DSS/Data Mining ppt.

DSS and Data Mining

ISM3011

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).

DSS/Data Mining

Decision Making

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

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

Start

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

DECISION SUPPORT SYSTEMS

• What-if Analysis

DECISION SUPPORT SYSTEMS• Goal-seeking analysis

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

Next Time

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