2. Learning Objectives
• Decision Making
• Decision Models
• Types of Decision Making
• Decision Support Systems (DSS)
• Types of DSS
• Group Decision Support Systems (GDSS)
• Data Warehousing
• Data Analysis using Data Warehouse
• Data Mining
• Data Mining Tools
3. Decision Making
• Decision making is the study of identifying and
choosing alternatives based on the values and
preferences of the decision maker.
• Decision making is the process of sufficiently
reducing uncertainty and doubt about
alternatives to allow a reasonable choice to be
made.
4. Styles of Decision Making
• Optimizing: This helps in selecting the best possible
alternative for a decision problem. This style involves:
– Identification of a problem
– Generating alternatives
– Selecting the best alternative
– Implementing the best alternative
– Feedback
• Satisficing
• Organizational
• Political
• Maximax
• Maximin
5. Decision Making Procedure
• Identify the decision problem keeping the
goals in mind.
• Get the facts.
• Develop alternatives.
• Evaluate each alternative.
• Rate the risk of each alternative.
• Make the decision.
6. The Modeling Process
Analysis
Model Results
Interpretation
Abstraction
Symbolic World
Real World
Management Intuition
Decisions
Situation
7. Modeling Characteristics
• A model is a simplified representation of a real-world
situation.
• The advantages of a model are:
– The cost of modeling is less
– Models enable compression of time
– Manipulation of model is much simpler and easier
– Testing a model is easier
– It is easy for the decision-maker to understand
– There is less risk when experimenting with a model than
with the real system.
– Mathematical models enables testing of large data sets
8. Types of Models
Model Type Characteristics Examples
Tangible
Easy to Comprehend
Car or Aero plane or
Physical Model Difficult to Duplicate and Share
House or Building Models
Difficult to Modify and Manipulate
Limited Scope of Use
Intangible
Tough to Comprehend
Road Map, Speedometer,
Analog Model Easy to Duplicate and Share
Bar or Pie Chart
Easy to Modify and Manipulate
Wider Scope of Use
Intangible
Tough to Comprehend Simulation Model,
Symbolic Model Easy to Duplicate and Share Algebraic Model,
Easy to Modify and Manipulate Spreadsheet Model
Widest Scope of Use
9. Types of Decision Making
• Business decision making is mainly of three
types:
– Decisions taken under conditions of certainty
(Structured Decisions)
– Decisions taken under conditions of risk (Semi-
Structured Decisions)
– Decisions taken under conditions of uncertainty
(Un-structured Decisions)
10. Characteristics of Decision Types
The decision-making environment
Characteristics Certainty Risk Uncertainty
Controllable variables Known Known Known
Uncontrollable variable Known Probabilistic Unknown
Type of model Deterministic Probabilistic Non-probabilistic
Type of decision Best Informed Uncertain
Information type Quantitative Quantitative and Qualitative Qualitative
Mathematical tools Linear Statistical methods; Decision analysis;
Programming Simulation Simulation
11. Decision Support Systems (DSS)
• Decision Support System (DSS) is an
interactive computer-based information
system that supports a decision.
• The primary function of a DSS is to assist
managers in solving unstructured, semi-
structured and structured decision problems.
• DSS primarily supports analytical, quantitative
type of work using modeling techniques.
12. Characteristics of DSS
• The major characteristics of DSS would include:
• For semi-structured and Unstructured decisions
• For managers at different levels
• For groups and individuals
• For Adaptable and flexible decisions
• Effectiveness, not efficiency the focus
• Humans control the machine
• Modeling & Knowledge based
– Communication DSS
– Data-Driven DSS
– Document-Driven DSS
– Knowledge-Driven DSS
– Model-Driven DSS
14. Group Decision Support Systems
• GDSS is an interactive computer-based system
that facilitates solution of unstructured
decision problems by decision makers working
as group.
• Organizations decision making process is
individual or group driven.
• DSS systems are widely used by individuals
and the GDSS is meant to be used by the
Group decision processes.
15. Advantages of Group Decision Process
• Increased participation
• Improved pre-planning
• Open, collaborative atmosphere
• Idea generation free of criticism
• Groups are better than individuals at understanding
problems
• People are accountable for decisions that they are
participating in
• Group has more information (Knowledge) than individual
• Group members will have their egos embedded in the
decision
• Better and easy implementation
16. Problems of Group Decision Process
• Time consuming and slow process
• Lack of coordination
• Poor planning of meetings
• Inappropriate influence of group dynamics like
fear to speak
• Tendency toward compromised solutions of poor
quality
• Tendency to repeat what already was said
• Larger cost of making decision
• Inappropriate representation in the group
17. Data Warehouse
• A data warehouse is a subject-oriented,
integrated, time-variant and non-volatile
collection of data in support of management's
decision making process.
