2. CHAPTER 1
BUSINESS INTELLIGENCE (BI)
Rapid advances in computer technology allow business Intelligence (BI)
systems to provide managers with access to a tremendous amount of data.
Many successful companies have been investing large sums of money in
business intelligence tools and technologies. They believe that up-to-date,
accurate and integrated information about their supply chain, products and
customers are critical for their very survival.
BI is becoming more and more popular of a topic with each passing year. The
term “Business Intelligence (BI)” was originally coined in 1865 by Richard Millar
Devens in the “Cyclopedia of Commercial and Business Anecdotes”.
BI makes companies smarter.
BI is the processes, technologies, and tools that help us change data into
information, information into knowledge and knowledge into plans that guide
Technologies for gathering, sorting, analyzing and providing access to data to
help enterprise users make better business decisions.
BI is a variety of software applications used to analyze an organization’s
raw data. BI can include data mining, online analytical processing, and
BI helps to make strategic, tactical and operational decisions by
providing right information to right person at right place at tight time.
BI is an enterprise wide platform that supports reporting, analysis and
BI is more about Intelligence.
BI can manage external as well as internal data which creates an
intelligence that cannot be derived from any singular set of data.
“A set of concepts, methods and process to improve business decisions
using information from multiple sources and applying experience and
assumptions to develop an accurate understanding of business
“BI is a system that turns data into information and them into
knowledge thereby adding substantial value to firm’s decision making
It is knowledge – knowledge about your customers, your competitors,
your business partners, your competitive environment and your own
internal operations that give you ability to make effective, important and
often strategic business decisions.
Objective of BI is to improve the timeliness and effectiveness of
Why Business Intelligence?
4. Phases of BI
Architecture of BI
Need / Benefits of BI
To remove guess work: BI provides accurate data to update in real time
and any other requirement to improve decision making in precise
manner. Therefore, to remove guess work, BI is required.
For quick responses to business related queries: BI provides framework
to get immediate answer to the business related query or question.
Thus, BI provides tools to improve responses about business related
5. Valuable insights into the customer behaviour: BI tools help to predict
market situation and consumer behaviour based on available data. Thus,
for accurate purchasing pattern and consumer behaviour, BI is required.
Developing efficiency: BI helps regarding data collection, conversion and
reporting same for communicate to the management regarding
efficiency, performance and operation. Thus, to improve efficiency BI is
To identify and control costs: It’s an important task for managers to
identify various costs incurred during business operations. BI tools help
to identify different costs regarding requiring a price statement on
formulating pricing policy. Moreover, BI tools help to control some costs
which increase the overall profit.
For maintaining inventory / Inventory control: BI software helps to
make right order for the inventory; it means the right quantity of right
inventory at the right time.
For overall analysis of business: BI system helps to know or identify
business statistics (Profit & losses, Overall performance, Information
regarding customer, employee, production etc.) over a given period of
time. This data related information help to take an advantage over the
competition as well as sustain business.
Features / Characteristics of BI
Fact based decision making: Decision made through BI are purely based
on facts and history. BI provides flow of data to the business system.
Single version of truth: It means some type of data or same data
available at more than one place and all such data should agree
completely and every respect.
360 degree perspective of the business: BI allows looking at the
business for various perspectives. Each person in the project team (BI
system) will look at the data from his/her goal and will look for
attributes that add value for decision making on his/her role.
Virtual team members on the same page: In today’s business, team of
people who work for common project but are spread across
geographical location is long known as a ‘virtual team’. Technologies like
business intelligence bring them together and provide them some fact of
the speed of life in personalized form.
Others: There are some common feature require in BI system like; Data
sources, Data filters/drill down, Security, Self-service, Data visualisation,
Mobile application etc.
6. Uses / Applications of BI
Business report categories: This includes the actual and how actual
operations meet against the goals. Here, BI can be used for preparing
standard weekly per month reports. For better decision making, BI can
be used for smooth business operations.
Forecasting: Forecasting is an important tool for business decisions and
operations. Without forecasting business cannot be performed well.
Thus, BI can be used through estimated required tool as well as
forecasting activities for the business functions.
For multidimensional Analysis: Such analysis offers good insights to the
managers as per the requirement. Such analysis required sound data
warehousing or data mart as well as constant flow of the data. BI
provides all the required tools for multidimensional analysis.
To find correlation among different factors: For high level decision
making, it is required to find correlation between different functions
within and outside the business. Such analysis can only be programmed
with the help of BI.
To manage business at all level and in all sectors: In today’s
competition it’s required to manage business through large number of
data, communicated many forms of media. BI can be used to handle
such flow of data and analyse as per the requirements. BI is now a day
applicable to every sector of business likes, retail, healthcare,
transportation, insurance, banking and others.
