Describe the various ways business intelligence, big data and analytics improves the utilization of information within an organization. Suggest a company (Netflix.com) th

1

Business Intelligence Systems

Chapter 9

9-2

Study Questions

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

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: What are three techniques for processing BI Data?

Q5: What are the alternatives for publishing BI?

 How does the knowledge in this chapter help you?

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Q1: How Do Organizations Use Business Intelligence (BI) Systems?

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

Structure of Business Intelligence System

9-4

Sample of Uses of Business Intelligence

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

3

9-5

What Are Typical Uses for BI?

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• Identifying changes in purchasing patterns – Important life events change what customers buy

• BI for Entertainment – Netflix has data on watching, listening, and rental habits – Classify customers by viewing patterns

• Just-in-Time Medical Reporting – Real-time injection notification services to doctors during

exams

9-6

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

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

4

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Using Business Intelligence to Find Candidate Parts at Falcon Security

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• Candidate parts – Provided by vendors who agree to make part design files

available for sale – Purchased by larger customers – Frequently ordered parts – Ordered in small quantities – Simple in design (easier to 3D print)

• Part weight and price surrogates for simplicity

9-8

Acquire Data: Extracted Order Data

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• Query Sales (CustomerName, Contact, Title, Bill Year, Number Orders, Units, Revenue, Source, PartNumber) Part (PartNumber, Shipping Weight, Vendor)

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Sample Extracted Data: Part Table

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

9-10

Joining Order Extract and Filtered Parts Tables

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

6

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Sample Orders and Parts View Data

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

9-12

Creating Customer Summary Query

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

7

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Customer Summary

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

9-14

Qualifying Parts Query Design

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

8

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Publish Results: Qualifying Parts Query Results

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

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Publish Results: Sales History for Selected Parts

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

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Q3: How Do Organizations Use Data Warehouses and Data Marts to Acquire Data?

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• Data Warehouse Functions – Obtain data from operational, internal and external databases – Cleanse data – Organize and relate data – Catalog data using metadata

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Components of a Data Warehouse

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

10

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Examples of Consumer Data That Can Be Purchased

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

9-20

Possible Problems with Source Data

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

Curse of dimensionality

11

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Data Warehouses Versus Data Marts

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

9-22

Q4: What are Three Techniques for Processing BI Data?

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

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Reporting Analysis

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• Process of sorting, grouping, summing, filtering, and formatting structured data

• Structured data – Data in rows and columns like tables

• Exception reports – Produced when something out of predefined bounds occurs

• Printed and dynamic reports

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Data Mining

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• Application of statistical techniques to find patterns and relationships among data for classification and prediction

• Combined discipline of statistics, mathematics, artificial intelligence, and machine-learning

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Two Broad Categories of Data Mining

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

1. Unsupervised procedures – Does not start with a priori hypothesis or model – Hypothesized model created afterward based on analytical

results to explain patterns found  Example: Cluster analysis

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Supervised Data Mining

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• Uses a priori model to compute outcome of model

• Prediction, such as multiple linear regression • Ex: CellPhoneWeekendMinutes

= (12 + (17.5*CustomerAge)+(23.7*NumberMonthsOfAccount) = 12 + 17.5*21 + 23.7*6 = 521.7

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BigData

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• Huge volume – Petabyte and larger

• Rapid velocity – Generated rapidly

• Great variety – Structured data, free-form text, log files, graphics, audio, and

video

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MapReduce Processing Summary

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

Google search log broken into pieces

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Google Trends on the Term Web 2.0

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

9-30

Hadoop

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• Open-source program supported by Apache Foundation • Manages thousands of computers • Implements MapReduce

– Written in Java • Amazon.com supports Hadoop as part of EC3 cloud offering • Pig query language platform for large dataset analysis

– Easy to master – Extensible – Automatically optimizes queries on map-reduce level

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Q5: What Are the Alternatives for Publishing BI?

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

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What Are the Two Functions of a BI Server?

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

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How Does the Knowledge in This Chapter Help You?

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• Business intelligence a critical skill

• Enables imagining innovative uses for data your employer generates and know some of constraints of such use

• Digital marketing #1 priority for technology investment

• At PRIDE, knowledge of this chapter helps understand possible uses for exercise data

9-34

Ethics Guide: Unseen Cyberazzi

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• 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

– Used to create business intelligence to sell to companies and governments

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Ethics Guide: Unseen Cyberazzi (cont'd)

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• Cheap cloud processing makes processing 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

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Ethics Guide: Unseen Cyberazzi (cont'd)

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• 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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Guide: Semantic Security

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

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?

1

Reporting Systems and OLAP

Chapter Extension 13

ce13-2

Study Questions

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

Q1: How do reporting systems enable people to create information?

Q2: What are the components and characteristics of reporting systems?

Q3: How are reports authored, managed, and delivered?

Q4: How are OLAP reports dynamic?

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Q1: How Do Reporting Systems Enable People to Create Information?

