Presentation is loading. Please wait.

Presentation is loading. Please wait.

McGraw-Hill/Irwin Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter 4 Analytics, Decision Support, and Artificial Intelligence:

Similar presentations

Presentation on theme: "McGraw-Hill/Irwin Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter 4 Analytics, Decision Support, and Artificial Intelligence:"— Presentation transcript:

1 McGraw-Hill/Irwin Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter 4 Analytics, Decision Support, and Artificial Intelligence: Brainpower for Your Business McGraw-Hill/Irwin Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved.

2 4-2 STUDENT LEARNING OUTCOMES 1. Compare and contrast decision support systems and geographic information systems. 2. Describe the decision support role of specialized analytics (predictive and text analytics). 3. Describe the role and function of an expert system in analytics.

3 4-3 STUDENT LEARNING OUTCOMES 4. Explain why neural networks are effective decision support tools. 5. Define genetic algorithms and the types of problems they help solve. 6. Describe data-mining agents and multi-agent systems.

4 4-4 ONLINE LEARNING Notice the increase in online learning and the decrease in traditional enrollments.

5 4-5 Questions 1. Have you taken or are taking an online course? Fully online or hybrid? 2. Why do students opt to take online courses over traditional classroom courses? 3. Is this transformation occurring at the K-12 level?

6 4-6 INTRODUCTION  Businesses make decisions everyday  Some big and some small  Many IT tools can aid in the decision-making process  Analytics is now key to the success of any business

7 4-7 CHAPTER ORGANIZATION 1. Decisions and Decision Support  Learning outcome #1 2. Geographic Information Systems  Learning outcome #1 3. Data-Mining Tools and Models  Learning Outcome #2 4. Artificial Intelligence  Learning outcomes #3, 4, and 5 5. Agent-Based Technologies  Learning outcome #6

8 4-8 DECISIONS AND DECISION SUPPORT Carry out the chosen solution and monitor the results Examine the merits of each solution and choose the best one Consider ways of solving the problem Find or recognize the problem, need, or opportunity

9 4-9 Types of Decisions You Face  Structured decision – processing a certain information in a specified way so you always get the right answer  Nonstructured decision – may be several “right” answers, without a sure way to get the right answer  Recurring decision – happens repeatedly  Nonrecurring (ad hoc) decision – one you make infrequently

10 4-10 Types of Decisions You Face EASIEST MOST DIFFICULT

11 4-11 Decision Support Systems  Decision support system (DSS) – a highly flexible and interactive system that is designed to support decision making when the problem is not structured  Decision support systems help you analyze, but you must know how to solve the problem, and how to use the results of the analysis

12 4-12 Components of a DSS  Model management component – consists of both the DSS models and the model management system  Data management component – stores and maintains the information that you want your DSS to use  User interface management component – allows you to communicate with the DSS

13 4-13 Components of a DSS

14 4-14 GEOGRAPHIC INFORMATION SYSTEMS  Geographic information system (GIS) – DSS designed specifically to analyze spatial information  Spatial information is any information in map form  Businesses use GIS software to analyze information, generate business intelligence, and make decisions

15 4-15 Google Earth as a GIS

16 4-16 DATA-MINING TOOLS AND MODELS  Business need IT-based analytics tools  Databases and DBMSs  Query-and-reporting tools  Multidimensional analysis tools  Digital dashboards  Statistical tools  GISs  Specialized analytics  Artificial intelligence Our remaining focus

17 4-17 Data-Mining Tools and Models Support  Association/dependency modeling – cross-selling opportunities, recommendation engine effectiveness  Clustering – groups of entities that are similar (without using known structures)  Classification – use historical data to derive future inferences

18 4-18 Data-Mining Tools and Models Support  Regression – find corollary and often causal relationships between data sets  Summarization – basic, but powerful  Sums, averages  Standard deviations  Histograms, frequency distributions

19 4-19 Predictive Analytics  Predictive analytics – highly computational data-mining technology that uses information and business intelligence to build a predictive model for a given business application  Insurance, retail, healthcare, travel, financial services, CRM, SCM, credit scoring, etc

20 4-20 Predictive Analytics  Prediction goal – the question you want addressed by the predictive analytics model  Prediction indicator – specific measurable value based on an attribute of the entity under consideration

21 4-21 Predictive Analytics

22 4-22 Predictive Analytics Example  Prediction goal – What customers are most likely to respond to a social media campaign within 30 days by purchasing at least 2 products in the advertised product line?  Prediction indicators  Frequency of purchases  Proximity of date of last purchase  Presence on Facebook and Twitter  Number of multiple-product purchases

23 4-23 Text Analytics  Text analytics – uses statistical, AI, and linguistic technologies to convert textual information into structured information  Gaylord Hotels uses text analytics to make sense of customer satisfaction surveys

24 4-24 Text Analytics Support  Lexical analysis – word frequency distributions  Named entity recognition – identifying peoples, places, and things  Disambiguation – meaning of a named entity recognition  “Ford” can refer to how many different things?

