1. Business Intelligence Systems (Decision Support Systems)
Chapter 5
Business Intelligence Systems
(Decision Support Systems)

2. Agenda Business Intelligence (BI) Types of data
History of BI
Evolution of BI
Architecture of BI
Benefits of BI
Types of data
Structured
Semi-Structured
Unstructured
Decision Support Systems (DSS)
Definition
Characteristics and capabilities of DSS
DSS Components

3. Business Intelligence (BI)
BI is an umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies
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

4. The Evolution of BI Capabilities

5. Components in a BI Architecture
The data warehouse is a large repository of well-organized historical data
Business analytics are the tools that allow transformation of data into information and knowledge
Business performance management (BPM) allows monitoring, measuring, and comparing key performance indicators
User interface (e.g., dashboards) allows access and easy manipulation of other BI components

6. A High-Level Architecture of BI

7. Faster, more accurate reporting Improved decision making
The Benefits of BI
The ability to provide accurate information when needed, including a real-time view of the corporate performance and its parts.
Faster, more accurate reporting
Improved decision making
Improved customer service
Increased revenue

8. The DSS–BI Connection (1)
First, their architectures are very similar because BI evolved from DSS
Second, DSS directly support specific decision making, while BI provides accurate and timely information, and indirectly support decision making
Third, BI has an executive and strategy orientation, especially in its BPM and dashboard components, while DSS, in contrast, is oriented toward analysts

9. The DSS–BI Connection (2)
Fourth, most BI systems are constructed with commercially available tools and components, while DSS is often built from scratch
Fifth, DSS methodologies and even some tools were developed mostly in the academic world, while BI methodologies and tools were developed mostly by software companies
Sixth, many of the tools that BI uses are also considered DSS tools (e.g., data mining and predictive analysis are core tools in both)

10. The DSS–BI Connection (3)
Although some people equate DSS with BI, these systems are not, at present, the same
Some people believe that DSS is a part of BI—one of its analytical tools
Others think that BI is a special case of DSS that deals mostly with reporting, communication, and collaboration (a form of data-oriented DSS)
BI is a result of a continuous revolution and, as such, DSS is one of BI's original elements
Management Support Systems
MSS = BI and/or DSS

11. What is Business Intelligence (BI)?
Business intelligence is an umbrella term that refers to competencies, processes, technologies, applications and practices used to support evidence-based decision making in organizations.
In the widest sense it can be defined as a collection of approaches for gathering, storing, analyzing and providing access to data that helps users to gain insights and make better fact-based business decisions.

12. What is BI used for?
Analyzing customer behaviors, buying patterns and sales trends.
Measuring, tracking and predicting sales and financial performance
Budgeting and financial planning and forecasting
Tracking the performance of marketing campaigns
Optimizing processes and operational performance
Improving delivery and supply chain effectiveness
Web and e-commerce analytics
Customer relationship management
Risk analysis
Strategic value driver analysis

13. The Basics of Business Intelligence1 4 2 3

14. Gathering Data
Collecting or accessing data that can be used to inform decision making.
It can come in many formats and basically refers to the automated measurement and collection of performance data.
Relevant data should be collected in the right way at the right time.
Uncontrolled data can jeopardize the entire BI efforts that might follow.

15. Storing Data
Data is filed and stored in appropriate ways to ensure it can be found and used for analysis and reporting.
Data can be stored under different categories; also called data marts or data-warehouse access layers.
Good data storage starts with the needs and requirements of the end users and a clear understanding of what they want to use the data for.

16. Analyzing Data
Inspect, transform or model data in order to gain new insights that will support our business decision making.
Data analysis comes in many different formats and approaches, both quantitative and qualitative.
Analysis techniques includes the use of statistical tools, data mining approaches as well as visual analytics or even analysis of unstructured data such as text or pictures.

17. Providing Access
Access to data is needed to perform analysis or to view the results of the analysis.
Software tools allow end-users to perform data analysis
Data are provided through reporting, dashboard (control panel) and scorecard (note, card, memo) applications.

