Transcript business intelligence (BI)

Business Intelligence
Chapter 1: Introduction to Business
Intelligence
Matthew J. Liberatore
Fall 2009
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

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.
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
Changing Business Environments
and Computerized Decision Support
Business Environment Factors
FACTOR
Markets
Consumer
demand
Technology
Societal
DESCRIPTION
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
Desire for customization
Desire for quality, diversity of products, and speed of delivery
Customers getting powerful and less loyal
More innovations, new products, and new services
Increasing obsolescence rate
Increasing information overload
Social networking, Web 2.0 and beyond
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
A Framework for
Business Intelligence (BI)

business intelligence (BI)
A conceptual 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.
A Framework for
Business Intelligence
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
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
A Framework for
Business Intelligence (BI)
 BI’s




Architecture and Components
Data Warehouse
Business Analytics
Business Performance Management (BPM)
User Interface
A Framework for
Business Intelligence (BI)
A Framework for
Business Intelligence (BI)
 BI’s

Architecture and Components
Data Warehouse (Data Sources)
• Data obtained from operational systems needed to
support decision making
A Framework for
Business Intelligence (BI)
 BI’s
Architecture and Components
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: rule – based

App. Case 1.1 – price setting example
• Data Mining: a class of information analysis based
on databases that looks for hidden patterns in a
collection of data which can be used to predict
future behavior
A Framework for
Business Intelligence (BI)
 BI’s

Architecture and Components
business (or corporate) performance
management (BPM)
A component of BI based on the balanced
scorecard methodology, which is a
framework for defining, implementing, and
managing an enterprise’s business strategy
by linking objectives with factual measures
(Monitoring , measuring and comparing )
A Framework for
Business Intelligence (BI)
 BI’s

Architecture and Components
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:
http://www.idashboards.com/?gclid=CIDDrpLR05QC
FQNaFQodSWDQkQ
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
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.
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.
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
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
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)

Transaction Processing Versus
Analytic Processing
(OLTP Vs OLAP)

Transaction 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 ongoing business
ERP, SCM, CRM systems generate and store data in
OLTP systems
The main goal is to have high efficiency
Transaction Processing Versus
Analytic Processing

Online analytic processing (OLAP) systems
are involved in extracting information from data stored by
OLTP systems
Routine sales reports by product, by region, by
sales person, etc.
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
More on OLAP will be covered in Chapter 2
1.5 Successful BI Implementation
 Impelementing
BI can be lengthy,
expensive and failure.
 The
Typical BI User: the successful of BI
must benefit to the enterprise as whole.
- Many of whom should be involved from the
outset of (DW “datawarehouse).
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 changes the way a company conducts business
by:
improving business processes, and
transforming decision making to a more
data/fact/information driven activity.
 BI should help execute the business strategy and
not be an impediment for it!
Real-time, On-demand BI
The demand for “real-time” BI is growing!
 Is “real-time” BI attainable?
 Technology is getting there…
Automated, faster data collection (RFID,
sensors,… )
Database and other software technologies (agent,
SOA, …) are advancing
Telecommunication infrastructure is improving
Computational power is increasing while the cost
for these technologies is decreasing
 Trent -> Business Activity Management

Issues for Successful BI


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?
Justifying via cost-benefit analysis
It is easier to quantify costs
Harder to quantify benefits
Most of them are intangibles
Issues for Successful BI
 Security
and Privacy
Still an important research topic in BI
How much security/privacy?
 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.
Major BI Tools and Techniques

Tool categories
Data management
Reporting, status tracking
Visualization
Strategy and performance management
Business analytics
Social networking & Web 2.0
New/advanced tools/techniques to handle
massive data sets for knowledge discovery