Unit I: INTRODUCTION TO BUSINESS INTELLIGENCE

1.Defiition
1. Business Intelligence (BI) is a set of ideas, methodologies, processes, architectures, and
technologies that change raw data into significant and useful data for business purpose.
Business Intelligence can handle large amounts of data to help identify and evolve new
opportunities for the business

Common functions of enterprise Intelligence technologies are reporting, online analytical

processing, analytics, data excavation, process excavation, business performance management,
benchmarking, text mining, predictive analytics and prescriptive analytics
BI (Business Intelligence) refers to set of techniques which assist in spotting, digging out and
investigating best data from the large amount of data to improve conclusion making

Business intelligence may be defined as a set of mathematical models and analysis

methodologies that exploit the available data to generate information and knowledge useful for
complex decision-making processes
Business intelligence refers to an infrastructure that collects and analyzes large amounts of data
to give organizations a clear and comprehensive picture of their data. The goal of a BI system is
to give stakeholders a clear and customized view of their data to empower them to make data-
driven decisions.
BI includes processes such as data mining, infrastructure, visualization, analytics, and more.

2.Benefits of BI

 Improved decision making

 Faster real time analysis
 Increased organizational efficiency
 Data-driven business decisions
 Improved customer experience
 Improved employee satisfaction
 Trusted and governed data
 Increased competitive advantage
 data visualization to offer projections for future outcomes
 reduces bottlenecks

3. Purpose Of BI
Purpose is to provide knowledge 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 resulting 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

4.Data Information and Knowledge

DATA: Generally, data represent a structured codification of single primary entities, as well as of
transactions involving two or more primary entities. For example, for a retailer data refer to primary
entities such as customers, points of sale and items, while sales receipts represent the
commercial transactions
INFORMATION: Information is the outcome of extraction and processing activities carried out on
data, and it appears meaningful for those who receive it in a specific domain. For example, to the
sales manager of a retail company, the proportion of sales receipts in the amount of over ¤100
per week, or the number of customers holding a loyalty card who have reduced by more than
50% the monthly amount spent in the last three months, represent meaningful pieces of
information that can be extracted from raw stored data.
KNOWLDEGE: Information is transformed into knowledge when it is used to make decisions and
develop the corresponding actions. Therefore, we can think of knowledge as consisting of
information put to work into a specific domain, enhanced by the experience and competence of
decision makers in tackling and solving complex problems. For a retail company, a sales analysis
may detect that a group of customers, living in an area where a competitor has recently opened
a new point of sale, have reduced their usual amount of business. The knowledge extracted in
this way will eventually lead to actions aimed at solving the problem detected, for example by
introducing a new free home delivery service for the customers residing in that specific area. We
wish to point out that knowledge can be extracted from data both in a passive way, through the
analysis criteria suggested by the decision makers, or through the active application of
mathematical models, in the form of inductive learning or optimization

5. Mathematical Models:
A business intelligence system provides decision makers with information and knowledge
extracted from data, through the application of mathematical models and algorithms. In some
instances, this activity may reduce to calculations of totals and percentages, graphically
represented by simple histograms, whereas more elaborate analyses require the development of
advanced optimization and learning models.
It facilitates understanding of how changes within the framework can affect outcomes. Modelling
with data can explain past behavior, predict and forecast future.
Example: An ice cream company keeps track of how many ice creams get sold on different days.
By comparing this to the weather on each day they can make a model of sales vs weather
A mathematical model is a representation of a system using mathematical concepts and
language. Every model requires a set of input and mathematical function to generate an output.

Input output
function

The process of building a mathematical model can be understood as:

-Understanding the problem
-Choosing variables and making assumptions (Educated guess) and relationships among these
(model making)
-Applying mathematical model- Solving the equations, interpreting the solution
-Validating the model and improving the model (if expected value is different)
Advantages Of Mathematical Model:
Although their primary objective is to enhance the effectiveness of the decision-making process,
the adoption of mathematical models also affords other advantages, which can be appreciated
particularly in the long term.
a. the development of an abstract model forces decision makers to focus on the main
features of the analyzed domain, thus inducing a deeper understanding of the
phenomenon under investigation.
b. the knowledge about the domain acquired when building a mathematical model can be
more easily transferred in the long run to other individuals within the same organization,
thus allowing a sharper preservation of knowledge.
c. a mathematical model developed for a specific decision-making task is so general and
flexible that in most cases it can be applied to other ensuing situations to solve problems
of similar type.
d. Decision-making, real-world problem solving, predictions