18. Becoming an ‘Intelligent Company’1 5 2 3 4

19. Becoming an ‘Intelligent Company’
Step 1: More intelligent strategies – by identifying strategic priorities and agreeing your real information needs.
Step 2: More intelligent data – by creating relevant and meaningful performance indicators as well as qualitative management information linked back to your strategic information needs.
Step 3: More intelligent insights – by using good evidence to test and prove ideas and by analyzing the data to gain robust and reliable insights.
Step 4: More intelligent communication – by creating informative and engaging management information packs and dashboards that provide the essential information, packaged in a way that is targeted and easy-to-understand.
Step 5: More intelligent decision making – by fostering an evidence- based culture of turning information into actionable knowledge and real decisions.

20. Types of Data Structured Semi-Structured Unstructured
Both inputs and outputs are clear
Semi-Structured
Either inputs or outputs are unclear
Unstructured
Both inputs and outputs are unclear

21. A Decision Support Framework (1)

22. A Decision Support Framework (2)
Degree of Structuredness (Simon, 1977)
Decisions are classified as
Highly structured (a.k.a. programmed)
Semi-structured
Highly unstructured (i.e., non-programmed)
Types of Control (Anthony, 1965)
Strategic planning (top-level, long-range)
Management control (tactical planning)
Operational control

23. Simon’s Decision-Making Process

24. How Decisions Are Supported
Artificial Neural Networks (ANN)
Management Information Systems (MIS)
Online Analytical Processing (OLAP)
Expert Systems (ES)
Enterprise Resource Planning (ERP)
(ESS)
Software Configuration Management (SCM) is the task of tracking and controlling changes in the software, part of the larger cross-discipline field of configuration management
Customer Relationship Management (CRM) is a system for managing a company’s interactions with current and future customers. It often involves using technology to organize, automate and synchronize sales, marketing, customer service, and technical support.
Knowledge Valuation System (KVS)
Knowledge Managmement System (KMS)

25. Classical Definitions of DSS
Interactive computer-based systems, which help decision makers utilize data and models to solve unstructured problems"
- Gorry and Scott-Morton, 1971
It is a computer-based support system for management decision makers who deal with semi-structured problems
- Keen and Scott-Morton, 1978

26. DSS as an Umbrella Term
The term DSS can be used as an umbrella term to describe any computerized system that supports decision making in an organization
E.g., an organization wide knowledge management system; a decision support system specific to an organizational function (marketing, finance, accounting, manufacturing, planning, SCM, etc.)

27. High-Level Architecture of a DSS

28. Decision Support Systems (1)
Systems designed to support managerial decision-making in unstructured problems
More recently, emphasis has shifted to inputs from outputs
Mechanism for interaction between user and components
Usually built to support solution or evaluate opportunities

29. Decision Support Systems (2)
A DSS is a methodology that supports decision-making.
It is:
Flexible
Adaptive
Interactive
GUI-based
Iterative
Employs modeling.

30. Key Characteristics and Capabilities of DSS

31. Characteristics and Capabilities of DSS (1)
Provide support in semi-structured and unstructured situations, includes human judgment and computerized information
Support for various managerial levels
Support to individuals and groups
Support to interdependent and/or sequential decisions
Support all phases of the decision-making process
Support a variety of decision-making processes and styles
(more)

32. Characteristics and Capabilities of DSS (2)
Are adaptive
Have user friendly interfaces
Goal: improve effectiveness of decision making
The decision maker controls the decision-making process
End-users can build simple systems
Utilizes models for analysis
Provides access to a variety of data sources, formats, and types
Decision makers can make better, more consistent decisions in a timely manner

33. Components of DSS
Subsystems:
Data management
Managed by DBMS
Model management
User interface
Knowledge Management and organizational knowledge base

34. Data Management Subsystem
Components:
Database
Database management system
Data directory
Query facility

35. Database
Interrelated data extracted from various sources, stored for use by the organization, and queried
Internal data, usually from TPS
External data from government agencies, trade associations, market research firms, forecasting firms
Private data or guidelines used by decision-makers

36. Database Management System
Extracts data
Manages data and their relationships
Updates (add, delete, edit, change)
Retrieves data (accesses it)
Queries and manipulates data
Employs data dictionary

37. What are the major functions (capabilities) of DBMS?
Storage, retrieval, and control are the three basic functions.
The DBMS manages the database: organize, extract/access, modify, delete, and catalogue data.

