This document discusses business intelligence (BI), including its definition as IT-enabled business decision making based on data analysis, why BI is important for making informed decisions and gaining competitive advantages, the advantages it provides organizations, key technologies that support BI like data warehousing and analytics tools, the common components of BI systems, how BI can be applied in management, stakeholders in BI systems, and an overview of data mining and the tools used.

1. Presented by-
Gaurav Khatri
Devendran S.P
Siddharth Tripathi
Pushpam Shree
Timsi Luthra
Vandana Madhuri Singh

2. CONTENT
 What is Business Intelligence? (Pushpam)
 Why Business Intelligence? (Pushpam)
 Advantages of Business Intelligence (Timsi)
 Technologies Supporting BI (Timsi)
 Components of Business Intelligence (Siddharth)
 Applications of Business Intelligence in
management (Vandana)
 Business Intelligence Stakeholders (Dev)
 Data mining and case presentation (Dev)

3. What is Business Intelligence (BI)?
 IT-enabled business decision making based
on simple to complex data analysis processes.
 It is an architecture and a collection of
integrated operational as well as decision-
support applications and databases that
provide the business community easy access
to business data

4. Why Business Intelligence?
 Make more informed business decisions
 Competitive and location analysis
 Customer behavior analysis
 Targeted marketing and sales strategies
 Business scenarios and forecasting
 Business service management
 Business planning and operation optimization
 Financial management and compliance

5. Advantages of business intelligence
 Enhanced reaction and sensitivity of the
organization toward the customers
 Identification of customer demands
 Capability to respond to market transformations
 Improved optimality within operations
 Effective use and saving of wealth
 Intricate study assisting for future prospects
 Optimum utilization of organizational resources

6. Technologies Supporting BI
 Database systems and database integration
 Data warehousing, data stores and data marts
 Enterprise resource planning (ERP) systems
 Query and report writing technologies
 Data mining and analytics tools
 Decision support systems
 Customer relation management software
 Product lifecycle and supply chain management
systems

7. Components of Business Intelligence
 OLAP (On-Line Analytical Processing)
 Advance Analytics
 Corporate Performance Management
(Portals, Scorecards, Dashboards)
 Data Warehousing and Data Mart
 Data Sources

8. Importance of Business Intelligence
 To Customers
 To Competitor
 To Business Partners
 Economic Environment
 Internal Operations

9. Applications of Business Intelligence
 Measurement – program that creates a hierarchy of
performance metrics and benchmarking that informs
business leaders about progress towards business goals.
 Analytics – program that builds quantitative processes
for a business to arrive at optimal decisions and to
perform business knowledge discovery.
 Reporting – program that builds infrastructure for
strategic reporting to serve the strategic management of
a business. Frequently involves data visualization,
executive information system and OLAP.

10.  Collaboration– program that gets different areas
(both inside and outside the business) to work
together through data sharing and electronic data
interchange.
 Knowledge management – program to make the
company data driven through strategies and
practices to identify, create, represent, distribute,
and enable adoption of insights and experiences
that are true business knowledge. Knowledge
management leads to learning management and
regulatory compliance/compliance.
In addition to above, business intelligence also can
provide a pro-active approach, such as ALARM
function to alert immediately to end-user.

11. Business Intelligence Stakeholders
Applications
Tools
& Analytical
Clients
Platform

12. Data Mining
 Data mining is the process of extracting hidden
patterns from data.
 Important tool to transform data into
information.
 It is commonly used in a wide range of
profiling practices, such as marketing,
surveillance, fraud detection and scientific
discovery.

13. Data Mining Tools
 Analyze the data
 Uncover problems or opportunities hidden in
data relationships
 Form computer models based on their findings
 And then user the models to predict business
behavior – with minimal end-user intervention.