Customer Relationship Manager
Web & E-Commerce Analytics
Senior Executives: To monitor firm’s activities using dashboards &
Middle Managers: To enter the data, queries etc., slicing-dicing the data.
Business Analysts: To forecast
Other employees, customers, suppliers: To looking forward for reports
for making decisions.
7. BI Components
Business Intelligence combines
1. Data warehousing,
2. Business analytics,
3. Business Performance Management,
4. User interface.
Data Ware house has subject oriented data. This subject oriented data
could be information such as sales, customer name, etc.
Data warehouse excludes information, which is not useful for decision-
Data warehouse provides information with time as function. This gives
historical perspective to the information.
A data warehouse (DW) is a pool of data produced to support decision
making; it is also a repository of current and historical data of potential
interest to managers throughout the organization.
Business Analytics is a set of techniques and processes that can be used
to analyze data to improve business performance through fact-based
Business analytics are made up of statistical methods that can be applied
to a specific project, process or product.
Business Performance Management (BPM)
BPM is a set of performance management and analytical processes that
enables the management of an organization’s performance to achieve
one or more pre-selected goals.
It enables an organization to enhance the management of their business
performance through the aid of reports, analytics, Key Performance
Indicators, etc. that help them measure and monitor efficiency and
success of their business activities.
In IT, the User Interface (UI) everything designed into an information
device with which a person may interact.
8. This can include display screens, keyboards, a mouse and the
appearance of a desktop.
It is also the way through which a user interacts with an application or a
User interface, also sometimes called a human-computer interface,
comprises both hardware and software components. It handles the
interaction between the user and the system. There are different ways
of interacting with computer systems which have evolved over the
years. There are five main types of user interface:
o Command Line Interface : Command line interfaces are the oldest
of the interfaces discussed here. It involves the computer
responding to commands typed by the operator. It means
interacting with a computer program where the user issues
commands to the program in the form of successive lines of text.
o Graphical UI: Graphical user interfaces (GUI) are sometimes also
referred to as WIMP because they use Windows, Icons, Menus
and Pointers. Operators use a pointing device (such as a mouse,
touchpad or trackball) to control a pointer on the screen which
then interacts with other on-screen elements. It allows the user to
interact with devices through graphical icons and visual indicators
such as secondary notations.
o Menu Driven A menu driven interface is commonly used on cash
machines (also known as automated teller machines (ATM's),
ticket machines and information kiosks (for example in a
museum). They provide a simple and easy to use interface
comprised of a series of menus and sub-menus which the user
accesses by pressing buttons, often on a touch-screen device.
o Form Based A form-based interface uses text-boxes, drop-down
menus, text areas, check boxes, radio boxes and buttons to create
an electronic form which a user completes in order to enter data
into a system. This is commonly used on websites to gather data
from a user, or in call centres to allow operators to quickly enter
information gathered over the phone.
o Natural Language A natural language interface is a spoken
interface where the user interacts with the computer by talking to
it. Sometimes referred to as a 'conversational interface', this
9. interface simulates having a conversation with a computer.
Commonly used by telephone systems as an alternative to the
user pressing numbered buttons the user can speak their
responses instead. This is the kind of interface used by the
popular iPhone application called Siri and Cortana in Windows.
10. CHAPTER 2
BUSINESS ANALYTICS (BA)
“Data is what you need to do analytics. Information is what you need to do
business.” – John Owen
The word analytics has come into the foreground in last decade or so.
Analytics is a field which combines data, information technology,
statistical analysis, quantitative methods and computer-based models
This all are combined to provide decision makers all the possible
scenarios to make a well thought and researched decision. The
computer-based model ensures that decision makers are able to see
performance of decision under various scenarios.
In the era of knowledge economy, getting the right information to
decision makers at the right time is critical to their business success. One
such attempt includes the growing use of business analytics. It provides
a competitive advantage to companies.
BA is the use of data with the help of Information Technology, Statistical
analysis, Quantitative Methods, Mathematical/ Computer based models.
The purpose of using BA is to help managers gain improved insight about
their business operations and make better-fact based decisions.
Business Analytics is the subset if Business Intelligence, which creates
capabilities for companies to complete in the market effectively and is
likely to become one of the main functional areas in most companies.
Analytics companies develop the ability to support decisions through
11. BA is the methodical exploration of an organization’s data with emphasis
on statistical analysis. BA is used by companies committed to data-
driven decision making.
“Extensive use of data, statistical and quantitative analysis, exploratory
and predictive models, and fact based management to drive decisions
“The scientific process or discipline of fact based problem solving.”
“The application of process and techniques that transform raw data into
meaningful information to improve decision making.”
Today’s businesses are growing increasingly digital and are capable of
accurately measuring every aspect of their operations, from marketing to
human resources, in real time.
