Business Intelligence and Data Mining Assignment

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Business Intelligence, Analytics, and Data Science: A Managerial Perspective

Fourth Edition

Chapter 1

An Overview of Business Intelligence, Analytics, and Data Science

Learning Objectives

1.1 Understand the need for computerized support of managerial decision making

1.2 Recognize the evolution of such computerized support to the current state—analytics/data science

1.3 Describe the business intelligence (BI) methodology and concepts

1.4 Understand the various types of analytics, and see selected applications

1.5 Understand the analytics ecosystem to identify various key players and career opportunities

Slide 1-2

Slide 2 is a list of textbook LO numbers and statements.

OPENING VIGNETTE Sports Analytics—An Exciting Frontier for Learning and Understanding Applications of Analytics (1 of 5)

Sports analytics is becoming a specialty within analytics

Sports is a big business

Generating $145B in revenues annually

Additional $100B in legal and $300B in illegal gambling

Analytic in sports popularized by the Moneyball book by Michael Lewis in 2003

About Oakland A’s

And the follow-on movie in 2011

Nowadays, analytics is used in many facets of sports

Slide 1-3

OPENING VIGNETTE Sports Analytics—An Exciting Frontier for Learning and Understanding Applications of Analytics (2 of 5)

Example 1: The Business Office

FIGURE 1.1 Season Ticket Renewals—Survey Scores

Slide 1-4

OPENING VIGNETTE Sports Analytics—An Exciting Frontier for Learning and Understanding Applications of Analytics (3 of 5)

Example 2: The Coach

FIGURE 1.4 Heat Map Zone Analysis for Passing Plays

Slide 1-5

OPENING VIGNETTE Sports Analytics—An Exciting Frontier for Learning and Understanding Applications of Analytics (4 of 5)

Example 3: The Trainer

FIGURE 1.7 Single Leg Squat Hold Test – Core Body Strength Test

(Source: WILKERSON and GUPTA).

Slide 1-6

OPENING VIGNETTE Sports Analytics—An Exciting Frontier for Learning and Understanding Applications of Analytics (5 of 5)

Discussion Questions

What are three factors that might be part of a PM for season ticket renewals?

What are two techniques that football teams can use to do opponent analysis?

How can wearables improve player health and safety? What kinds of new analytics can trainers use?

What other analytics applications can you envision in sports?

Slide 1-7

Changing Business Environments and Evolving Needs for Decision Support and Analytics

Increased hardware, software, and network capabilities

Group communication and collaboration

Improved data management

Managing giant data warehouses and Big Data

Analytical support

Overcoming cognitive limits in processing and storing information

Knowledge management

Anywhere, anytime support

Slide 1-8

Evolution of Computerized Decision Support to Analytics/Data Science

FIGURE 1.8 Evolution of Decision Support, Business Intelligence, and Analytics

Slide 1-9

A Framework for Business Intelligence

Slide 1-10

DSS  EIS  BI

Definition of Business Intelligence

[Broad Definition] An umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies

[Narrow Definition] Descriptive analytics tools and techniques (i.e., reporting tools)

A Brief History of BI – 1970s  1980s  1990s …

The Origins and Drivers of BI (See Figure 1.9)

The Architecture of BI (See Figure 1.10)

A Framework for Business Intelligence

FIGURE 1.9 Evolution of Business Intelligence (BI) 

Slide 1-11

A Framework for Business Intelligence

The Architecture of BI

FIGURE 1.10 A High-Level Architecture of BI

Slide 1-12

Application Case 1.1 Sabre Helps Its Clients through Dashboards and Analytics

Questions for Discussion

What is traditional reporting? How is it used in the organization?

How can analytics be used to transform the traditional reporting?

How can interactive reporting assist organizations in decision making?

Slide 1-13

A Multimedia Exercise in Business Intelligence

Slide 1-14

TUN (TeradataUniversityNetwork.com)

BSI Videos (Business Scenario Investigations)

Analogues to CSI (Crime Scene Investigation)

Go To

www.youtube.com /watch?v=NXEL5F4_aKA

See the

www.slideshare.net/teradata/bsi-how-we-did-it-the-case-of-the-misconnecting-passengers.slides

Discuss the case presented in the video and in the slides

Transaction Processing versus Analytic Processing

Online Transaction Processing (OLTP)

Operational databases

ERP, SCM, CRM, …

Goal: data capture

Online Analytical Processing (OLAP)

Data warehouses

Goal: decision support

What is the relationship between OLTP and OLAP?

Slide 1-15

Appropriate Planning and Alignment with the Business Strategy

Planning and Execution  Business, Organization, Functionality, and Infrastructure

Functions served by BI Competency Center

How BI is linked to strategy and execution of strategy

Encourage interaction between the potential business user communities and the IS organization

Serve as a repository and disseminator of best BI practices between and among the different lines of business.

Standards of excellence in BI practices can be advocated and encouraged throughout the company

…

Slide 1-16

Real-Time, On-Demand BI Is Attainable

Emergence of real-time BI applications

Justifying the need

Is there a need for real-time [is it worth the additional expense]?

Leveraging the enablers

RFID

Web services

Intelligent agents

Slide 1-17

Critical BI System Considerations

Developing or Acquiring BI Systems

Make versus buy

BI shells

Justification and Cost–Benefit Analysis

A challenging endeavor, why?

