LECTURE 10:
DATA MART
Data warehouses can become enormous with hundreds of gigabytes of transactions. As a result,
subsets, known as "data marts," are often created for just one department or product line. Data
Warehouse combines databases across an entire enterprise. However, Data Marts are usually
smaller and focus on a particular subject or department or product line.
Following are the common techniques through which a data warehouse can be used.
11.1 Online Analytical Processing (OLAP)
Decision support software that allows the user to quickly analyze information that has been
summarized into multidimensional views and hierarchies. The term online refers to the
interactive querying facility provided to the user to minimize response time. It enables users to
drill down into large volume of data in order to provide desired information, such as isolating the
products that are more volatile from sales data. OLAP summarizes transactions into
multidimensional user defined views.
11.2 Data Mining
Data mining is also known as Knowledge-Discovery in Databases (KDD). Put simply it is the
processing of the data warehouse. It is a process of automatically searching large volumes of data
for patterns. The purpose is to uncover patterns and relationships contained within the business
activity and history and predict future behaviour. Data mining has become an important part of
customer relationship management (CRM).
The data mining procedure involves following steps
1. Exploration – includes data preparation which may involve filtering data and data
transformations, selecting subsets of records.
2. Model building and validation – involves the use of various models for predictive
performance (i.e., explaining the variability in question and producing stable results
across samples). Each model contains various patterns of queries used to discover new
patterns and relations in the data.
3. Deployment – That final stage involves using the model selected as best in the previous
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stage and applying it to new data in order to generate predictions or estimates of the
expected outcome.
Example of Data Mining
Consider a retail sales department. Data mining system may infer from routine transactions that
customers take interests in buying trousers of a particular kind in a particular season. Hence, it
can make a correlation between the customer and his buying habits by using the frequency of
his/her purchases. The marketing department will look at this information and may forecast a
possible clientele for matching shirts. The sales department may start a departmental campaign to
sell the shirts to buyers of trousers through direct mail, electronic or otherwise.
In this case, the data mining system generated predictions or estimates about the customer that
was previously unknown to the company.
12.2.1 Concept of Models Used in Decision Support System (DSS)
“A model is an abstract representation that illustrates the components or relationships of a
phenomenon.”
Models are prepared so as to formulate ideas about the problem solutions that is allowing the
managers to evaluate alternative solutions available for a problem in hand.
11.3 Types of Models Used in DSS
1. Physical Models
2. Narrative Models
3. Graphic Models
4. Mathematical Model
11.3.1 Physical Models
Physical models are three dimensional representation of an entity (Object / Process). Physical
models used in the business world include scale models of shopping centres and prototypes of
new automobiles.
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The physical model serves a purpose that cannot be fulfilled by the real thing, e.g. it is much less
Expensive for shopping centre investors and automakers to make changes in the designs of their
physical models than to the final product themselves.
11.3.2 Narrative Models
The spoken and written description of an entity as Narrative model is used daily by managers
and surprisingly, these are seldom recognized as models. For instance, all business
communications are narrative models
11.3.3 Graphic Models
These models represent the entity in the form of graphs or pictorial presentations. It represents its
entity with an abstraction of lines, symbols or shapes. Graphic models are used in business to
communicate information. Many company’s annual reports to their stockholders contain
colourful graphs to convey the financial condition of the firm. For Instance, Bar graphs of
frequently asked questions with number of times they are asked.
11.3.4 Mathematical Models
They represent Equations / Formulae representing relationship between two or more factors
related to each other in a defined manner.
Mathematical models can further be classified as follows, based on

Influence of time – whether the event is time dependant or related

Degree of certainty – the probabilities of occurrence of an event

Level of optimization – the perfection in solution the model will achieve.
Hence use of right model in decision support software is critical to the proper functionality of the
system.
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11.3.4.1 Group DSS
When people responsible for decision making are geographically dispersed or are not available at
a place at the same time, GDSS is used for quick and efficient decision making. GDSS is
characterized by being used by a group of people at the same time to support decision making.
People use a common computer or network, and collaborate simultaneously.
An electronic meeting system (EMS) is a type of computer software that facilitates group
decision-making within an organization. The concept of EMS is quite similar to chat rooms,
where both restricted and unrestricted access can be provided to a user/member.
11.3.4.2 DSS vs. GDSS
DSS can be extended to become a GDSS through
The addition of communication capabilities the ability to vote, rank, rate etc
Greater system reliability
11.4 Knowledge / Intelligent Systems
Before we proceed with defining these systems, first we should have clear concept of Knowledge
Management. The set of processes developed in an organization to create, gather, store, maintain
and apply the firm’s knowledge is called Knowledge Management. Hence the systems that aid in
the creation and integration of new knowledge in the organization are called knowledge systems.
There are two questions
Who are they built for?
This refers to defining the knowledge workers for whom the knowledge system is being built.
The term refers to people who design products and services and create knowledge for an
organization. For instance, Engineers, Architects, Scientists
Knowledge systems are specially designed in assisting these professionals in managing the
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knowledge in an organization.
What are they built for?
Every knowledge system is built to maintain a specific form of knowledge. Hence it needs to be
defined in the start, what the system would maintain. There are major types of knowledge.

