Download Business intelligence - Department of Intelligent Systems

INTELLIGENT SYSTEMS
BUSINESS MOTIVATION
BUSINESS INTELLIGENCE
M. Gams
Intelligent systems, BI
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IN. SOCIETY
ENGINEERING, TECHNOLOGY,
BUSINESS, ECONOMY
ARTIFICIAL INTELLIGENCE
Definition
Business intelligence (BI)
(Wikipedia)
mainly refers to computer-based techniques used in
identifying, extracting, and analyzing business data, such as
sales revenue by products and/or departments, or by
associated costs and incomes.
BI technologies provide historical, current and predictive
views of business operations. Common functions of business
intelligence technologies are reporting, online analytical
processing, analytics, data mining, process mining, complex
event processing, business performance management,
benchmarking, text mining and predictive analytics.
Definition
Business intelligence (BI)
(Wikipedia)
Sometimes used as a synonym for competitive intelligence,
because they both support decision making, but BI uses
technologies, processes, and applications to analyze mostly
internal, structured data and business processes while
competitive intelligence gathers, analyzes and disseminates
information with a topical focus on company competitors.
For us, BI including some AI tool (seminar work rather not
including genetic algorithms, decision systems)
Properties
Learning
 Flexibility
 Adaptation
 Explanation
 Discovery
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Intelligent system,
some AI tool
BI (IS) areas
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Support for BI/IS solutions: BI/IS governance, BI/IS strategies, BI/IS
maturity models, BI/IS success factors, and BI/IS performance
Emerging trends in BI: pervasive BI, BI 2.0 (social media and BI),
and mobile BI
Real time data warehousing und operational BI
Applications of BI, such as customer relationship management and
business performance management
Data warehousing and data integration
Predictive and advanced analytics, and data visualization
Data, text and web mining for BI
Management of knowledge and business process improvement
Social and behavioral issues , and social media usage
Capturing and sharing knowledge in social networks and distributed
contexts
Design, development, adoption, usage, and impact of IS on KI
Inter-organizational IS BI systems, such as in the supply chain and
learning
BI (IS) APPLICATIONS
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BUSINESS
 FINANCE
 ECONOMY
Related to a person, institution, country, continent …
Anything of this related to IS, i.e. using AI methods
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RECOMMENDED METHODS FOR SEMINAL WORK
DM on business-related data
agent modeling on a business process
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PRACTICAL EXAMPLES
analyze efficiency of tax systems
predict stock (share) values
predict oil prices
design a model for bank loans
is selling country assets beneficial or not?
Intelligent systems
Engineering, invisible intelligence
Practical directions, real-life problems
Verified AI methods: rule-based systems, trees,
expert systems, fuzzy systems, neural networks,
genetic algorithms, hybrid systems
Intelligent systems often simulate human
bureaucrats, expert systems simulate experts
Motivation / business
People are expensive (to buy or maintain),
computers cheap: computers work 24 hours a
day, no vacations, network accessibility is
worldwide, only 3% microprocessors in
computers, an average car 16
microprocessors, exponential trend (faster,
cheaper, more applications)
Intelligent systems are more friendly, more
flexible than classical systems (not truly
intelligent, just a bit more than classical)
S. Goonatilake, P. Treleaven:
I. S. for Finance and Business
•20 years ago substantial increase in IS
Killer applications - breakthrough
•Visa, 6 G trans. ann., 550G$, security;
American Express, 15$ > 1.4$
•typical: lots of data, new AI and HW cap.
•quality improvement, lower costs,
Killer application
American Express, Visa
Authorizer’s Assistant - an expert system
before: simple rigid rules, majority left to
human supervisors, many people with
different performance
Then new: an expert / intelligent system
with many rules, copies expert supervisors,
faster, cheaper, more equilibrated
15$ > 1.4$ per one transaction
(Visa - an neural network – DM and ML
prevail)
Benefits
The key question – trust – can IS be
trusted - obviously good enough (actually
as good as average humans)
Intelligent systems enabled
organizational changes in terms of HW,
SW and humans
Work done better and faster, more
profits, cheaper transactions
Less employed, more work done by
computers
Problem - unemployment
Discussion
Intelligent systems (IS) apply AI methods and
introduce intelligent services
BI = IS for business and economy
IS combine advantages of computer systems
(cost, availability) with some human properties
(simple engineering intelligence – learning,
adapting, reasoning), and achieve better
cost/benefit for several tasks