Security

Protection of Privacy

Integration to Other Systems and Applications

Slide 1-18

Analytics Overview

Analytics…a relatively new term/buzz-word

Analytics…the process of developing actionable decisions or recommendations for actions based on insights generated from historical data

According to the Institute for Operations Research and Management Science (INFORMS)

Analytics represents the combination of computer technology, management science techniques, and statistics to solve real problems.

Slide 1-19

Business Analytics

FIGURE 1.11 Three Types of Analytics

Slide 1-20

Descriptive Analytics

Descriptive or reporting analytics

Answering the question of what happened

Retrospective analysis of historic data

Enablers

OLAP / DW

Data visualization

Dashboards and Scorecards

Descriptive statistics

Slide 1-21

Application Case 1.2 Silvaris Increases Business with Visual Analysis and Real-Time Reporting Capabilities

Questions for Discussion

What was the challenge faced by Silvaris?

How did Silvaris solve its problem using data visualization with Tableau?

Slide 1-22

Application Case 1.3 Siemens Reduces Cost with the Use of Data Visualization

Questions for Discussion

What challenges were faced by Siemens’ visual analytics group?

How did the data visualization tool Dundas BI help Siemens in reducing cost?

Slide 1-23

Predictive Analytics

Aims to determine what is likely to happen in the future (foreseeing the future events)

Looking at the past data to predict the future

Enablers

Data mining

Text mining / Web mining

Forecasting (i.e., time series)

Slide 1-24

Application Case 1.4 Analyzing Athletic Injuries

Questions for Discussion

What types of analytics are applied in the injury analysis?

How do visualizations aid in understanding the data and delivering insights into the data?

What is a classification problem?

What can be derived by performing sequence analysis?

Slide 1-25

Prescriptive Analytics

Aims to determine the best possible decision

Uses both descriptive and predictive to create the alternatives, and then determines the best one

Enablers

Optimization

Simulation

Multi-Criteria Decision Modeling

Heuristic Programming

Analytics Applied to Many Domains

Analytics or Data Science?

Slide 1-26

Application Case 1.5 A Specialty Steel Bar Company Uses Analytics to Determine Available-to-Promise Dates

Questions for Discussion

Why would reallocation of inventory from one customer to another be a major issue for discussion?

How could a DSS help make these decisions?

Slide 1-27

Analytics Examples in Selected Domains

Analytics Application in HealthCare—Humana Examples

Example 1: Preventing Falls in a Senior Population—An Analytic Approach

Example 2 : Humana’s Bold Goal—Application of Analytics to Define the Right Metrics

Example 3: Predictive Models to Identify the Highest Risk Membership in a Health Insurer

Slide 1-28

Analytics Examples in Selected Domains

Slide 1-29

Analytics in Retail Value Chain

FIGURE 1.12 Example of Analytics Applications in a Retail Value Chain

Analytics Examples in Retail Value Chain

Slide 1-30

For the complete table, refer to your textbook

A Brief Introduction to Big Data Analytics

What Is Big Data? (Is it just “big”?)

Big Data is data that cannot be stored or processed easily using traditional tools/means

Big Data typically refers to data that comes in many different forms: large, structured, unstructured, continuous

3Vs – Volume, Variety, Velocity

Data (Big Data or otherwise) is worthless if it does not provide business value (and for it to provide business value, it has to be analyzed)

More on Big Data Analytics is in Chapter 7

Slide 1-31

Application Case 1.6 CenterPoint Energy Uses Real-Time Big Data Analytics to Improve Customer Service

Questions for Discussion

How can electric companies predict possible outage at a location?

What is customer sentiment analysis?

How does customer sentiment analysis help provide a personalized service to their customers?

Slide 1-32

An Overview of the Analytics Ecosystem

What are the key players in analytics industry?

What do they do?

Is there a place for you to be a part of it?

There is a need to classify different industry participants in the broader view of analytics to

Identify providers (as an analytics consumer)

Identify roles to play (as a potential provider)

Identify job opportunities

Identify investment/entrepreneurial opportunities

Understand the landscape and the future of computerized decision sport systems

Slide 1-33

An Overview of the Analytics Ecosystem

FIGURE 1.13 Analytics Ecosystem

Slide 1-34

An Overview of the Analytics Ecosystem

Data Generation Infrastructure Providers

Data Management Infrastructure Providers

Data Warehouse Providers

Middleware Providers

Data Service Providers

Analytics Focused Software Developers

Descriptive, Predictive, Prescriptive

Application Developers: Industry Specific or General

Analytics Industry Analysts and Influencers

Slide 1-35

Academic Institutions and Certification Agencies

Certificates

Masters programs

Undergraduate programs

Offered by

MIS, Engineering

Marketing, Statistics

Computer Science

…

Regulators and Policy Makers

Analytics User Organizations

An Overview of the Analytics Ecosystem

Slide 1-36

Plan of the Book

FIGURE 1.15 Plan of the Book

Slide 1-37

Resources

Teradata University Network (TUN)

Slide 1-38

TeradataUniversityNetwork.com

End of Chapter 1

Questions / Comments

Slide 1-39