Explicit knowledge – Structured internal knowledge e.g. product manuals, research
reports, etc.

External knowledge of competitors, products and markets

Tacit knowledge – informal internal knowledge, which resides in the minds of the
employees but has not been documented in structured form.
Knowledge systems promote organizational learning by identifying, capturing and distributing
these forms of knowledge
11.5 Knowledge Support Systems (KSS) / Intelligent Systems
These systems are used to automate the decision making process, due to its high-level-problemsolving support. KSS also has the ability to explain the line of reasoning in reaching a particular
solution, which DSS does not have.
11.5.1 Intelligent Systems
Knowledge systems are also called intelligent systems. The reason is that once knowledge
system is up and running, it can also enable non experts to perform tasks previously done by
experts. This amounts to automation of decision making process i.e. system runs independently
of the person making decisions.
11.5.2 Artificial Intelligence
“Artificial intelligence is the ability of a machine to replicate the human thought processes. The
way humans proceed to analyze a problem and find appropriate solutions, similarly computers
are geared up to follow human logic to solve problems.”
These knowledge-based applications of artificial intelligence have enhanced productivity in
business, science, engineering, and the military. With advances in the last decade, today's expert
systems clients can choose from dozens of commercial software packages with easy-to-use
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interfaces. The most popular type of intelligent systems is the Expert System.
11.5.3 Expert System
An expert system is a computer program that attempts to represent the knowledge of human
experts in the form of Heuristics. It simulates the judgment and behaviour of a human or an
organization that has expert knowledge and experience in a particular field.
Examples are

Medical diagnosis, Equipment repair, Investment analysis,

Financial, estate and insurance planning, Vehicle routing,

Contract bidding
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11.5.4 Heuristics
Heuristic is the art and science of discovery and invention. The word comes from the same
Greek root as "eureka", which means "I have found it". A heuristic is a way of directing your
attention fruitfully. It relates to using a problem-solving technique, in which the most
appropriate solution is found by alternative methods. This solution is selected at successive
stages of a program for use in the next step of the program.
11.6 Components of an Expert System
There are four main components of Expert systems
1. User Interface: to enable the manager to enter instructions and information into an
expert system to receive information from it.
2. Knowledge Base: it is the database of the expert system. It contains rules to express
the logic of the problem.
3. Inference engine: it is the database management system of the expert system. It
performs reasoning by using the contents of the knowledge base.
4. Development engine – it is used to create an expert system.
11.6.1 Neural Network
Hardware or software that attempt to emulate the processing patterns of the biological brain.
It is a device, modelled after the human brain, in which several interconnected elements
process information simultaneously, adapting and learning from past patterns.
11.6.1.1 Neural Network vs. Expert System
Expert systems seek to model a human expert’s way of solving problems. They are highly
specific to seeking solutions. Neural networks do not model human intelligence. They seek to
put intelligence into the hardware in the form of generalized capability to learn.
11.6.1.2 Fuzzy Logic
The word Fuzzy literally means vague, blurred, hazy, and not clear. Real life problems may
not be solved by an optimized solution. Hence allowance needs to be made for any
imperfections which may be faced while finding a solution to a problem. Fuzzy logic is a
form of algebra employing a range of values from “true” to “false” that is used in decisionmaking with imprecise data, as in artificial intelligence systems. It is a rule based technology
that tolerates imprecision by using non specific terms/ imprecise concepts like "slightly",
"quite" and "very". To solve problems. It is based on the Possibility theory, which is a
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mathematical theory for dealing with certain types of uncertainty and is an alternative to
probability theory.
11.6.1.3 Executive Support Systems (ESS)
This Computer Based Information System (CBIS) is used by senior managers for strategic
decision making. The decisions at this level are non-routine and require judgment and
evaluation. They draw summarized information from internal MIS and Decision Support
Systems. These systems deal with external influences on an organization as well.

New Tax laws

Competitors

Acquisitions, take-overs, spin offs etc.
They filter, compress and track critical data so as to reduce time and effort required to obtain
information useful for executives. They are not designed to solve specific problems. They are
generalized to be capable of dealing with changing problems. Since executives have little
contact with all levels of the organization, ESS uses more graphical interface for quick
decision making.
11.6.1.4 ESS vs. DSS
ESS implies more of a war room style graphical interface that overlooks the entire enterprise.
A decision support system (DSS) typically provides a spreadsheet style "what if?" analysis
capability, often for only one department or one product at time.
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