6.Business Intelligence Architecture

It includes three major components: Data Sources, Data warehouses and data marts and
Business intelligence methodologies.
Data sources. In a first stage, it is necessary to gather and integrate the data stored in the various
primary and secondary sources, which are heterogeneous in origin and type may also include
unstructured documents, such as emails and data received from external providers. Generally
speaking, a major effort is required to unify and integrate the different data sources.
Data warehouses and data marts. Using extraction and transformation tools known as extract,
transform, load (ETL), the data originating from the different sources are stored in databases
intended to support business intelligence analyses. These databases are usually referred to as
data warehouses and data marts.
Business intelligence methodologies. Data are finally extracted and used to feed mathematical
models and analysis methodologies intended to support decision makers. In a business
intelligence system, several decision support applications may be implemented.
The following figure represents a typical Architecture of Business Intelligence System :
Building block of BI system:

Data exploration we find the tools for performing a passive business intelligence analysis,
which consist of query and reporting systems, as well as statistical methods. These are
referred to as A passive methodologies because decision makers are requested to
generate prior hypotheses or define data extraction criteria, and then use the analysis
tools to find answers and confirm their original insight.
Data mining: The fourth level includes active business intelligence methodologies, whose
purpose is the extraction of information and knowledge from data. These include mathematical
models for pattern recognition, machine learning and data mining techniques. the models of an
active kind do not require decision makers to formulate any prior hypothesis to be later verified.
Their purpose is instead to expand the decision makers’ knowledge.
Optimization: we find optimization models that allow us to determine the best solution out of a
set of alternative actions.
Decisions: Finally, the top of the pyramid corresponds to the choice and the actual adoption of a
specific decision, and in some way represents the natural conclusion of the decision-making
process. Even when business intelligence methodologies are available and successfully adopted,
the choice of a decision pertains to the decision makers, who may also take advantage of informal
and unstructured information available to adapt and modify the recommendations and the
conclusions achieved through the use of mathematical models.

As we progress from the bottom to the top of the pyramid, business intelligence systems offer
increasingly more advanced support tools of an active type. Even roles and competencies
change. At the bottom, the required competencies are provided for the most part by the
information systems specialists within the organization, usually referred to as database
administrators Analysts and experts in mathematical and statistical models are responsible for
the intermediate phases. Finally, the activities of decision makers responsible for the application
domain appear dominant at the top.
Cycle of a business intelligence analysis
Each business intelligence analysis follows its own path according to the application domain, the
personal attitude of the decision makers and the available analytical methodologies. However, it
is possible to identify an ideal cyclical path characterizing the evolution of a typical business
intelligence analysis.
Analysis: During the analysis phase, it is necessary to recognize and accurately spell out the
problem at hand. Decision makers must then create a mental representation of the phenomenon
being analyzed, by identifying the critical factors that are perceived as the most relevant. The
availability of business intelligence methodologies may help already in this stage, by permitting
decision makers to rapidly develop various paths of investigation. For instance, the exploration of
data cubes in a multidimensional analysis, according to different logical views.

Insight: The second phase allows decision makers to better and more deeply understand the
problem at hand, often at a causal level. For instance, if the analysis carried out in the first phase
shows that a large number of customers are discontinuing an insurance policy upon yearly
expiration, in the second phase it will be necessary to identify the profile and characteristics
shared by such customers. The information obtained through the analysis phase is then
transformed into knowledge during the insight phase.
Decision: During the third phase, knowledge obtained as a result of the insight phase is converted
into decisions and subsequently into actions. The availability of business intelligence
methodologies allows the analysis and insight phases to be executed more rapidly so that more
effective and timely decisions can be made that better suit the strategic priorities of a given
organization. This leads to an overall reduction in the execution time of the analysis–decision–
action– revision cycle, and thus to a decision-making process of better quality.
Evaluation: Finally, the fourth phase of the business intelligence cycle involves performance
measurement and evaluation. Extensive metrics should then be devised that are not exclusively
limited to the financial aspects but also take into account the major performance indicators defined
for the different company departments.

