CHAPTER 1 : Business Intelligence and Decision
Support System
26 March 2021 21:41
1.1 Introduction to Business Intelligence:
WHAT DO YOU MEAN BY BUSINESS INTELLIGENCE? WRITE ITS ADVANTAGES
(5MARKS)
SOLN:
– The term Business Intelligence (BI) refers to technologies, applications and practices for th
collection, integration, analysis, presentation of business information.
– The main reason behind Business Intelligence is to provide better business decision
making
– These systems are data-driven Decision Support System(DSS). Business intelligence is
sometimes used interchangeably with briefing books, reports and query tools and executiv
information systems.
– Also called as a set of mathematical model and analysis methodology which is very usefu
for decision making process which are complex.
– Large amount of data can be easily accessed by individual and organizations because of
numerous internet connections and low data storage technologies.
– Transactions are commercial, financial and administrative making the data heterogenou
in origin, content and representation. Emails, text and hypertexts and the result of clinica
test are a few exampled
– Their accessibility opens various scenarios and opportunities and raises a rather importan
question: is it possible to convert such data into formation and knowledge that can be use
by decision makers to assist and improve the operation of enterprises and of public
administration.
1.2. Effective and Timely Decisions:
– In complex organizations, public or private, decisions are made on a continual
Basis.
– Such decisions may be more or less critical, have long- or short-term
effects and involve people and roles at various hierarchical levels. The ability
of these knowledge workers to make decisions, both as individuals and as a
community, is one of the primary factors that influence the performance and
competitive strength of a given organization.
– Most knowledge workers reach their decisions primarily using easy and
intuitive methodologies, which take into account specific elements such as
experience, knowledge of the application domain and the available information.
This approach leads to a stagnant decision-making style which is inappropriate
for the unstable conditions determined by frequent and rapid changes in
the economic environment
Page 1
, – Indeed, decision-making processes within today’s
organizations are often too complex and dynamic to be effectively dealt with
through an intuitive approach, and require instead a more rigorous attitude
based on analytical methodologies and mathematical models.
Example 1. – Retention in the mobile phone industry:
The marketing
manager of a mobile phone company realizes that a large number of
customers are discontinuing their service, leaving her company in favor
of some competing provider. As can be imagined, low customer loyalty,
also known as customer attrition or churn, is a critical factor for many
companies operating in service industries. Suppose that the marketing
manager can rely on a budget adequate to pursue a customer retention
campaign aimed at 2000 individuals out of a total customer base of 2
million people. Hence, the question naturally arises of how she should
go about choosing those customers to be contacted so as to optimize the
effectiveness of the campaign. In other words, how can the probability
that each single customer will discontinue the service be estimated so as to
target the best group of customers and thus reduce churning and maximize
customer retention? By knowing these probabilities, the target group can
be chosen as the 2000 people having the highest churn likelihood among
the customers of high business value. Without the support of advanced
mathematical models and data mining techniques.
It would be arduous to derive a reliable estimate of the churn probability
and to determine the best recipients of a specific marketing campaign.
Example 2 – Logistics planning: The logistics manager of a manufacturing
company wishes to develop a medium-term logistic-production
plan. This is a decision-making process of high complexity which includes,
among other choices, the allocation of the demand originating from different
market areas to the production sites, the procurement of raw materials
and purchased parts from suppliers, the production planning of the plants
and the distribution of end products to market areas. In a typical manufacturing
company this could well entail tens of facilities, hundreds of
suppliers, and thousands of finished goods and components, over a time
span of one year divided into weeks. The magnitude and complexity of
the problem suggest that advanced optimization models are required to
devise the best logistic plan. Optimization models allow highly complex and large-scale
problems to be tackled
successfully within a business intelligence framework.
The main purpose of business intelligence systems is to provide knowledge
Page 2
, workers with tools and methodologies that allow them to make effective and
timely decisions.
Effective decisions: The application of rigorous analytical methods allows decision
makers to rely on information and knowledge which are more dependable.
As a result, they are able to make better decisions and devise action plans that
allow their objectives to be reached in a more effective way. Indeed, turning to
formal analytical methods forces decision makers to explicitly describe both the
criteria for evaluating alternative choices and the mechanisms regulating the
problem under investigation. Furthermore, the ensuing in-depth examination
and thought lead to a deeper awareness and comprehension of the underlying
logic of the decision-making process
Timely decisions: Enterprises operate in economic environments characterized
by growing levels of competition and high dynamism. As a consequence, the
ability to rapidly react to the actions of competitors and to new market conditions
is a critical factor in the success or even the survival of a company.
Figure 1 below illustrates the major benefits that a given organization may draw
from the adoption of a business intelligence system. When facing problems
such as those described in Examples 1 and 2 above, decision makers ask
themselves a series of questions and develop the corresponding analysis. Hence,
they examine and compare several options, selecting among them the best
decision, given the conditions at hand.