Provides faster and more accurate decisions: It enables businesses to
stay on top of the market by revealing sentiments towards the company
as well as its competitors.
Minimizes risk: It offers valuable insights to help business make the right
choices based on performance, consumer behavior and trends.
Organized works: Having accurate information, the team is able to work
together in an organized manner to come up with a plan that will bring
higher chances of success fir the company. In this day and age, there are
already available business analysis software and applications that enable
managers to keep up with advanced consumer shopping trends and also
project future trends.
Assessment of previous business performance: It is used to predictive
analysis, which is typical used to assess previous business performance.
It also clears picture of what is being worked and what is not.
Pricing decisions: Business analytics is used to determine pricing of
various products in a departmental store based past and present set of
information, i.e. Movies theatre.
Disseminate (circulate) information to relevant stakeholders through
interactive dashboards and reports: It is used for sharing information to
12. internal and external stakeholders of the company. So many stake
holders can make proper decisions on their basis.
Improved customer service: It keeps track of a frequent customer query
which prevents business from repeating mistakes and improving
Merchandising: It helps to determine what sell and buy.
Social Media: It helps to understand trends and customer perception
which will help managers and product designers.
Components of BA
1. Business Context
3. Data Science
Any business analytics project starts with a business context and
continue with asking the right questions. To help with business decisions, one
should ask questions, he/she wants to gain insights into before starting the
data collection process.
Based on the company’s strategy, goals, budget and target customers,
one should prepare a set of questions that will help him/her through the data
Technology is also necessary to analyze the data. IT can be used for
acquisition, storage, preparation, analysis and dissemination of data.
Companies use much software for analysis.
Technology is important to implement solution. E.g., in the case of
targeted advertising, technology can be used to personalize advertisements to
be sent to individual customers.
It is the most significant component. It comprises of statistical
techniques, deep learning, machine learning etc. The aim of these components
is to identify the best fit technique in current context. Multiple models are
developed for solving the problems using available techniques and some more
of them are selected for deployment of the business analytics solution.
13. Types of Business Analytics
90% of organizations today use descriptive analytics which is the most
basic form of analytics. It is the easiest and quickest part.
It helps in answering the question: “What has happened?”
It is the simplest class of analytics, one that allows you to condense big
data into smaller, more useful information.
The main objective of descriptive analytics is to find out the reasons
behind precious success or failure in the past.
14. Most of the social analytics are descriptive analytics. They summarize
certain groupings based on simple counts of some events. The number of
followers, likes, posts, fans is mere event counters. These metrics are used for
social analytics like average response time, average number of replies per post,
number of page views, etc. that are the outcome of basic arithmetic
The vast majority of big data analytics used by organizations falls into
the category of descriptive analytics.
The subsequent step in data reduction is predictive analytics.
It is used by business to study the data to find answers to the question
“What could happen in the future based on previous trends and patterns?”
The purpose of predictive analytics is not to tell you what will happen in
the future. It cannot do that. In fact, no analytics can do that. Predictive
analytics can only forecast what might happen in the future, because all
predictive analytics are probabilistic in nature.
It helps predict the likelihood of a future outcome by using various
statistical and machine learning algorithms but the accuracy of predictions is
not 100%, as it is based on probabilities. Organizations should capitalize on
hiring a group of data scientists who can develop statistical and machine
learning algorithms to leverage predictive analytics and design an effective
It is the next step of predictive analytics that adds the spice of
manipulating the future.
Prescriptive analytics advises on possible outcomes and resultant in
actions that are likely to maximize key business metrics. It basically uses
simulation and optimization to ask “What should a business do?”
Prescriptive analytics are comparatively complex in nature and many
companies are not yet using them in day-to-day business activities, as it
becomes difficult to manage.
Prescriptive analytics if implemented properly can have a major impact
on business growth. Large scale organizations use prescriptive analytics for
scheduling the inventory in the supply chain, optimizing production, etc. to
optimizing customer experience.
15. Business Intelligence versus Business Analytics
Intelligence is what you have and Analytics is what you do with it.
Meaning BI is a software application which
can include data mining, OLAP
and business reporting.
BA is the methodical exploration of the
data with emphasis on statistical
Objective To collect and represent data in
an understandable manner
To derive insights and understanding.
Example Dashboard, Report Generation,
Data Mining, Predictive Modeling,
Focus Present Present and Future
Data type Structured Data Structured and Unstructured Data
Users It includes business users It includes data scientists, Business
Analyst, Business users
When did it happen?
Who is accountable for
Where did it happen?
Why did it happen?
Will it happen again?
What will happen if we change
What is the best that can
Statistical Quantitative analysis
Text / Multimedia mining etc.