7. Ethics and Business Intelligence

the progress toward the information and knowledge society opens up countless opportunities, but
may also generate distortions and risks which should be prevented and avoided by using
adequate control rules and mechanisms. Usage of data by public and private organizations that
is improper and does not respect the individuals’ right to privacy should not be tolerated.
It is essential that business intelligence analysts and decision makers abide by the ethical
principle of respect for the personal rights of the individuals.
The risk of overstepping the boundary between correct and intrusive use of information is
particularly high within the relational marketing and web mining fields.
analysts developing a mathematical model and those who make the decisions cannot remain
neutral, but have the moral obligation to take an ethical stance.
The ethics in business intelligence is the ethical principles of conduct that govern an individual in
the workplace or a company in general. Profit is not the only important strategy of a business
anymore. There is also more of a concern and motivator of companies to do what is right.
Ethical Issues in BI-While many ethical issue are obscure and hard to notice at the surface there
is one concern brought up by most users and according to Hackathorn (2005),the ethical issue in
BI that is known by most is the involuntary release of personal information that has lead to identity
theft.The theft of personal information like social security numbers, birth-dates, and credit card
numbers has allowed for technology skilled criminals to possibly walk away with billions of dollars
in innocent victims’ money nationally.

8.Decision Support System

Definition of system: a system is made up of a set of components that are in some way connected
to each other so as to provide a single collective result and a common purpose.

Every system is characterized by boundaries that separate its internal components from the
external environment.

A system is said to be open if its boundaries can be crossed in both directions by flows of materials
and information. In general terms, any given system receives specific input flows, carries out an
internal transformation process and generates observable output flows.

A system receives a set of input flows and returns a set of output flows through a transformation
process regulated by internal conditions and external conditions. The effectiveness and efficiency
of a system are assessed using measurable performance indicators that can be classified into
different categories. The figure shows the abstract representation of a system along with main
types of metrics used to evaluate systems embedded within the enterprises and the public
administration.

A system will often incorporate a feedback mechanism. Feedback occurs when a system
component generates an output flow that is fed back into the system itself as an input flow,
possibly as a result of a further transformation. Systems that are able to modify their own output
flows based on feedback are called closed cycle systems.
For example, the closed cycle system outlined in the following figure describes the development
of a sequence of marketing campaigns.
A decision support system (DSS) is an interactive computer-based application that combines
data and mathematical models to help decision makers solve complex problems faced in
managing the public and private enterprises and organizations.

The analysis tools provided by a business intelligence architecture can be regarded as DSSs
capable of transforming data into information and knowledge helpful to decision makers. In this
respect, DSSs are a basic component in the development of a business intelligence architecture.

It is often necessary to assess the performance of a system. For this purpose, it is appropriate to
categorize the evaluation metrics into two main classes: effectiveness and efficiency.

Effectiveness. Effectiveness measurements express the level of conformity of a given system to

the objectives for which it was designed. The associated performance indicators are therefore
linked to the system output flows, such as production volumes, weekly sales and yield per share.
Efficiency. Efficiency measurements highlight the relationship between input flows used by the
system and the corresponding output flows. Efficiency measurements are therefore associated
with the quality of the transformation process.
For example, they might express the amount of resources needed to achieve a given sales
volume.

Generally speaking, effectiveness metrics indicate whether the right action is being carried out or
not, while efficiency metrics show whether the action is being carried out in the best possible way
or not.

9. Representation of Decision-Making Process

In order to build effective DSSs, we first need to describe in general terms how a decision-making
process is articulated. In particular, we wish to understand the steps that lead individuals to make
decisions

Rationality and problem solving: A decision is a choice from multiple alternatives, usually made
with a fair degree of rationality. A rational approach to decision making implies that the option
fulfilling the best performance criteria is selected out of all possible alternatives. We will focus on
decisions made by knowledge workers in public and private enterprises and organizations. These
decisions may concern the development of a strategic plan and imply therefore substantial
investment choices, the definition of marketing initiatives and related sales predictions, and the
design of a production plan that allows the available human and technological resources to be
employed in an effective and efficient way

In the decision-making process individuals try to bridge the gap between the current operating
conditions of a system and the supposedly better conditions to be achieved in the future. In
general, the transition of a system toward the desired state implies overcoming certain obstacles.
These forces decision makers to devise a set of alternative feasible options to achieve the desired
goal, and then choose a decision based on a comparison between the advantages and
disadvantages of each option. Hence, the decision selected must be put into practice and then
verified to determine if it has enabled the planned objectives to be achieved. When this fails to
happen, the problem is reconsidered.

The basic structure of problem-solving process can be expressed as follows:

The alternatives represent the possible actions aimed at solving the given problem and
helping to achieve the planned objective.
Criteria are the measurements of effectiveness of the various alternatives and correspond to the
different kinds of system performance.