• Data warehousing provides architectures and
tools for business executives to systematically
organize, understand, and use their data to
make strategic decisions.
18. Components of Data Warehouse
• Subject-oriented.
• Integrated.
• Time-variant.
• Nonvolatile.
19. Creating Data Warehouse
• Source Data/System Identification
• Data Warehouse Design and Creation
• Data Acquisition
• Data Cleansing
• Data Aggregation
20. BI Tools for Data Analysis
• Business Intelligence (BI) is a very broad field,
which contains technologies such as:
• Decision Support Systems (DSS)
• Executive Information Systems (EIS)
• On-Line Analytical Processing (OLAP)
• Relational OLAP (ROLAP)
• Multi-Dimensional OLAP (MOLAP)
• Hybrid OLAP (HOLAP, a combination of MOLAP
and ROLAP)
21. Components of BI Tool
• Multi-dimensional Analysis Tools: Tools that allow the user to look
at the data from a number of different "angles". It helps the user to
have a 360 degree view of data. These tools often use a multi-
dimensional database referred to as a "cube".
• Query tools: Tools that allow the user to use SQL (Structured Query
Language) queries against the warehouse and get a result.
• Data Mining Tools: Tools that automatically search for patterns in
data. These tools are usually driven by complex statistical formulas.
The easiest way to distinguish data mining from the various forms
of OLAP is that OLAP can only answer questions you know to ask,
data mining answers questions that one may not be aware of.
• Data Visualization Tools: Tools that show graphical representations
of data, including complex three-dimensional data pictures. The
theory is that the user can "see" trends more effectively in this
manner than when looking at complex statistical graphs.
22. ROLAP Vs. MOLAP
Characteristic ROLAP MOLAP
Schema Uses star schema Uses data cubes
Additional dimensions can be Additional dimensions require
added dynamically re-creation of the data cube
Database size Medium to large Small to medium
Architecture Client/server Client/server
Standards-based Proprietary
Open
Access Supports ad hoc requests Limited to predefined
Unlimited dimensions dimensions
Resources High Very high
Flexibility High Low
Scalability High Low
Speed Good with small data sets; Faster for small to medium
average for medium to large data sets; average for large
data sets data sets
23. Data Warehouse Structures
• Data warehouse uses the star schema as a data-
modeling technique The basic star schema has
four components:
– Facts: Facts are numeric measurements (values) that
represent a specific business aspect or activity.
– Dimensions: Dimensions are qualifying characteristics
that provide additional perspectives to a given fact.
– Attributes: Each dimension table contains attributes.
Attributes are often used to search, filter, or classify
facts.
– Attribute Hierarchies: Attributes within dimensions
can be ordered in a well-defined attribute hierarchy.
24. Data Mining
• Data mining tools predict future trends and
behaviors, allowing businesses to make
proactive, knowledge-driven decisions.
• The purpose of data mining is to discover
previously unknown data characteristics,
relationships, dependencies, or trends.
• Data mining is described as a methodology
designed to perform knowledge-discovery
expeditions over the database data with minimal
end user intervention during the actual
knowledge-discovery phase.
25. Data Preparation Stages
• Data preparation
• Data analysis and classification
• Knowledge acquisition
• Prognosis
26. Data Mining Tools
• Classes: Stored data is used to locate data in predetermined
groups. For example, a retail chain could mine customer purchase
data to determine when customers visit and what they typically
buy. This information could be used to increase traffic by having
special offers for the day.
• Clusters: Data items are grouped according to logical relationships
or customer preferences. For example, data can be mined to
identify market segments or customer affinities.
• Associations: Data can be mined to identify associations between
the buying patterns. The bread-butter or beer-nuts are examples of
associative mining. This helps in doing market-basket analysis.
• Sequential patterns: Data is mined to anticipate behavior patterns
and trends. It helps in identifying the sequence of purchase. For
example, if a customer buys a pen what probability that he/she is
going to buy a notebook as its next item.
27. Data Mining Tools
• Decision trees: A structure that can be used to divide up a large collection of records into
successively smaller sets of records by applying a sequence of simple decision rules. A
decision tree model consists of a set of rules for dividing a large heterogeneous population
into smaller, more homogeneous groups with respect to a particular target variable.
• Artificial Neural Networks (ANN): Non-linear predictive models that learn through training
and resemble biological neural networks in structure. When applied in well-defined
domains, their ability to generalize and learn from data “mimics” a human’s ability to learn
from experience.
• Nearest Neighbor method: In order to predict the prediction value for an unclassified
record is, look for similar records and use the prediction value of the record that is nearest
to the unclassified record. Records that are near each other will have similar prediction
values.
