Transcript Ch 4
CHAPTER 4 ANALYTICS, DECISION SUPPORT, AND ARTIFICIAL INTELLIGENCE Brainpower for Your Business Opening Case: Online Learning Notice the increase in online learning and the decrease in traditional enrollments. Phases of Decision Making Intelligence Design Choice Implementation Types of Decisions Structured decision Semi-Structured decision Nonstructured decision What Job Do I Take? Types of Decisions Recurring decision Nonrecurring (ad hoc) decision Types of Decisions You Face EASIEST MOST DIFFICULT Decision Support Systems Decision support system (DSS) Helps you analyze, but you must know how to solve the problem, and how to use the results of the analysis Components of a DSS Model management component Data management component User interface management component Components of a DSS Geographic Information Systems Geographic information system (GIS) https://www.youtube.com/watch?v=rokWdaG c3u4 Spatial information is any information in map form Used to analyze information, generate business intelligence, and make decisions Google Earth as a GIS 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 Our focus GISs Specialized analytics Artificial intelligence Data-Mining: 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 Data-Mining: 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 Data-Mining: 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 Data-Mining: 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 Marketing analytics – analysis of marketingrelated data to improve product placement, marketing mix, etc Data-Mining: Endless Analytics CRM analytics – analysis of CRM data to improve sales force automation, customer service, and support 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 (mcommerce) Artificial Intelligence Artificial intelligence (AI) Types of AI systems used in business 1. 2. 3. 4. Expert systems Neural networks Genetic algorithms Agent-based technologies AI systems deliver the conclusion (rather than helping you analyze the options) Expert Systems Expert (knowledge-based) system Used for Diagnostic problems (what’s wrong?) Prescriptive problems (what to do?) Expert System Example: Traffic Light Expert System: Components 1. Information Types 2. People 3. Problem facts Domain expertise “Why?” information Domain expert Knowledge engineer Knowledge worker IT Components Knowledge acquisition Knowledge base Inference engine User interface Explanation module Expert System: Components 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 Neural Networks and Fuzzy Logic Neural network (NN) (or artificial neural network (ANN)) Learns through training Finds patterns The Layers of a Neural Network 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 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 Genetic Algorithms Genetic algorithm (GA) Takes thousands or even millions of possible solutions, combining and recombining them until it finds the optimal solution Work in environments where no model of how to find the right solution exists 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 Agent-Based Technologies Intelligent Agents Multi-Agent Systems Intelligent Agents Intelligent agent Information agents or shopping/buyer agents Monitoring-and-surveillance agents User or personal agents Data-mining agents Multi-Agent Systems Biomimicry Swarm (collective) Intelligence Which System Should be Used? Problem Type of System You and another marketing executive on a different continent want to develop a new pricing structure for products. You want to predict when customers are about to take their business elsewhere. You want to fill out a short tax form. You want to determine the fastest route for package delivery to 23 different addresses in a city. You want to decide where to spend advertising dollars (TV, radio, newspaper, direct mail, e-mail). You want to keep track of competitors’ prices for comparable goods and services. System choices are: DSS, GIS, ES, NN, GA, or IA.