Business Intelligence paper 1

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Chapter 1:

An Overview of Business Intelligence, Analytics, and Decision Support

Business Intelligence and Analytics: Systems for Decision Support

(10th Edition)

Business Intelligence and Analytics: Systems for Decision Support

(10th Edition)

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Learning Objectives

Understand today’s turbulent business environment and describe how organizations survive and even excel in such an environment (solving problems and exploiting opportunities)

Understand the need for computerized support of managerial decision making

Understand an early framework for managerial decision making

...

(Continued…)

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Learning Objectives

Learn the conceptual foundations of the DSS methodology

Describe the BI methodology and concepts and relate them to DSS

Understand the various types of analytics

List the major tools of computerized decision support

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Opening Vignette…

Magpie Sensing Employs Analytics to Manage a Vaccine Supply Chain Effectively and Safely

Company background

Problem

Proposed solution and results

Answer & discuss the case questions...

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Opening Vignette…

Questions for the Opening Vignette

What information is provided by the descriptive analytics employed at Magpie Sensing?

What type of support is provided by the predictive analytics employed at Magpie Sensing?

How does prescriptive analytics help in business decision making?

In what ways can actionable information be reported in real time to concerned users of the system?

In what other situations might real-time monitoring applications be needed?

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Changing Business Environment & Computerized Decision Support

Companies are moving aggressively to computerized support of their operations  Business Intelligence

Business Pressures–Responses–Support Model

Business pressures result of today's competitive business climate

Responses to counter the pressures

Support to better facilitate the process

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Business Pressures–Responses–Support Model

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The Business Environment

The environment in which organizations operate today is becoming more and more complex, creating

opportunities, and

problems.

Example: globalization.

Business environment factors:

markets, consumer demands, technology, and societal…

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Business Environment Factors

FACTOR DESCRIPTION

Markets Strong competition

Expanding global markets

Blooming electronic markets on the Internet

Innovative marketing methods

Opportunities for outsourcing with IT support

Need for real-time, on-demand transactions

Consumer Desire for customization

demand Desire for quality, diversity of products, and speed of delivery

Customers getting powerful and less loyal

Technology More innovations, new products, and new services

Increasing obsolescence rate

Increasing information overload

Social networking, Web 2.0 and beyond

Societal Growing government regulations and deregulation

Workforce more diversified, older, and composed of more women Prime concerns of homeland security and terrorist attacks

Necessity of Sarbanes-Oxley Act and other reporting-related legislation Increasing social responsibility of companies

Greater emphasis on sustainability

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Organizational Responses

Be Reactive, Anticipative, Adaptive, and Proactive

Managers may take actions, such as

Employ strategic planning.

Use new and innovative business models.

Restructure business processes.

Participate in business alliances.

Improve corporate information systems.

… more [in your book]

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Closing the Strategy Gap

One of the major objectives of computerized decision support is to facilitate closing the gap between the current performance of an organization and its desired performance, as expressed in its mission, objectives, and goals, and the strategy to achieve them.

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Managerial Decision Making

Management is a process by which organizational goals are achieved by using resources.

Inputs: resources

Output: attainment of goals

Measure of success: outputs / inputs

Management  Decision Making

Decision making: selecting the best solution from two or more alternatives

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The Nature of Managers’ Work Mintzberg's 10 Managerial Roles

Interpersonal

1. Figurehead

2. Leader

3. Liaison

Informational

4. Monitor

5. Disseminator

6. Spokesperson

Decisional

7. Entrepreneur

8. Disturbance handler

9. Resource allocator

10. Negotiator

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Decision-Making Process

Managers usually make decisions by following a four-step process (a.k.a. the scientific approach)

Define the problem (or opportunity)

Construct a model that describes the real-world problem.

Identify possible solutions to the modeled problem and evaluate the solutions.

Compare, choose, and recommend a potential solution to the problem.

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Information Systems Support for Decision Making

Group communication and collaboration

Improved data management

Managing data warehouses and Big Data

Analytical support

Overcoming cognitive limits in processing and storing information

Knowledge management

Anywhere, anytime support

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An Early Decision Support Framework (by Gory and Scott-Morten, 1971)

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An Early Decision Support Framework

Degree of Structuredness (Simon, 1977)

Decisions are classified as

Highly structured (a.k.a. programmed)

Semi-structured

Highly unstructured (i.e., nonprogrammed)

Types of Control (Anthony, 1965)

Strategic planning (top-level, long-range)

Management control (tactical planning)

Operational control

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The Concept of DSS

DSS - interactive computer-based systems, which help decision makers utilize data and models to solve unstructured problems

(Gorry and Scott-Morton, 1971)

Decision support systems couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions.