FACTORS AFFECTING DECISION MAKING

Economic. Economic factors are the most influential in decision-making processes, and are often
aimed at the minimization of costs or the maximization of profits. For example, an annual logistic
plan may be preferred over alternative plans if it achieves a reduction in total costs.
Technical. Options that are not technically feasible must be discarded. For instance, a production
plan that exceeds the maximum capacity of a plant cannot be regarded as a feasible option.
Legal. Legal rationality implies that before adopting any choice the decision makers should verify
whether it is compatible with the legislation in force within the application domain.
Ethical. Besides being compliant with the law, a decision should abide by the ethical principles
and social rules of the community to which the system belongs.
Procedural. A decision may be considered ideal from an economic, legal and social standpoint,
but it may be unworkable due to cultural limitations of the organization in terms of prevailing
procedures and common practice.
Political. The decision maker must also assess the political consequences of specific decision
among individuals, departments and organizations.

The process of evaluating the alternatives may be divided into two main stages, exclusion and
evaluation. During the exclusion stage, compatibility rules and restrictions are applied to the
alternative actions that were originally identified. Within this assessment process, some
alternatives will be dropped from consideration, while the rest represent feasible options that will
be promoted to evaluation. In the evaluation phase, feasible alternatives are compared to one
another on the basis of the performance criteria, in order to identify the preferred decision as the
best opportunity.

PHASES OF DECISION MAKING

Intelligence. In the intelligence phase the task of the decision maker is to identify, circumscribe
and explicitly define the problem that emerges in the system under study. The analysis of the
context and all the available information may allow decision makers to quickly grasp the signals
and symptoms pointing to a corrective action to improve the system performance.

Design. In the design phase actions aimed at solving the identified problem should be developed
and planned. At this level, the experience and creativity of the decision makers play a critical role,
as they are asked to devise viable solutions that ultimately allow the intended purpose to be
achieved.

Choice. Once the alternative actions have been identified, it is necessary to evaluate them on
the basis of the performance criteria deemed significant. Mathematical models and the
corresponding solution methods usually play a valuable role during the choice phase. For
example, optimization models and methods allow the best solution to be found in very complex
situations involving countless or even infinite feasible solutions. On the other hand, decision trees
can be used to handle decision-making processes influenced by stochastic events.

Implementation. When the best alternative has been selected by the decision maker, it is
transformed into actions by means of an implementation plan. This involves assigning
responsibilities and roles to all those involved into the action plan.

Control. Once the action has been implemented, it is finally necessary to verify and check that
the original expectations have been satisfied and the effects of the action match the original
intentions. In particular, the differences between the values of the performance indicators
identified in the choice phase and the values actually observed at the end of the implementation
plan should be measured. In an adequately planned DSS, the results of these evaluations trans-
late into experience and information, which are then transferred into the data warehouse to be
used during subsequent decision-making processes.

TYPES OF DECISIONS:

Depending on their scope, decisions can be classified as strategic, tactical and operational.

Strategic decisions. Decisions are strategic when they affect the entire organization or at least
a substantial part of it for a long period of time. Strategic decisions strongly influence the general
objectives and policies of an enterprise. As a consequence, strategic decisions are taken at a
higher organizational level, usually by the company top management.
Tactical decisions. Tactical decisions affect only parts of an enterprise and are usually restricted
to a single department. The time span is limited to a medium-term horizon, typically up to a year.
In a company hierarchy, tactical decisions are made by middle managers, such as the heads
of the company departments.
Operational decisions. Operational decisions refer to specific activities carried out within an
organization and have a modest impact on the future. Operational decisions are framed within
the elements and conditions determined by strategic and tactical decisions. Therefore, they are
usually made at a lower organizational level, by knowledge workers responsible for a single
activity or task such as sub-department heads, workshop foremen, back-office heads.
10. EVOLUTION OF INFORMATION SYSTEMS

Digital computers made their appearance in the late 1940s, and soon began be applied in the
business environment. The first decades saw a rush toward information technology development,
usually under the mantra of data processing. They were characterized by the widespread diffusion
of applications that achieved an increase in efficiency by automating routine operations within
companies, especially in administration, production, research and development.

In the 1970s there began to arise within enterprises increasingly complex needs to devise
software applications, called management information systems (MIS), in order to ease access to
useful and timely information for decision makers. However, attempts to develop such systems
were hampered by the state of information technologies at the time. The mainframe computers of
those days lacked graphic visualization capabilities, and communicated with users through
character-based computer terminals and dot printers. A further difficulty lay in the organizational
structure of companies, based on a highly centralized information systems department, usually
resulting in very long- and frustrating-time delays in implementing changes or extensions to the
available applications.

