CHAPTER 4ANALYTICS, 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.What are the causes of this: Universities offering more online classes? More alternatives to Universities?What are the business implications of this? More space on campus…what can be done with that if it becomes available? Should colleges move towards onlinecourses?

Phases of Decision Making

Intelligence (diagnostic) Design (create solutions) Choice (select solution)Implementation (apply

solution)

May cycle between these phases.

Types of DecisionsStructured decision : data is available and variables

are known. Decision is based on known criteria.

Semi-Structured decision: In between. Some uncertainty.

Nonstructured decision: not sure what data to collect. Variables affecting decision are not known. Not sure what the success criteria may be.

Satisficing: Not optimal decision but allows us to satisfy predetermined Criteria. E.g., Salary at least $50,000.

Types of DecisionsRecurring decision Nonrecurring (ad hoc)

decisionDecision Support Systems

Helps you analyze, but you must know how to solve the problem, and how to use the results of the analysis

Model management component Data management component User interface management component

Components of a DSS

Geographic Information SystemsGeographic information system (GIS)

Spatial information is any information in map form

Used to analyze information, generate business intelligence, and make decisions. It’s easier to see information on a map. Suppose you are going to start a new store selling electronic accessories. Where would you locate it?

www.historypin.com

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DATA-MINING TOOLS AND MODELS

◦Databases and DBMSs: operational data◦Multidimensional analysis tools:

summarized data◦Digital dashboards: managers can get real-

time info and drill down or roll up◦Statistical tools: regression,

summarization, association rules, clustering

◦GISs: See information on a map◦Artificial intelligence: Genetic algorithms,

neural nets

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 unstructured textual information into structured information

Gaylord Hotels uses text analytics to make sense of customer satisfaction surveys

Data-Mining: Endless AnalyticsWeb 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 marketing-related 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 (m-commerce)

Artificial Intelligence Artificial intelligence (AI)

Types of AI systems used in business1. Expert systems2. Neural networks3. Genetic algorithms4. Agent-based technologies

AI systems deliver the conclusion (rather than helping you analyze the options)

Expert Systems

Expert (knowledge-based) system Uses if-then rules…lots of them. Used for

◦Diagnostic problems (what’s wrong?)◦Prescriptive problems (what to do?) E.g., should

we give a loan to someone. Or should a credit card customer be allowed to run a charge? If customer older than 40 AND customer income > 100,000 and previous payments all on time THEN allow transaction.

Expert System: Components1. Information Types

◦ Problem facts ◦ Domain expertise◦ “Why?” information

2. People◦ Domain expert: know the rules to apply◦ Knowledge engineer: Capture the rules◦ Knowledge worker: use the rules

3. IT Components◦ Knowledge acquisition ◦ Knowledge base ◦ Inference engine ◦ User interface ◦ Explanation module

Expert System: Components

What Expert Systems Can and Can’t DoAn 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 dataFinds patternsDifferent from expert systems since

rules do not have to be spelled out here…it discovers its own rules in the data!

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 LogicFuzzy 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 brakes

Genetic AlgorithmsGenetic algorithm (GA)

Takes thousands or even millions of possible solutions, combining and recombining them until it finds the an optimal solution

Needs a fitness function and encoding of the possible solutions in terms of chromosomes. It then attempts to find the best chromosomes, after reproduction and mutation.

E.g., What is the best inventory level for engines for the Ford F150?

Work in environments where no model of how to find the right solution exists

Agent-Based Technologies

Intelligent Agents

Multi-Agent Systems

Intelligent AgentsSoftware that acts on our behalf

◦Information agents or shopping/buyer agents

◦Monitoring-and-surveillance agents◦User or personal agents ◦Data-mining agents

Multi-Agent Systems

BiomimicrySwarm (collective) Intelligence. Ants find shortest route to food by laying

pheromone trails that decay automatically if they are unfit or the food supply is gone. Based on this, they find theshortest path to the food. Similarly, find simple rules that can create big desirable patterns.