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What We’re Going to do Today
• Announcements• MIDTERM• Q&A• Alphabet soup• MIS in the professions• Decision Support Systems• Artificial Intelligence and Data Mining
Midterm Exam
• Fifty multiple choice questions– Class discussions– Textbook readings– Podcasts
• (B5, B10, T1, T2, Unit 2 Intro, Ch. 7, B2, Ch. 8, B1)
Alphabet Soup
• CRM– Customer Relationship Management
• Hot topic in today’s business world• Intended to increase customer loyalty• Cluster Analysis aides in CRM (segment customers)
• Digital Dashboard– A display of information from a variety of
sources that has been pieced together to aid in decision making
• Dashboard
MIS in the Professions – Andersen Consulting
• Pocket BargainFinder is a handheld device that looks up critical pricing information for products available online. – It allows customers to scan a barcode at a
retail store entering product information into this handheld device which scours the internet for online retail stores with better prices
– This is an example of a “Shopping Bot” (Intelligence Agent).
Decision Making (cont’d)
• The amount of information people must understand to make decisions, solve problems, and find opportunities is growing exponentially.
Decision Making (cont’d)
• Model – a simplified representation or abstraction of reality
• The following systems use models to support decision making, problem solving, and opportunity capturing:– Decision support systems (DSS)– Executive information systems (EIS)– Artificial intelligence (AI)– Data mining
DECISION SUPPORT SYSTEMS
• Decision support system (DSS) – models information to support managers and business professionals during the decision-making process
• Three quantitative models typically used by DSSs:1.Sensitivity analysis – the study of the impact that
changes in one (or more) parts of the model have on other parts of the model
2.What-if analysis – checks the impact of a change in an assumption on the proposed solution
3.Goal-seeking analysis – finds the inputs necessary to achieve a goal such as a desired level of output
EXECUTIVE INFORMATION SYSTEMS
• Executive information system (EIS) – a specialized DSS that supports senior level executives within the organization
• Most EISs offering the following capabilities:– Consolidation – involves the aggregation of
information and features simple roll-ups to complex groupings of interrelated information
– Drill-down – enables users to get details, and details of details, of information
– Slice-and-dice – looks at information from different perspectives
EXECUTIVE INFORMATION SYSTEMS
• Digital dashboard – integrates information from multiple components and present it in a unified display
ARTIFICAL INTELLIGENCE (AI)
• Intelligent systems – various commercial applications of artificial intelligence
• Artificial intelligence (AI) – simulates human intelligence such as the ability to reason and learn and typically can:– Learn or understand from experience– Make sense of ambiguous or contradictory
information– Use reasoning to solve problems and make
decisions
ARTIFICAL INTELLIGENCE (AI)• The ultimate goal of AI is the ability to
build a system that can mimic human intelligence
ARTIFICAL INTELLIGENCE (AI)
• The three most common categories of AI include:
1. Expert systems – computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems1. Eg. – Deep Blue vs. Garry Kasparov
2. Neural Networks – attempts to emulate the way the human brain works
3. Intelligent agents – special-purposed knowledge-based information system that accomplishes specific tasks on behalf of its users
DATA MINING
• Data-mining software typically includes many forms of AI such as neural networks and expert systems
DATA MINING
• Common forms of data-mining analysis capabilities include
– Cluster analysis– Association detection– Statistical analysis
Cluster Analysis
• Cluster analysis – a technique used to divide an information set into mutually exclusive groups such that the members of each group are as close together as possible to one another and the different groups are as far apart as possible
• CRM systems depend on cluster analysis to segment customer information and identify behavioral traits
Association Detection
• Association detection – reveals the degree to which variables are related and the nature and frequency of these relationships in the information
– Market basket analysis – analyzes such items as Web sites and checkout scanner information to detect customers’ buying behavior and predict future behavior by identifying affinities among customers’ choices of products and services
Statistical Analysis• Statistical analysis – performs such
functions as information correlations, distributions, calculations, and variance analysis
– Forecasts – predictions made on the basis of time-series information
– Time-series information – time-stamped information collected at a particular frequency