DS as an Umbrella Term

Evolution of DS into Business Intelligence

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A Framework for Business Intelligence (BI)

BI is an evolution of decision support concepts over time

Then: Executive Information System

Now: Everybody’s Information System (BI)

BI systems are enhanced with additional visualizations, alerts, and performance measurement capabilities

The term BI emerged from industry

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Definition of BI

BI is an umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies

BI is a content-free expression, so it means different things to different people

BI's major objective is to enable easy access to data (and models) to provide business managers with the ability to conduct analysis

BI helps transform data, to information (and knowledge), to decisions, and finally to action

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A Brief History of BI

The term BI was coined by the Gartner Group in the mid-1990s

However, the concept is much older

1970s - MIS reporting - static/periodic reports

1980s - Executive Information Systems (EIS)

1990s - OLAP, dynamic, multidimensional, ad-hoc reporting -> coining of the term “BI”

2010s - Inclusion of AI and Data/Text Mining capabilities; Web-based Portals/Dashboards, Big Data, Social Media, Analytics

2020s - yet to be seen

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The Evolution of BI Capabilities

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The Architecture of BI

A BI system has four major components

a data warehouse, with its source data

business analytics, a collection of tools for manipulating, mining, and analyzing the data in the data warehouse

business performance management (BPM) for monitoring and analyzing performance

a user interface (e.g., dashboard)

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A High-Level Architecture of BI

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Business Value of BI Analytical Applications

Customer segmentation

Propensity to buy

Customer profitability

Fraud detection

Customer attrition

Channel optimization

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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?

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A Multimedia Exercise in Business Intelligence

Teradata University Network (TUN)

www.teradatauniversitynetwork.com

BSI Videos (Business Scenario Investigations)

www.youtube.com/watch?v=NXEL5F4_aKA

Also look for other BSI Videos at TUN

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DSS-BI Connections

Similarities and differences?

Similar architectures, data focus, …

Direct vs. indirect support

Different target audiences

Commercially available systems versus in-house development of solutions

Origination – Industry vs. Academia

So, is DSS = BI ?

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Analytics Overview

Analytics?

Something new or just a new name for …

A Simple Taxonomy of Analytics (proposed by INFORMS)

Descriptive Analytics

Predictive Analytics

Prescriptive Analytics

Analytics or Data Science?

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Analytics Overview

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Application Case 1.2

Eliminating Inefficiencies at Seattle Children’s Hospital

Questions for Discussion

Who are the users of the tool?

What is a dashboard?

How does visualization help in decision making?

What are the significant results achieved by the use of Tableau?

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Application Case 1.3

Analysis at the Speed of Thought

Questions for Discussion

What are the desired functionalities of a reporting tool?

What advantages were derived by using a reporting tool in the case?

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Application Case 1.4

Moneyball: Analytics in Sports and Movies

Questions for Discussion

How is predictive analytics applied in Moneyball?

What is the difference between objective and subjective approaches in decision making?

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Application Case 1.5

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?

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Application Case 1.6

Industrial and Commercial Bank of China (ICBC) Employs Models to Reconfigure Its Branch Networks

Questions for Discussion

How can analytical techniques help organizations to retain competitive advantage?

How can descriptive and predictive analytics help in pursuing prescriptive analytics?

What kind of prescriptive analytic techniques are employed in the case study?

Are the prescriptive models once built good forever?

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Introduction to Big Data Analytics

Big Data?

Not just big!

Volume

Variety

Velocity

More of Big Data and related analytics tools and techniques are covered in Chapter 13.

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Application Case 1.7

Gilt Groupe’s Flash Sales Streamlined by Big Data Analytics

Questions for Discussion

What makes this case study an example of Big Data analytics?

What types of decisions does Gilt Groupe have to make?

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End-of-Chapter Application Case

Nationwide Insurance Used BI to Enhance Customer Service

Questions for Discussion

Why did Nationwide need an enterprise-wide data warehouse?

How did integrated data drive the business value?

What forms of analytics are employed at Nationwide?

With integrated data available in an enterprise data warehouse, what other applications could Nationwide potentially develop?

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Plan of the Book

Part I - Decision Making and Analytics: An Overview

(Chapters 1 & 2)

Part II - Descriptive Analytics

(Chapters 3 & 4)

Part III - Predictive Analytics

Chapters 5 - 8

Part IV - Prescriptive Analytics

Chapter 9 - 12

Part V - Big Data and Future Directions for Business Analytics

Chapters 13 & 14

PLUS - Online Supplements

…

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End of the Chapter

Questions / Comments…

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All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America.

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Data

Warehouse

Technical staff

Data Warehouse

Environment

Data

Sources

Business Analytics

Environment

Performance and

Strategy

Business usersManagers / executives

Built the data warehouseAccess

Manipulation

Results

BPM strategy

üOrganizing

üSummarizing

üStandardizing

Future component

intelligent systems

User Interface

-browser

-portal

-dashboard