1. Intelligent Information
Systems

2. Evolutionary
Step
Business Question
Enabling
Technologies
Product
Providers
Characteristics
Data
Collection
(1960s)
"What was my total
revenue in the last
five years?"
Computers, tapes
, disks
IBM, CDC
Retrospective,
static data
delivery
Data Access
(1980s)
"What were unit
sales in New England
last March?"
Relational
databases
(RDBMS), Struct
ured Query
Language
(SQL), ODBC
Oracle, Sybas Retrospective,
e, Informix, I dynamic data
BM, Microsoft delivery at
record level
Data
Warehousing
&
Decision
Support
(1990s)
"What were unit
sales in New England
last March? Drill
down to Boston."
On-line analytic
processing
(OLAP),
multidimensional
databases, data
warehouses
Pilot,
Comshare,
Arbor,
Cognos,
Microstrategy
Retrospective,
dynamic data
delivery at
multiple levels
Data Mining
(Emerging
Today)
"What’s likely to
happen to Boston
unit sales next
month? Why?"
Advanced
algorithms,
multiprocessor
computers,
massive
databases
Pilot,
Lockheed,
IBM, SGI,
numerous
start-ups
(nascent
Prospective,
proactive
information
delivery

3. • Standard database operations present
results to the user as they existed in
databases
• A report showing the breakdown of sales
by
product
line
and
region
is
straightforward for the user to understand
because they intuitively know that this kind
of information already exists in the database

4. 

Business Intelligence (BI) tools such as
query and reporting are used to answer
questions by the user
These questions deal primarily with the
analysis of historical results and trends
- what were our sales in the past month in a
certain region?
- what were our most profitable products?
- which of our suppliers were most reliable?
- which customers generated the most
revenue?

5. • Extracts information from a database that the
user did not know existed
• Relationships
between
variables
and
customer behaviour that are non-intuitive is
the vital information that data mining extracts
• Since the user does not know beforehand
what the data mining process has discovered,
it is a much bigger leap to take the output of
the system and translate it into a solution for a
business problem

6. Datamining tools provide answers to questions related
to the detection of previously undetected patterns and
are undirected in nature such as:
and
cost-
- Who are our best suppliers or most profitable customers?
- Should we extend credit to a particular customer?
- Which customers are likely to become profitable, when
to what extent?
- How do we optimally allocate resources to ensure
profitability and growth targets?
- What are the root causes of quality issues and can we
effectively minimize them?
- What factors or combinations of factors are directly
impacting marketing campaigns?

7. • Intelligence is the aptitude to learn,
comprehend, or to counter new or trying
situations
• It is the skillful use of reason and the capacity
to apply knowledge to influence one's
environment or to think conceptually
• Business intelligence is a set of notions,
methods, and practices, which improves
business decisions. It uses information from
multiple sources and applies experience and
assumptions that helps in understanding
accurately the intricacies of business dynamics.

8. • Business Intelligence (coined by
Gartner in the late 1980s) is “a usercentered process that includes accessing
and exploring information, analyzing this
information, and developing insights and
understanding, which leads to improved
and informed decision making.”

9. • BI is the means by which organizations interpret the
sea of organizational data to derive insights that are
critical to competing in the new economy
• BI aids in:
- a deeper understanding of customer and partner
relationships
- indicating key performance indicators
- a consistent view of the organization from the executive
level to the front line
By translating these insights into action companies
can:
•
- increase
profits
- respond more quickly to changing market demands
- improve accountability by giving every employee an
accurate view of the organization

11. 
The track - analyze - model decide –
monitor loop is referred to as the
closed loop model for business
intelligence

12. • Track extracting, transforming, loading
(ETL), and integrating data into a data
warehouse as well as monitoring data in a
real-time or near real-time environment
• Transaction capturing systems or
operational systems capture data which is
later transformed, integrated, and loaded
into a data warehouse

13. • Analyze (analyzing data using BI tools)
- query and reporting, multi-dimensional analysis, and data mining
- Simple analysis methods like regression, co-relation , factor analysis etc.
are available in MS-EXCEL , ORACLE , SPSS, etc., .
- Data mining tools are available with software packages like SPSS, SAS,
Intelligent Miner, and Data Mind

14. • Model
- formulating models for forecasting,
optimization, and scenario planning
- utilizing advanced analytics tools
•A
model (a rule or a hypotheses) is made
based on the patterns discovered by data
mining tools

15. •Decide
- arriving at a decision based on analysis and preexisting or newly developed models
- decision support systems use the models
developed as a result of data-mining and business
intelligence modeling processes for decision
making

16. •Act
- a business manager uses the business analysis results
to take an action (e.g., launching a new marketing
campaign based on the analysis of previous campaign
results, customer behavior, new promotional plan or
inventory levels)
- approving or denying a request for credit based on
past financial activity
- re-negotiating sourcing contracts based on supplier
delivery trends, product quality, and warranty activity
trends, adjusting the type of data being tracked for
analysis, etc., .

17. • Identify buying behavior from customers
• Find
associations
among
customer
demographic characteristics
• Predict responses to mailing campaigns
• Market basket analysis

18. • Detect patterns of fraudulent credit card use
• Identify loyal customers
• Predict customers likely to change their credit
card affiliation
• Determine credit cards spending by customer
groups
• Find hidden correlations between different
financial indicators
• Identify stock trading rules from historical data

19. • Claims analysis
• Predict which customers will buy new
policies
• Identify behaviour patterns of risky
customers
• Identify fraudulent behaviour

20. • Determine the distribution schedules
among
outlets
• Analyze loading patterns

21. • Successful BI architecture has
four parts
-
information architecture The information
architecture defines what business application
systems you need to access, report, and analyze
information to enable business decision making.
- data architecture The data architecture defines the
data, source systems and framework for
transforming data into useful information.
- technical architecture The technical architecture
defines the technology of the products and
infrastructure.
- product architecture The product architecture
includes the actual products used

24. 
Phase I Data Preparation:
- Data Integration
- Data Selection and Pre-analysis
- Data Integration refers to the process of merging data
which
typically resides in an operational environment having
multiple
files or databases

Phase II Data Mining processor:
- accesses a Data Warehouse that uses a relational database
such
as DB2 for AIX/6000
- access is done through a standard SQL interface using a
middleware product which allows mining of data from
multiple
sources

Phase III Presentation of facts and follow up:

25. • a class of computer software built around
mathematical
models
and
algorithms
(procedures) which, by converting data into
information and intelligence, help a manager
make better decisions for his organization
• DSS are interactive computer based systems and
subsystems intended to help decision makers
use communication technologies , data ,
documents , knowledge and/ or models to
successfully complete decision process tasks
• DSS can be divided into five basic tasks:
- communications-driven DSS
- data-driven DSS
- knowledge-driven DSS
- document-driven DSS
- model-driven DSS