INTELLIGENT SYSTEMS BUSINESS MOTIVATION BUSINESS INTELLIGENCE M. Gams

Intelligent systems, BI 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 Intelligent system, some AI tool

BI (IS) areas 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 BUSINESS FINANCE ECONOMY Related to a person, institution, country, continent … Anything of this related to IS, i.e. using AI methods RECOMMENDED METHODS FOR SEMINAL WORK DM on business-related data agent modeling on a business process 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