DSS and Data Mining

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DSS and Data Mining

• ISM3011

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What Were Going to do Today

• Announcements
• MIDTERM
• QA
• Alphabet soup
• MIS in the professions
• Decision Support Systems
• Artificial Intelligence and Data Mining

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Midterm Exam

• Fifty multiple choice questions
• Class discussions
• Textbook readings
• Podcasts
• (B5, B10, T1, T2, Unit 2 Intro, Ch. 7, B2, Ch. 8,
  B1)

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Alphabet Soup

• CRM
• Customer Relationship Management
• Hot topic in todays 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

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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).

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DSS/Data Mining
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Decision Making

• Decision-enabling, problem-solving, and
  opportunity-seizing systems

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Decision Making (contd)

• The amount of information people must understand
  to make decisions, solve problems, and find
  opportunities is growing exponentially.

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Decision Making (contd)

• 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

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DECISION SUPPORT SYSTEMS
Start
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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
• Sensitivity analysis the study of the impact
  that changes in one (or more) parts of the model
  have on other parts of the model
• What-if analysis checks the impact of a change
  in an assumption on the proposed solution
• Goal-seeking analysis finds the inputs
  necessary to achieve a goal such as a desired
  level of output

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DECISION SUPPORT SYSTEMS

• What-if Analysis

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DECISION SUPPORT SYSTEMS

• Goal-seeking analysis

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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

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EXECUTIVE INFORMATION SYSTEMS

• Digital dashboard integrates information from
  multiple components and present it in a unified
  display

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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

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ARTIFICAL INTELLIGENCE (AI)

• The ultimate goal of AI is the ability to build a
  system that can mimic human intelligence

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ARTIFICAL INTELLIGENCE (AI)

• The three most common categories of AI include
• Expert systems computerized advisory programs
  that imitate the reasoning processes of experts
  in solving difficult problems
• Eg. Deep Blue vs. Garry Kasparov
• Neural Networks attempts to emulate the way the
  human brain works
• Intelligent agents special-purposed
  knowledge-based information system that
  accomplishes specific tasks on behalf of its users

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DATA MINING

• Data-mining software typically includes many
  forms of AI such as neural networks and expert
  systems

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DATA MINING

• Common forms of data-mining analysis capabilities
  include
• Cluster analysis
• Association detection
• Statistical analysis

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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

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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

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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

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Next Time

• Exam Review on Tuesday
• Mid-term on Thursday!