1. Chapter 1:
An Overview of Business Intelligence, Analytics, and Decision Support

2. Learning Objectives
Understand today’s turbulent business environment and describe how organizations survive and even excel in such an environment
Understand the need for computerized support of managerial decision making
Understand an early framework for managerial decision making
Learn the conceptual foundations of the decision support systems (DSS) methodology

3. Learning Objectives
Describe the business intelligence (BI) methodology and concepts and relate them to DSS
Describe the concept of work systems and its relationship to decision support
List the major tools of computerized decision support
Understand the major issues in implementing computerized support systems

5. 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

6. Managerial Decision Making

7. 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…

8. Business Environment Factors

9. 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]

10. 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.

11. Managerial Decision Making
The nature of managers’ work
Interpersonal
Informational
Decisional

12. 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

13. 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

14. Managerial Decision Making
The process of decision making
Defining the problem (a decision situation that may deal with some difficulty or with an opportunity)
Constructing a model that describes the real-world problem
Identifying possible solutions to the modeled problem and evaluating the solutions
Comparing, choosing, and recommending a potential solution to the problem

15. How hard it is to follow the process?
Sufficient alternatives solutions are being considered
These alternatives should be reasonably predicted
Comparisons should be done properly
However!!

16. Difficulties in following the decision process
Technology, information systems, advanced search engines, and globalization result in more and more alternatives
Government regulations, changing, consumer demand, political instability = more uncertainty
Trial and error learning is costly and difficult
Environments are growing more complex
The need for computer support is vital! >>

17. Managerial Decision Making
Decision support systems (DSS)
A conceptual framework for a process of supporting managerial decision- making, usually by modeling problems and employing quantitative models for solution analysis

18. Computerized Support for Decision Making
Why use computerized decision support systems
Speedy computations
Improved communication and collaboration
Increased productivity of group members
Improved data management
Managing giant data warehouses

19. Computerized Support for Decision Making
Why use computerized decision support systems
Quality support
Agility support
Overcoming cognitive limits in processing and storing information
Using the Web
Anywhere, anytime support

20. An Early Decision Support Framework (Gorry and Scott-Mortin 1971)
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

21. An Early Framework for Computerized Decision Support

22. An Early Framework for Computerized Decision Support
Degree of structuredness
Four-phase decision making process
Intelligence
Design
Choice
Implementation

23. An Early Framework for Computerized Decision Support

24. An Early Framework for Computerized Decision Support
Computer support for structured decisions
Management science (MS) or operations research (OR)
The application of a scientific approach and mathematical models to the analysis and solution of managerial decision situations (e.g., problems, opportunities)
- Since 1960’s

25. An Early Framework for Computerized Decision Support
Computer support for structured decisions
Automated decision systems (ADS)
A business rules-based system that uses intelligence to recommend solutions to repetitive decisions (such as pricing)

26. An Early Framework for Computerized Decision Support

27. An Early Framework for Computerized Decision Support
The decision support matrix
For semistructured decisions and unstructured decisions, conventional MIS and management sciences (MS) tools are insufficient
Supportive information systems = they called: Decision support systems (DSS)

28. 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

29. The Concept of Decision Support Systems (DSS)
DSS as an Umbrella term: Describes any computerized system that supports decision making in an organization
DSS as a specific application
The architecture of DSS
Data
Models manipulate data as related to a specific situation
Knowledge component
Users
User interface

30. The Concept of Decision Support Systems (DSS)

31. The Concept of Decision Support Systems (DSS)
Types of DSS
model-oriented DSS: quantitative models used to generate a recommended solution to a problem
data-oriented DSS: support ad-hoc reporting and queries

32. A Framework for Business Intelligence (BI)
An umbrella term that combines architectures, tools, databases, applications, and methodologies
Evolution of BI>>>

33. 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

34. 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

35. Definition of BI
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

36. A Framework for Business Intelligence (BI)

37. 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)

38. A High-Level Architecture of BI

39. A Framework for Business Intelligence (BI)
Data warehouse
Originally, included historical data that were organized and summarize, so end users could easily view or manipulate data and information
Today, some data warehouses include current data as well, so they can provide real time decision support

40. A Framework for Business Intelligence (BI)
Business analytics
Reporting and queries
Advanced analytics
Data, text and Web mining and other sophisticated mathematical and statistical tools

41. A Framework for Business Intelligence (BI)
Data mining
A process of searching for unknown relationships or information in large databases or data warehouses, using intelligent tools such as neural computing, predictive analytics techniques, or advanced statistical methods

42. A Framework for Business Intelligence (BI)
Business performance management (BPM)
An advanced performance measurement and analysis approach that embraces planning and strategy
BPM extends the monitoring, measuring, and comparing of sales, profit, cost, profitability, and other performance indicators by introducing the concept of “management and feedback
BPM provides a top-down enforcement of corporate-wide strategy

43. A Framework for Business Intelligence (BI)
Business performance management
User interface
Dashboard
A visual presentation of critical data for executives to view. It allows executives to see hot spots in seconds and explore the situation
Dashboards integrate information from multiple business areas
Visualization tools

44. Business Value of BI Analytical Applications
Customer segmentation
Propensity to buy
Customer profitability
Fraud detection
Customer attrition
Channel optimization

45. A Framework for Business Intelligence (BI)
Styles of BI
Report Delivery and Alerting
Enterprise Reporting (dashboard, scorecard)
Cube Analysis (Slice and Dice Analysis)
Ad-hoc Query
Statistics and Data Mining

46. A Framework for Business Intelligence (BI)
The DSS-BI connection
The architecture is very similar since BI evolved from DSS
DSS are constructed to directly support specific decision making; BI systems are geared to provide accurate and timely information (indirect support)
BI has an executive and strategy orientation while DSS has been oriented toward analysts

47. A Framework for Business Intelligence (BI)
The DSS-BI connection
BI systems are constructed with commercially available tools and components that are fitted to the needs of organizations; DSS more programming is used to construct custom solutions to very unstructured problems
DSS were developed mostly in the academic world; BI were developed mostly by software companies
Many tools used by BI are also considered DSS tools (e.g., data mining and predictive analysis )

48. A Multimedia Exercise in Business Intelligence
Teradata University Network (TUN)
BSI Videos (Business Scenario Investigations)
Also look for other BSI Videos at TUN

49. Takeaways? You can use data to make decisions
The decisions may be complex – with tradeoffs because of capacity - which is why you may need to automate the decisions
The rules you use will evolve over time, be refined
You can measure the consequences of decisions – good vs. bad
You can tie decisions to business goals

50. Analytics Overview Analytics?
Something new or just a new name for …
SAS Institute Inc. proposed eight levels of analytics.
The Institute for Operations Research and Management Sciences (INFORMS). Has created major initiative to organize and promote analytics.

52. Analytics Overview
A Simple Taxonomy of Analytics (proposed by INFORMS)
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
Analytics or Data Science?

53. Analytics Overview

54. Introduction to Big Data Analytics
Not just big!
Volume
Variety
Velocity
More of Big Data and related analytics tools and techniques are covered in Chapter 13.

55. 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
Part V - Big Data and Future Directions for Business Analytics
Chapters 13 & 14

56. End of the Chapter
Questions / Comments…

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