1. Business Intelligence: A Managerial Approach (2nd Edition)
Chapter 1:
Introduction to Business Intelligence

2. 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
Describe the business intelligence/business analytics methodology Understand the major issues in implementing business analytics

3. Introduction
Business environment is changing, and its become more complex (pressures).
Force them to respond quickly.
To take a quick decisions they need a relevant amount of data, information and knowledge.

4. Changing Business Environments and Computerized Decision Support
The Business Pressures-Responses-Support Model
The business environment
Organizational responses: be reactive, anticipative, adaptive, and proactive
Computerized support
Closing the Strategy Gap One of the major objectives of BI 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 for achieving them

5. Changing Business Environments and Computerized Decision Support

6. 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

7. 1.2 A Framework for Business Intelligence (BI)
A conceptual ( umbrella) framework for decision support. It combines architecture, databases (or data warehouse), analytical tools and applications .
BI Objective: enable interactive access to data to enable manipulation of data and to give business managers to take a good decision.

8. 1.2 A Framework for Business Intelligence (BI)
The process of BI is based on the transformation of data to information, then to decisions, and finally to actions.

9. A Framework for Business Intelligence

10. 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”
2005+ Inclusion of AI (Artificial Intelligent) and Data/Text Mining capabilities; Web-based Portals/Dashboards
2010s - yet to be seen

11. A Framework for Business Intelligence (BI)
The Origins and Drivers of Business Intelligence
Organizations are being compelled to capture, understand, and harness their data to support decision making in order to improve business operations
Managers need the right information at the right time and in the right place

12. A Framework for Business Intelligence (BI)
BI’s Architecture and Components
Data Warehouse
Business Analytics
Business Performance Management (BPM)
User Interface

13. A Framework for Business Intelligence (BI)

14. A Framework for Business Intelligence (BI)
BI’s Architecture and Components
1) Data Warehouse (Data Sources)
Data obtained from operational systems needed to support decision making.
the data warehouse included only historical or current data that were organized and summarized, so end users could easily view or manipulate data and information.

15. A Framework for Business Intelligence (BI)
BI’s Architecture and Components
2 ) Business Analytics
-a collection of tools for manipulating, mining, and analyzing the data in the data warehouse;
Create on-demand reports and queries and analyze data (originally called Online Analytical Processing – OLAP)
Automated decision systems
Data Mining: looks for hidden patterns in a collection of data which can be used to predict future behavior.

16. A Framework for Business Intelligence (BI)
BI’s Architecture and Components
3) Business performance management (BPM)
applications and methodology ,BPM extends the monitoring, measuring, and comparing of sales, profit, cost, profitability, and other performance indicators by introducing the concept of management and feedback.
(Monitoring , measuring and comparing )

17. A Framework for Business Intelligence (BI)
BI’s Architecture and Components
4) User Interface: Dashboards and Other Information Broadcasting Tools
Dashboards
A visual presentation of critical data for executives to view. It allows executives to see hot spots in seconds and explore the situation
Examples of dashboards and scorecards:

19. A Framework for Business Intelligence (BI)
The Benefits of BI
Time savings
Single version of truth
Improved strategies and plans
Improved tactical decisions
More efficient processes
Cost savings
Faster, more accurate reporting
Improved decision making
Improved customer service
Increased revenue
Many benefits are intangible

20. Automated Decision Making (ADS)
It’s a rule based systems that provide a solution, usually in one functional area
E.g.( finance, manufacturing ) to a specific repetitive managerial problem.
Its used in the Airline industry, dynamicly price ticket based on demands.

22. Event-Driven Alerts
Its an example of ADS, which is a warning or action that is activated when a predefined or unusual event occur.
For example: credit card comp. make a predictive analysis models to identify cases possible fraud.

23. 1.3 Intelligence Creation and Use
Steps Involved
 Data warehouse deployment
 Creation of intelligence
 Identification and prioritization of BI projects
 By using ROI and TCO (cost-benefit analysis)
 This process is also called BI governance
BI Governance
 Who should do the prioritization?
 Partnership between functional area heads and leaders(middles)
 Partnership between customers and providers

24. BI Governance Issues/Tasks
1. Create categories of projects (investment, business opportunity, strategic, mandatory, etc.) 2. Define criteria for project selection 3. Determine and set a framework for managing project risk 4. Manage and leverage project interdependencies 5. Continuously monitor and adjust the composition of the portfolio

25. Intelligence and Espionage
Stealing corporate secrets, CIA, …
 Intelligence vs. Espionage
Intelligence
The way that modern companies ethically and legally organize themselves to glean as much as they can from their customers, their business environment, their stakeholders, their business processes, their competitors, and other such sources of potentially valuable information
Problem – too much data, very little value
 Use of data/text/Web mining (see Chapter 4, 5)

26. 1.4 Transaction Processing Versus Analytic Processing (OLTP Vs OLAP)
Transaction (OLTP) processing systems are constantly involved in handling updates (add/edit/delete) to what we might call operational databases.
 ATM withdrawal transaction, sales order entry via an ecommerce site – updates DBs
 Online analytic processing (OLTP) handles routine on-going business
 The main goal is to have high efficiency

27. Transaction Processing Versus Analytic Processing
Online analytic processing (OLAP) systems
are involved in extracting information from data stored by OLTP systems and Analyze them.
 Often built on top of a data warehouse where the data is not transactional
 Main goal is effectiveness (and then, efficiency)
provide correct information in a timely manner

28. 1.5 Successful BI Implementation
Impelementing BI can be lengthy, expensive and failure prone.
The Typical BI User: the successful of BI must benefit to the enterprise as whole.
one important characteristic of a company that excels in its approach to BI is proper appreciation for different classes of potential users.

29. 1.5 Successful BI Implementation
Appropriate Planning and Alignment with Business Strategy
To be successful, BI must be aligned with the company’s business strategy.
 BI cannot/should not be a technical exercise for
the information systems department.
 BI should help execute the business strategy and not be an impediment for it!

30. 1.5 Successful BI Implementation
Real-time, On-demand BI
The demand for “real-time” BI is growing!
Traditional BI use a static data but Real time BI use a dynamic online data.

31. Issues for Successful BI
1) Developing vs. Acquiring BI systems  Developing everything from scratch  Buying/leasing a complete system  Using a shell BI system and customizing it  Use of outside consultants?

32. Issues for Successful BI
2) Justifying via cost-benefit analysis
 It is easier to quantify costs
 Harder to quantify benefits
 Most of them are intangibles

33. Issues for Successful BI
3) Security and Privacy
 Still an important research topic in BI
 How much security/privacy?
4) Integration of Systems and pplications
 BI must integrate into the existing IS
- Often sits on top of ERP, SCM, CRM systems
 Integration to outside (partners of the
extended enterprise) via internet –
- customers, vendors, government agencies, etc.

34. 1.6 Major BI Tools and Techniques
Tool categories ( table 1.3)
 Data management (DBMS)
 Reporting, status tracking (OLAP)
 Visualization (DASHBOARD)
 Strategy and performance management (BPM)
 Business analytics ( DATA MINING)
 Social networking & (Web 2.0)
 New/advanced tools/techniques to handle
massive data sets for knowledge discovery