• Clustering: Used to segment a database into clusters based on a set of attributes. Clustering
governed by measurement of proximity. Members belong to a cluster if they have proximity
to each other. The process of grouping data into clusters so that records within a cluster
have high similarity in comparison to one another.
• Genetic algorithms: Optimization techniques that use process such as genetic combination,
mutation, and natural selection in a design based on the concepts of natural evolution.
• Rule induction: The extraction of if-then rules from data based on statistical significance.
• Data visualization: The visual interpretation of complex relationships in multidimensional
data. Graphics tools are used to illustrate data relationships.
28. Summary
• Decision making process is a systematic means of arriving at a decision. It is a way of organizing data with the purpose of
presenting or displaying it to the decision maker in such a way that is more obvious than simply making a list of the
alternatives.
• There are two major approaches to decision making in an organization, the authoritarian method in which an executive
figure makes a decision for the group and the group method in which the group decides what to do. Within these two
broader approaches, decision makers follow their own style of generating alternatives and taking decisions.
• Some of the common styles of decision making include: Optimizing; Satisficing, Organizational, Political, Maximax and
Maximin.
• Optimizing way of taking decision is the best approach as it helps the decision maker to take the decision in a structured
manner.
• The three types of models that are popularly being used by decision makers include: physical; analog; and symbolic.
• Models created by architect about new building is referred to a physical’ models. Physical models are three-dimensional
representations of real-world objects. There are also scaled-down versions of the models which are more suited to
computers include (i) analog or graphical models, which use lines, curves, and other symbols to produce flow charts, pie
charts, bar charts, scatter diagrams, etc. and (ii) symbolic or mathematical models which use formulae and algorithms to
represent real-world situations.
• Business decision making is mainly of three types: Decisions taken under conditions of certainty (Structured Decisions);
Decisions taken under conditions of risk (Semi-Structured Decisions); and Decisions taken under conditions of uncertainty
(Un-structured Decisions).
• Decision Support System (DSS) is an interactive computer-based information system that supports a decision. The primary
function of a DSS is to assist managers in solving unstructured, semi-structured and structured decision problems.
• Typical information that a decision support application might gather and present would be, (a) Accessing all information
assets, including legacy and relational data sources; (b) Comparative data figures; (c) Projected figures based on new data
or assumptions; (d) Consequences of different decision alternatives, given past experience in a specific context.
• The major components that a DSS system would include are User-interface; DSS Data Base; DSS Model Base; and
Knowledge Base.
29. Summary
• Five types of DSS includes: data-driven DSS; Model-driven DSS; Communications-driven DSS; Document-driven DSS;
and knowledge-driven DSS.
• GDSS is an interactive computer-based system that facilitates solution of unstructured decision problems by decision
makers working as group. Organizations decision making process is individual or group driven.
• A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of
management's decision making process. Data warehousing provides architectures and tools for business executives to
systematically organize, understand, and use their data to make strategic decisions.
• Once the warehouse has been built and populated, it becomes possible to extract meaningful information from it that
will provide a competitive advantage and a return on investment. This is done using Business Intelligence (BI) tools. BI
is a very broad field, which contains technologies such as Decision Support Systems (DSS), Executive Information
Systems (EIS), On-Line Analytical Processing (OLAP), Relational OLAP (ROLAP), Multi-Dimensional OLAP (MOLAP),
Hybrid OLAP (HOLAP, a combination of MOLAP and ROLAP), and more.
• Data warehouse uses the star schema as a data-modeling technique. It is also used to map multidimensional decision
support data into a relational database. The basic star schema has four components: facts, dimensions, attributes, and
attribute hierarchies.
• Online Analytical Processing (OLAP), create an advanced data analysis environment that supports decision making,
business modeling, and operations research activities. OLAP systems share the following characteristics: Use
multidimensional data analysis techniques; Provide advanced database support; Provide easy-to-use end user
interfaces; and Support client/server architecture. Multidimensional data analysis refers to the processing of data
such that data are viewed as part of a multidimensional structure.
• Data mining is a powerful technological tool that helps organization in extracting hidden predictive information from
large databases. Data mining tools predict future trends and behaviors, allowing businesses to make proactive,
knowledge-driven decisions.
• Data mining software analyzes relationships and patterns in stored transaction data based on open-ended user
queries. Several types of analytical software are available: statistical, machine learning, and neural networks etc.
Generally, while mining the data one or more of four types of relationships are sought: classes; clusters; association;
and sequencing. Different kind of tools that are popularly being used are: Decision tree; artificial neural network;
nearest neighbor; cluster analysis; genetic algorithm; rule induction; and data visualization.