If decision makers can rely on a business intelligence system facilitating
their activity, we can expect that the overall quality of the decision-making
process will be greatly improved. With the help of mathematical models and
algorithms, it is actually possible to analyze a larger number of alternative
Page 3
Support System
26 March 2021 21:41
1.1 Introduction to Business Intelligence:
WHAT DO YOU MEAN BY BUSINESS INTELLIGENCE? WRITE ITS ADVANTAGES
(5MARKS)
SOLN:
– The term Business Intelligence (BI) refers to technologies, applications and practices for th
collection, integration, analysis, presentation of business information.
– The main reason behind Business Intelligence is to provide better business decision
making
– These systems are data-driven Decision Support System(DSS). Business intelligence is
sometimes used interchangeably with briefing books, reports and query tools and executiv
information systems.
– Also called as a set of mathematical model and analysis methodology which is very usefu
for decision making process which are complex.
– Large amount of data can be easily accessed by individual and organizations because of
numerous internet connections and low data storage technologies.
– Transactions are commercial, financial and administrative making the data heterogenou
in origin, content and representation. Emails, text and hypertexts and the result of clinica
test are a few exampled
– Their accessibility opens various scenarios and opportunities and raises a rather importan
question: is it possible to convert such data into formation and knowledge that can be use
by decision makers to assist and improve the operation of enterprises and of public
administration.
1.2. Effective and Timely Decisions:
– In complex organizations, public or private, decisions are made on a continual
Basis.
– Such decisions may be more or less critical, have long- or short-term
effects and involve people and roles at various hierarchical levels. The ability
of these knowledge workers to make decisions, both as individuals and as a
community, is one of the primary factors that influence the performance and
competitive strength of a given organization.
– Most knowledge workers reach their decisions primarily using easy and
intuitive methodologies, which take into account specific elements such as
experience, knowledge of the application domain and the available information.
This approach leads to a stagnant decision-making style which is inappropriate
for the unstable conditions determined by frequent and rapid changes in
the economic environment
Page 1
, – Indeed, decision-making processes within today’s
organizations are often too complex and dynamic to be effectively dealt with
through an intuitive approach, and require instead a more rigorous attitude
based on analytical methodologies and mathematical models.
Example 1. – Retention in the mobile phone industry:
The marketing
manager of a mobile phone company realizes that a large number of
customers are discontinuing their service, leaving her company in favor
of some competing provider. As can be imagined, low customer loyalty,
also known as customer attrition or churn, is a critical factor for many
companies operating in service industries. Suppose that the marketing
manager can rely on a budget adequate to pursue a customer retention
campaign aimed at 2000 individuals out of a total customer base of 2
million people. Hence, the question naturally arises of how she should
go about choosing those customers to be contacted so as to optimize the
effectiveness of the campaign. In other words, how can the probability
that each single customer will discontinue the service be estimated so as to
target the best group of customers and thus reduce churning and maximize
customer retention? By knowing these probabilities, the target group can
be chosen as the 2000 people having the highest churn likelihood among
the customers of high business value. Without the support of advanced
mathematical models and data mining techniques.
It would be arduous to derive a reliable estimate of the churn probability
and to determine the best recipients of a specific marketing campaign.
Example 2 – Logistics planning: The logistics manager of a manufacturing
company wishes to develop a medium-term logistic-production
plan. This is a decision-making process of high complexity which includes,
among other choices, the allocation of the demand originating from different
market areas to the production sites, the procurement of raw materials
and purchased parts from suppliers, the production planning of the plants
and the distribution of end products to market areas. In a typical manufacturing
company this could well entail tens of facilities, hundreds of
suppliers, and thousands of finished goods and components, over a time
span of one year divided into weeks. The magnitude and complexity of
the problem suggest that advanced optimization models are required to
devise the best logistic plan. Optimization models allow highly complex and large-scale
problems to be tackled
successfully within a business intelligence framework.
The main purpose of business intelligence systems is to provide knowledge
Page 2
, workers with tools and methodologies that allow them to make effective and
timely decisions.
Effective decisions: The application of rigorous analytical methods allows decision
makers to rely on information and knowledge which are more dependable.
As a result, they are able to make better decisions and devise action plans that
allow their objectives to be reached in a more effective way. Indeed, turning to
formal analytical methods forces decision makers to explicitly describe both the
criteria for evaluating alternative choices and the mechanisms regulating the
problem under investigation. Furthermore, the ensuing in-depth examination
and thought lead to a deeper awareness and comprehension of the underlying
logic of the decision-making process
Timely decisions: Enterprises operate in economic environments characterized
by growing levels of competition and high dynamism. As a consequence, the
ability to rapidly react to the actions of competitors and to new market conditions
is a critical factor in the success or even the survival of a company.
Figure 1 below illustrates the major benefits that a given organization may draw
from the adoption of a business intelligence system. When facing problems
such as those described in Examples 1 and 2 above, decision makers ask
themselves a series of questions and develop the corresponding analysis. Hence,
they examine and compare several options, selecting among them the best
decision, given the conditions at hand.
If decision makers can rely on a business intelligence system facilitating
their activity, we can expect that the overall quality of the decision-making
process will be greatly improved. With the help of mathematical models and
algorithms, it is actually possible to analyze a larger number of alternative
Page 3
