change and users come and go
Management and delivery
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Q9: 2026?
Exponentially more information about customers, better data mining techniques.
Companies buy and sell your purchasing habits and psyche.
Singularity
Computer systems adapt and create their own software without human assistance.
Machines will possess and create information for themselves.
Will we know what the machines will know?
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60. 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 and 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.
Megan is able to combine data in various reports to infer protected information about company employees.
She was not supposed to see this information, but only used reports she was authorized to see.
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61. Guide: Data Mining in the Real World
Starting a data mining project, you never know how it will turn out.
Problems:
Dirty data
Missing values
Lack of knowledge at start of project
Over fitting
Probabilistic
Seasonality
High risk with unpredictable 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.
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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: 2026?
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63. Case Study 9: Hadoop the Cookie Cutter
Third-party cookie created by site other than one you visited.
Most commonly occurs when a Web page includes content from multiple sources.
DoubleClick
IP address where content was delivered.
DoubleClick instructs your browser to store a DoubleClick cookie.
Records data in cookie log on DoubleClick’s server.
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64. Case Study 9: Hadoop the Cookie Cutter (cont'd)
Third-party cookie owner has history of what was shown, what ads you clicked, and intervals between interactions.
Cookie log shows how you respond to ads and your pattern of visiting various Web sites where ads placed.
Firefox Lightbeam tracks and graphs cookies on your computer.
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65. FireFox Lightbeam: Display on Start Up
No Cookies
FireFox has an optional feature tracks and graphs all the cookies on your computer.
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After Visiting MSN.com
Who are these companies that are gathering my browser behavior data? If you hold your mouse over one of the cookies,
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67. 5 Sites Visited Yields 27 Third Parties
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68. Sites Connected to DoubleClick
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