38. Data Directory Catalog of all data Contains data definitions
Answers questions about the availability of data items
Source
Allows for additions, removals, and alterations

39. Data Directory
Extraction: is to capture data from several sources, filter them, summarize, condense, and reorganize the data. 
The function of a query facility is to provide the basis for access to data. Accepts requests, checks for feasibility, provides answers.
The function of a directory is a catalog of all data in the database. It includes data definitions.

40. Model Management Subsystem
Components:
Model base
Model base management system
Modeling language
Model execution, integration, and command processor

41. Models Strategic models
Support top management's strategic planning. For example, examination of acquisitions, diversifications, and mergers.
Tactical models
Support mainly middle management in resource allocation and in control. For example, make or buy decisions or devising a major promotion plan.

42. Types of Models Operational Supports daily activities Analytical
Used to perform analysis of data

43. Model Base Management System
Functions:
Model creation
Model updates
Model data manipulation
Model directory:
Catalog of models
Definitions

44. Model Management Activities
Model execution
Controls running of model
Model command processor
Receives model instructions from user interface
Routes instructions to MBMS or module execution or integration functions
Model integration
Combines several models’ operations

45. User Interface System Knowledge-based system Data management and DBMS
Model management and MBMS
User Interface Management System (UIMS)
Natural Language Processor
Input
Action
Languages
Output
Display
Language
Users
Printers, Plotters
PC Display

46. User Interface Management System
GUI
Natural language processor
Interacts with model management and data management subsystems
Examples
Speech recognition
Display panel

47. User Interface Management System
A user interface covers all aspects of the communications between a user and the MSS.
Most of the power, flexibility, and ease-of-use characteristic of MSS are derived from this component.
It is the part of the system that the user sees, to him/her, it is the system.

48. Knowledge-Based Management System
Expert or intelligent agent system component
Complex problem solving
Enhances operations of other components

49. DSS Hardware De facto standard Web server with DBMS:
Operates using browser
Data stored in variety of databases
Can be mainframe, server, workstation, or PC
Any network type
Access for mobile devices

50. Holsapple and Whinston
DSS Classifications
Alter
Extent to which outputs can directly support or determine the decision
Data oriented or model oriented
Holsapple and Whinston
Text oriented, database oriented, spreadsheet oriented, solver oriented, rule oriented, or compound

51. DSS Classifications Intelligent Descriptive Procedural Reasoning
Linguistic
Expert-system based
Adaptive
Hackathorn and Keen
Personal support, group support, or organizational support

52. Custom made vs. vendor ready made
DSS Classifications
GSS v. Individual DSS
Decisions made by entire group or by alone decision maker
Custom made vs. vendor ready made
Generic DSS may be modified for use
Database, models, interface, support are built in
Addresses repeatable industry problems
Reduces costs

53. List the major components of DSS and briefly define each of them.
DSS Components
List the major components of DSS and briefly define each of them.
The major components are:
Data management; includes a database and its management system.
Model management; includes models and their management system.
Knowledge base; includes artificial intelligence enhancements to the other components.
The user; he or she is the decision maker.

54. DSS Users (1) List and describe the major classes of DSS users.
The major classes of DSS users are:
Management (user, decision-maker) - looking for more user-friendly systems that can do more general analysis and aid in decision making.
Staff (intermediaries) - are looking for more detailed- oriented system and are willing to use more complex system. Staff acts as intermediary between MSS and manager. Different intermediaries:
Staff assistants have specialized knowledge about management problems and some experience with the decision support technology.

55. DSS Users (2)
An expert tool user is skilled in the application of one or more types of specialized problem- solving tools. The expert tool user performs the tasks the problem solver does not have the technical skills to do.
Business (system) analysts have a general knowledge of the application area, a formal business administration education, and considerable skill in DSS construction tools.
Facilitator in GSS controls and coordinates the software of group DSS.

56. What types of support are provided by DSS? DSS provides:
DSS Support
What types of support are provided by DSS?
DSS provides:
Support in semistructured and unstructured situations
Support by bringing human judgment and computerized information together.
Support for various managerial levels, ranging from top executives to line managers.

57. DSS Support
Support to individuals as well as groups, since less structured problems sometimes require several individuals from different departments and organizational levels.
Support to several interdependent and or sequential decisions.
Support in all phases of the decision-making process: intelligence, design, choice, and implementation.