From the late 1980s the introduction of personal computers with operating systems featuring
graphic interfaces and pointing devices, such as a mouse or an optical pen, had two major
consequences. On the one hand, it became possible implement applications capable of
sophisticated interactions and graphic presentation of results, a prerequisite for providing decision
makers with really useful support tools. On the other hand, knowledge workers could rely on
autonomous processing tools which made them substantially independent of the company
information systems department, thus avoiding the above-mentioned time lag in data access. This
led the most proactive knowledge workers to create local databases and develop simulation
models, for example by means spreadsheets, which can be regarded as true ancestors of today’s
business intelligence architectures.

The initial concept of decision support system was also introduced. Later developments
brought to light new types of applications and architectures: executive information systems and
strategic information systems were first introduced toward the late 1980s to support executives in
the decision-making process. Such systems were intended for unstructured decision-making
processes and therefore represented passive support systems oriented toward timely and easy
access to information.

From the early 1990s, network architectures and distributed information systems based on client–
server computing models began to be widely adopted. Moreover, there arose the need to logically
and physically separate the databases intended for DSSs from operational information systems.
This brought about concepts of data warehouses and data marts.

Finally, toward the end of the 1990s, the term business intelligence began to be used to generally
address the architecture containing DSSs, analytical methodologies and models used to
transform data into useful information and knowledge for decision makers.

Definition of Decision Support System:

It is defined as an interactive computer system helping decision makers to combine data and
models to solve semi-structured and unstructured problems. This definition entails the three main
elements of a DSS shown in Figure: a database, a repository of mathematical models and a
module for handling the dialogue between the system and the users. It thus highlights the role of
DSSs as the focal point of evolution trends in two distinct areas: on the one hand, data processing
and information technologies; and on the other hand, the disciplines addressing the study of
mathematical models and methods, such as operations research and statistics.

11. DEVELPOOMENT OF DECISION SUPPORT SYSTEM

The following figure shows the major steps in the development of a DSS. The logical flow of the
activities is shown by the solid arrows. The dotted arrows in the opposite direction indicate
revisions of one or more phases that might become necessary during the development of the
system, through a feedback mechanism.

Planning.
The main purpose of the planning phase is to understand the needs and opportunities, and to
translate them into a project and later into a successful DSS. General and specific objectives of
the system, recipients, possible benefits, execution times and costs are laid down(called as
feasibility study). If one decides to proceed with the system, the planning phase should be
followed by the definition of the activities, tasks, responsibilities and development phases, for
which classical project management methodologies should be used.
Analysis. In the analysis phase, it is necessary to define in detail the functions of the DSS to be
developed, by further developing and elaborating the preliminary conclusions achieved during the
feasibility study. Here we also analyze the decision processes to be supported, to try to thoroughly
understand all interrelations existing between the problems addressed and the surrounding
environment. The organizational implications determined by a DSS should be assessed. The
analysis also involves mapping out the actual decision processes and imagining what the new
processes will look like once the DSS is in place. Finally, it is necessary to explore the data in
order to understand how much and what type of information already exists and what information
can be retrieved from external sources.

Design. The entire architecture of the system is therefore defined, through the identification of
the hardware technology platforms, the network structure, the software tools to develop the
applications and the specific database to be used. It is also necessary to define in detail the
interactions with the users, by means of input masks, graphic visualizations on the screen and
printed reports. A further aspect that should be clarified during the design phase is the make-or-
buy choice – whether to subcontract the implementation of the DSS to third parties, in whole or in
part.

Implementation. Once the specifications have been laid down, it is time for implementation,
testing and the actual installation, when the DSS is rolled out and put to work. Any problems faced
in this last phase can be traced back to project management methods.
Sometimes a project may not come to a successful conclusion, may not succeed in fulfilling
expectations, or may even turn out to be a complete failure. However, there are ways to reduce
the risk of failure. The most significant of these is based on the use of rapid prototyping
development where, instead of implementing the system as a whole, the approach is to identify a
sequence of autonomous subsystems, of limited capabilities, and develop these subsystems step
by step until the final stage is reached corresponding to the fully developed DSS.
A further aspect that should not be overlooked is the periodic administration and revision of the
DSS. it is necessary to develop a DSS by making provision for future changes and adjustments.