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INTELLIGENT SYSTEMS BUSINESS MOTIVATION BUSINESS INTELLIGENCE

INTELLIGENT SYSTEMS BUSINESS MOTIVATION BUSINESS INTELLIGENCE. M. Gams. Intelligent systems , BI. I N . SOCIETY. ENGINEERING, TECHNOLOGY , BUSINESS, ECONOMY. ARTIFICIAL INTELLIGENCE. Definition. Business intelligence (BI) (Wikipedia)

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INTELLIGENT SYSTEMS BUSINESS MOTIVATION BUSINESS INTELLIGENCE

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  1. INTELLIGENT SYSTEMS BUSINESS MOTIVATIONBUSINESS INTELLIGENCE M. Gams

  2. Intelligent systems, BI IN. SOCIETY ENGINEERING, TECHNOLOGY,BUSINESS, ECONOMY ARTIFICIAL INTELLIGENCE

  3. 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.

  4. 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, artificial intelligence, intelligent systems, machine learning(seminar work rather not including genetic algorithms, decision systems)

  5. COMPETITIVE VIEWPOINT THE LEGATUM PROSPERITY INDEX2018 2017 rating 2018 rating Country 1 1 Norway 7 11 3 9 3 1 4 8 8 2 2 New Zealand 14 2 2 2 1 24 18 17 4 3 3 Finland 12 6 1 11 14 11 1 25 3 4 4 Switzerland 4 10 4 21 13 13 2 4 10 7 5 Denmark 8 8 9 16 5 9 10 18 11 5 6 Sweden 5 13 6 10 22 12 16 7 12 10 7 United Kingdom 16 4 11 18 8 14 12 26 2 8 8 Canada 21 3 8 1 11 17 15 21 19 6 9 Netherlands 6 14 5 7 9 7 5 11 49 12 10 Ireland 10 16 14 5 7 5 6 27 14 13 11 Iceland 1 15 13 3 4 10 31 14 27 14 12 Luxembourg 3 37 7 4 18 15 37 2 5 9 13 Australia 28 9 12 14 2 22 8 12 17 11 14 Germany 11 12 10 19 16 16 20 24 13 15 15 Austria 17 21 15 25 17 8 19 6 9 16 16 Belgium 24 18 16 12 24 30 13 13 22 18 17 United States 13 1 19 23 6 43 9 35 23 21 18 Slovenia 31 41 32 20 20 18 7 34 1 Economy BusinessGovern. Freedom SocialCap. Safety Education Health Nature 21 18 Slovenia31 41(+15) 32 20 20 18 7 34(-5) 1 22 19 Malta 15 54 24 13 10 6 40 20 42 19 20 France 30 17 21 28 34 31 29 15 7 17 21 Singapore 2 5 18 98 15 3 3 1 90 24 22 Hong Kong 20 7 30 31 51 4 23 9 86 23 23 Japan 19 19 17 46 99 2 21 3 39 25 24 Portugal 35 34 25 6 39 20 42 37 35 20 25 Spain 49 31 31 17 26 19 36 22 20 27 26 Estonia 33 25 20 34 69 38 11 44 6 26 27 Czech Republic 25 26 33 30 71 21 14 28 24 31 28 Cyprus 26 33 34 24 28 25 46 30 60 34 29 Mauritius 36 28 26 26 21 46 67 45 46 28 30 Uruguay 52 42 23 8 47 52 63 42 58 29 31 Costa Rica 53 40 27 15 46 72 47 29 32 35 32 Slovakia 37 62 45 41 59 26 26 36 34 32 33 Poland 38 47 38 54 76 23 24 41 47 30 34 Italy 48 68 44 32 41 27 35 39 64 36 35 South Korea 29 32 39 75 78 32 17 19 73 Analogy: soocer team; competition: player, team, coach, league // business, real life Idea: AI will help gain advantage

  6. BUSINESS INTELLIGENCE - theses • With AI methods it is possible to evaluate gain for past events(condition: enough data/information can be obtained if not, results are less valuable or less graded) • With AI approach, in „soft“ areas like business or economy or politics it is possible to evaluate gains • Fornearlyall future actionsit is possible to predict future gainswithbetterprobabilitythanbefore

  7. BI – overview • AI methods, data mining, business AI methods, intelligent systems, agents, MAS • Nash equilibrium, prisoner dilemma, Wooldridge • Bounded rationality, behavioral economics; Herbert A. Simon, Nobel Prize for decision making in economy, Turing award • Agent / crowd predictability, R. Heiner • Robert Merton, Social Theory and Social Structure, self fulfilling prophecy, bank Millingville, 1949

  8. Intelligent Systems Properties • Learning, Flexibility, Adaptation, Explanation, Discovery • Intelligent system, some AI tool – agents (equilibrium, selling, e-commerce, trading ..), user profiling, … • (I)DM, (I)ML

  9. BI practical

  10. 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

  11. 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 WORKDM on business-related dataagent modeling on a business process • PRACTICAL EXAMPLESanalyze efficiency of tax systemspredict stock (share) valuespredict oil pricesdesign a model for bank loansis selling country assets beneficial or not?

  12. 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

  13. 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)

  14. Examples: S. Goonatilake, P. Treleaven:I. S. for Finance and Business 20 years ago substantial increase in ISKiller 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,

  15. 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 equilibrated10 times better per one transaction • (Visa - an neural network – DM and ML prevail)

  16. 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 … NOT

  17. A CASE STUDY: EAST EUROPEAN GOVERNMENT SELLS A COMPANY CECIIS Varazdin, 23.9.2015

  18. Transition  $$$$ CECIIS Varazdin, 23.9.2015

  19. N-DIMENSIONAL DECISION SPACE: PARETO FRONT • - $ - Companies Green – Pareto front Blue – possibilities Red – typical human CECIIS Varazdin, 23.9.2015

  20. AN EXAMPLE OF A BAD DECISION The government gets 50 million Each ear 10 million leave the country In 5 years the gain is lost In 10 years, the country looses 50 million and has lost ownership Real example: banks CECIIS Varazdin, 23.9.2015

  21. Discussion • BI = IS/AI(DM) for business and economy • BI combine advantages of computer systems (cost, availability) with IS methods, simulating some human properties (learning, adapting, reasoning), and achieve better cost/benefit for several tasks in BI • How to use BI? IS/AI/DM (computer intelligence) + BI problem + additional knowledge (economic, BI)

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