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• Reporting systems – Create meaningful information from disparate data sources – Deliver information to user on time

• Generate information by: – Filtering data – Sorting data – Grouping data – Making simple calculations

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Raw Sales Data: List Contains Little or No Information

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

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Sales Data Sorted by Customer Name

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

Create Information by Sorting

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Sales Data Sorted by Customer Name and Grouped by Number of Orders and Showing Purchase Amounts

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

Create Information by Grouping & Summing

4

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Sales Data Filtered to Show Repeat Customers

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

Create Information by Filtering & Formatting

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Q2: What Are the Components and Characteristics of Reporting Systems?

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• Reads and combines data from disparate data sources

• Filters, sorts, groups, simple calculations to produce meaningful contexts

• Metadata to prepare and deliver reports • Report characteristics

– Type – Media – Mode

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Components of a Reporting System

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

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Summary of Report Characteristics

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

6

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Report Media

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• Printed on paper • PDFs—Portable Document Format • Delivered to computer screens and mobile devices

Applications for CRM and ERP systems • Digital dashboards • Alerts • Exported from report generator to another program • Published via Web service

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Digital Dashboard Example

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

7

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Report Mode

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

Types of reports

• Push report – Sent or e-mailed to users according to a preset schedule

• Pull report – Users download report from web portal or digital dashboard

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Q3: How Are Reports Authored, Managed and Delivered?

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

Report authoring • Connecting to data sources, create report structure, format

report • Examples: MS Access, Visual Studio

Report management • Defines who receives what reports, when, what format, and

by what means • Defines user accounts and user groups

8

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Connecting to a Report Data Source Using Visual Studio

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

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How Are Reports Delivered?

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

Report delivery function • Pushes or allows pulling reports based on metadata

• Delivered via an email server, Web site, SOA services, or other program-specific means

• Security components ensure only authorized users receive reports

• For queries, serves as intermediary between users and report generator

9

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Q4: How Are OLAP Reports Dynamic?

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• OLAP provides ability to sum, count, average, perform other simple arithmetic operations on groups of data

• OLAP reports – Dynamic

– Measure: item summed, averaged or processed in OLAP report.

– Dimension: category or class

– Cube: measure with associated dimensions

– OLAP vendors and products

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OLAP CUBE: Typical OLAP Report

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

OLAP Product Family by Store Type

10

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OLAP Sales Report by Product Family, Store Country, State, Store Type

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

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OLAP Sales Report by Country, State, City and Store Type

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

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Role of OLAP Server and Dimensional Database

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

1

Database Marketing

Chapter Extension 12

ce12-2

Study Questions

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

Q1: What is a database marketing opportunity?

Q2: How does RFM analysis classify customers?

Q3: How does market-basket analysis identify cross-selling opportunities?

Q4: How do decision trees identify market segments?

2

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Owner of Carbon Creek Gardens Needs Database Marketing

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• As business grew, lost track of customers

• Lost valuable customer and didn't know it

• Has lot of sales data, but needs system to: – Store and track customers – Store and track services provided to customers – Store and report future scheduled services

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Database Marketing

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• Application of business intelligence systems to: – Planning marketing programs – Executing marketing programs – Assessing marketing programs

• Databases and data mining techniques key components

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Q2: How Does RFM Analysis Classify Customers?

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• Recently

• Frequently

• Money

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RFM Analysis Classifies Customers

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

4

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Q3: How Does Market-Basket Analysis Identify Cross-Selling Opportunities?

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• Unsupervised data mining method – 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

How many customers bought both fins and a mask?

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Market-Basket Example: Transactions = 400

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

5

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Support: Probability that Two Items Will Be Bought Together

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• P(Fins and Masks) = 250/400, or 62% • P(Fins and Weights) = 20/400, or 5%

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Confidence = Conditional Probability Estimate

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• Probability of buying Fins = 250 — Probability of buying Mask = 270 • P(After buying Mask, then will buy Fins) — Confidence = 250/270 or 92.6%

6

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Lift = Confidence ÷ Base Probability

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• Lift = Confidence of Mask/Base P(Fins)

• Lift = .926/.625 = 1.32

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Warning!

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• Analysis only shows shopping carts with two items

• Must analyze large number of shopping carts with three or more items

• Know what problem you are solving before mining the data – Know what question you want to answer

7

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Q4: How Do Decision Trees Identify Market Segments?

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• Hierarchical arrangement of criteria to predict a classification or value

• If/Then hierarchy

• Unsupervised data mining technique

• Basic idea of a decision tree – Select attributes most useful for classifying something on

some criterion to create “pure groups”

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Decision Tree for Student

Performance

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

If Junior = Yes

Lower-level groups more similar than higher-level groups

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Transforming a Set of Decision Rules

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• If student is a junior and works in a restaurant, – Then predict grade =>3.0

• If student is a senior and is a nonbusiness major, – Then predict grade <3.0

• If student is a junior and does not work in a restaurant, – Then predict grade <3.0

• If student is a senior and is a business major, – Then make no prediction

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Decision Tree for Loan Evaluation

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• Classify loan applications by likelihood of default • Rules identify loans for bank approval • Identify market segment • Structure marketing campaign • Predict problems

9

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Credit Score Decision Tree

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

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Ethics Guide: Data Mining in the Real World

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

Problems with data: • Dirty data • Missing values • Lack of knowledge at start of project • Overfitting – too many variables • Probabilistic—good model may have unlucky first uses • Seasonality influences • High risk – uncovering something self-defeating to reveal

10

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Ethics Guide: Data Mining in the Real World (cont’d)

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• When you start a data mining project, you never know how it will turn out

• Decision trees can be used to select variables for other types of data mining analysis

1

Data Breaches

Chapter Extension 14

ce14-2

Study Questions

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

Q1: What is a data breach?