25 4-25 Text Analytics Support  Coreference – handling of differing noun phrases that refer to the same object  Sentiment analysis – discerning subjective business intelligence such as mood, opinion, and emotion

26 4-26 Endless Analytics  Web analytics – understanding and optimizing Web page usage  Search engine optimization (SEO) – improving the visibility of Web site using tags and key terms  HR analytics – analysis of human resource and talent management data

27 4-27 Endless Analytics  Marketing analytics – analysis of marketing-related data to improve product placement, marketing mix, etc  CRM analytics – analysis of CRM data to improve sales force automation, customer service, and support

28 4-28 Endless Analytics  Social media analytics – analysis of social media data to better understand customer/organization interaction dynamics  Mobile analytics – analysis of data related to the use of mobile devices to support mobile computing and mobile e-commerce (m-commerce)

29 4-29 ARTIFICIAL INTELLIGENCE  Artificial intelligence, the science of making machines imitate human thinking and behavior, can replace human decision making in some instances  Expert systems  Neural networks (and fuzzy logic)  Genetic algorithms  Agent-based technologies

30 4-30 Expert Systems  Expert (knowledge-based) system – an artificial intelligence system that applies reasoning capabilities to reach a conclusion  Used for  Diagnostic problems (what’s wrong?)  Prescriptive problems (what to do?)

31 4-31 Traffic Light Expert System

32 4-32 What Expert Systems Can and Can’t Do  An expert system can  Reduce errors  Improve customer service  Reduce cost  An expert system can’t  Use common sense  Automate all processes

33 4-33 Neural Networks and Fuzzy Logic  Neural network (artificial neural network or ANN) – an artificial intelligence system that is capable of finding and differentiating patterns

34 4-34 Neural Networks Can…  Learn and adjust to new circumstances on their own  Take part in massive parallel processing  Function without complete information  Cope with huge volumes of information  Analyze nonlinear relationships

35 4-35 Fuzzy Logic  Fuzzy logic – a mathematical method of handling imprecise or subjective information  Used to make ambiguous information such as “short” usable in computer systems  Applications  Google’s search engine  Washing machines  Antilock breaks

36 4-36 Genetic Algorithms  Genetic algorithm – an artificial intelligence system that mimics the evolutionary, survival-of-the-fittest process to generate increasingly better solutions to a problem

37 4-37 Genetic Algorithm Examples  Staples – determine optimal package design characteristics  Boeing – design aircraft parts such as fan blades  Many retailers – better manage inventory and optimize display areas

38 4-38 Genetic Algorithms Can…  Take thousands or even millions of possible solutions and combine and recombine them until it finds the optimal solution  Work in environments where no model of how to find the right solution exists

39 4-39 AGENT-BASED TECHNOLOGIES  Agent-based technology (software agent) – piece of software that acts on your behalf (or on behalf of another piece of software) performing tasks assigned to it


41 4-41 Types of Agent-Based Technologies  Autonomous agent – can adapt and alter the manner in which it works  Distributed agent – works on multiple distinct computer systems  Mobile agent – can relocate itself onto different computer systems

42 4-42 Types of Agent-Based Technologies  Intelligent agent – incorporates artificial intelligence capabilities such as reasoning and learning  Multi-agent system – group of intelligent agents that can work independently and also together to perform a task

43 4-43 Types of Intelligent Agents  Information agents (buyer agents) – search for information and bring it back  Monitoring-and-surveillance agents – constantly observe and report on some entity of interest, a network, or manufacturing equipment  User agents – take action on your behalf (e.g., sorting your email)

44 4-44 Types of Intelligent Agents  Data-mining agents – operate in a data warehouse discovering information  Important analytics tool for data warehouse data  Can find hidden patterns in the data  Can also classify and categorize

45 4-45 Multi-Agent Systems & Biomimicry  Biomimicry – learning from ecosystems and adapting their characteristics to human and organizational situations  Used to 1. Learn how people-based systems behave 2. Predict how they will behave under certain circumstances 3. Improve human systems to make them more efficient and effective

46 4-46 Swarm Intelligence  Swarm (collective) intelligence – the collective behavior of groups of simple agents that are capable of devising solutions to problems as they arise, eventually learning to coherent global patterns  A subfield of biomimicry

47 4-47 Characteristics of Swarm Intelligence  Flexibility – adaptable to change  Robustness – tasks are completed even if some individuals are removed  Decentralization – each individual has a simple job to do

Download ppt "McGraw-Hill/Irwin Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter 4 Analytics, Decision Support, and Artificial Intelligence:"

Similar presentations

Ads by Google