Q2: How do data breaches happen?

Q3: How should organizations respond to data breaches?

Q4: What are the legal consequences of a data breach?

Q5: How can data breaches be prevented?

2

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Q1: What is A Data Breach?

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

• Data breach – Unauthorized person views, alters, or steals secured data

• 1+ billion people affected in past 5 years, 75% of breaches happened in US

• Average cost of a single data breach $3.5 million • Average costs per stolen record

Healthcare ($359), Pharmaceutical ($227 Communications industries ($177)

Education ($294) Financial ($206)

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Costs of Handling a Data Breach

Direct Costs

• Notification

• Detection

• Escalation

• Remediation

• Legal fees and consultation

Indirect Costs • Loss of reputation • Abnormal customer

turnover • Increased customer

acquisition activities • Additional $3.3 million

per incident in US

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

3

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What Are the Odds?

• More likely to lose smaller amounts of data than larger amounts of data  22% chance of losing 10,000 records over any 24-month

period  <1% chance of losing 100,000 records over same period

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

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Well-known Data Breaches

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

4

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Why Do Data Breaches Happen?

• 67% are hackers trying to make money from: – Personally identifiable information (PII)  Names, addresses, dates of birth, Social Security

numbers, credit card numbers, health records, bank account numbers, PINs, email addresses

• Rogue internal employees

• Credit card fraud, identity theft, extortion, industrial espionage

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

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Q2: How Do Data Breaches Happen?

• Attack vectors – Phishing scam – Trick users into donating funds for a natural disaster – Exploit new software vulnerability

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

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Hitting Target

• Lost 40 million credit and debit card numbers to attackers (Dec. 18, 2013)

• Less than a month later, announced additional 70 million customer names, emails, addresses, phone numbers stolen – Total 98 million customers affected

• Stolen from point-of-sale (POS) systems at Target retail stores

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

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How Did They Do It?

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

6

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The Damage

• Attackers sold about 2 million credit card numbers and PINs for about $26.85 each (total $53.7 million)

• Sold in batches of 100,000 card numbers

• Cost Target $450 million – Upgraded POS terminals to support chip-and-PIN enabled cards – Increased insurance premiums, legal fees, credit card processors

settlement, pay for consumer credit monitoring, regulatory fines – Lost sales, 46% drop in next quarter revenues

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

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Collateral Damage

• Credit unions and banks – Spent more than $200 million issuing new cards

• Consumers – Enrolled in credit monitoring, continually watch their credit,

and fill out paperwork if fraudulent charges appear on statements

• Increased insurance premiums, stricter controls, and more system auditing for organizations similar to Target

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

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Q3: How Should Organizations Respond To Data Breaches?

• Respond Quickly – Stop hackers from doing more damage  Exfiltration or illegally transferring data out

– Immediately notify affected users

• Plan for a Data Breach – Walkthroughs, business continuity planning, computer

security incident response team (CSIRT)

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

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Q3: How Should Organizations Respond To Data Breaches? (cont'd)

• Get experts to perform an effective forensic investigation

• Identify additional technical and law enforcement professionals needed

• Be honest about the breach

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

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Best Practices for Notifying Users of a Data Breach

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

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Q4: What Are The Legal Consequences of a Data Breach?

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

9

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Regulatory Laws Govern the Secure Storage of Data in Certain Industries • Federal Information Security Management Act (FISMA)

– Requires security precautions for government agencies

• Gramm-Leach-Bliley Act (GLBA), a.k.a., Financial Services Modernization Act – Requires data protection for financial institutions

• Health Information Portability and Accountability Act (HIPAA) – Requires data protection for healthcare institutions

• Payment Card Industry Data Security Standard (PCI DSS) – Governs secure storage of cardholder data

• Family Educational Rights and Privacy Act (FERPA) – Provides protection for student education records

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

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Q5: How Can Data Breaches Be Prevented?

• Use countermeasures software or procedures to prevent an attack

• Better phishing detection software

• Better authentication (i.e., multifactor authentication

• Network intrusion detection system (NIDS) to examine traffic passing through internal network

• Data loss prevention systems (DLP) to prevent sensitive data from being released to unauthorized persons

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .

10

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Q5: How Can Data Breaches Be Prevented? (cont'd) • Appoint a chief information security officer (CISO) to ensure

sufficient executive support and resources

C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c .