OLAP – Online Analytical Processing
OLAP is computer processing that enables a user to easily and selectively
extract and view data from different points of view.
In 1993, E. F. Codd, an English Computer scientist who, while working for IBM,
invented the relational model for database management, came up with the
term online analytical processing (OLAP) and processed 12 criteria to define
It is a category of software tool which provide analyses of data for the business
decisions. OLAP system allows users to analyse database information from
multiple database system at one time. The primary objective of OLAP is data
analysis and not data processing.
For example: any data warehouse system is an OLAP system, uses of OLAP are;
A company might compare its mobile phone sales in September
with the sales in October, and then compare those results with
another location which may store in a separate database.
Amazon analysis, purchase by its customers to come-up with a
personalised home page with product which likely interest to their
It is used to derive summarized information from large volume database.
It also helps to generate automated reports for human view.
It supports multidimensional view of data.
It provides fast & efficient access to view various information.
In OLAP, complex query will be executed only.
It is easy to analyze the information by processing complex query on
multi dimensional view of data.
DW is generated to analyze the information where large amount of
historical data can be stored in OLAP Database.
Information of DW is related to more than one dimensional, e.g. Sales,
Marketing, generating pattern from supplier.
Types of OLAP Servers
17. We have four types of OLAP servers −
Relational OLAP (ROLAP)
Multidimensional OLAP (MOLAP)
Hybrid OLAP (HOLAP)
Desktop OLAP (DOLAP)
1. Relational OLAP
In ROLAP, data is stored in a relational database. In essence, each action
of slicing and dicing is equivalent to adding a “WHERE” clause in the SQL
Can handle large amount of data
Can leverage functionalities inherent in the relational
Difficult to perform complex calculation using SQL.
Performance can be slow.
2. Multidimensional OLAP
In MOLAP, data is stored in a multidimensional cube. The storage is in
proprietary formats and not in the relational database
18. Fast data retrieval
Optimal for slicing and dicing
Can perform complex calculations.
Limited in the amount of data that it can handle. The reason being as all
calculations are pre-generated when the cube is created, it is not
possible to include a large amount of data.
Additional investment in human and capital resources may be required
as the cube technology is proprietary and might not exist in the
3. Hybrid OLAP
Hybrid OLAP is a combination of both ROLAP and MOLAP. It offers higher
scalability of ROLAP and faster computation of MOLAP. HOLAP servers allow
storing the large data volumes of detailed information. The aggregations are
stored separately in MOLAP store.
19. 4. Desktop OLAP (DOLAP)
The OLAP which communicates with DESKTOP DATABASES to retrieve the
data is called DOLAP. Here, cubes are stored in desktop also.
There are different kinds of operations that we can perform on OLAP.
Slice and dice
Pivot / rotate
Roll-up performs aggregation on a data cube in any of the following ways −
By climbing up a concept hierarchy for a dimension
By dimension reduction
The following diagram illustrates how roll-up works.
20. Roll-up is performed by climbing up a concept hierarchy for the
Initially the concept hierarchy was "street < city < province < country".
On rolling up, the data is aggregated by ascending the location hierarchy
from the level of city to the level of country.
The data is grouped into cities rather than countries.
When roll-up is performed, one or more dimensions from the data cube
Drill-down is the reverse operation of roll-up. It is performed by either of the
following ways −
By stepping down a concept hierarchy for a dimension.
By introducing a new dimension.
The following diagram illustrates how drill-down works −
21. Drill-down is performed by stepping down a concept hierarchy for the
Initially the concept hierarchy was "day < month < quarter < year."
On drilling down, the time dimension is descended from the level of
quarter to the level of month.
When drill-down is performed, one or more dimensions from the data
cube are added.
It navigates the data from less detailed data to highly detailed data.
The slice operation selects one particular dimension from a given cube and
provides a new sub-cube. Consider the following diagram that shows how slice
Here Slice is performed for the dimension "time" using the criterion time
It will form a new sub-cube by selecting one or more dimensions.
Dice selects two or more dimensions from a given cube and provides a new
sub-cube. Consider the following diagram that shows the dice operation.
The dice operation on the cube based on the following selection criteria
involves three dimensions.
The pivot operation is also known as rotation. It rotates the data axes in view
in order to provide an alternative presentation of data. Consider the following
diagram that shows the pivot operation.
24. Advantages of OLAP System:
Multidimensional data representation.
Consistency of information and calculations.
“What if” analysis.
Provides a single platform for all information and business needs –
planning, budgeting, forecasting, reporting, and analysis.
Fast and interactive ad hoc exploration.
Drawbacks of OLAP
Implementation and maintenance are depending on IT progression
which takes high costs.
OLAP tools need cooperation between people of various departments to
be effective which might not always be possible.
Slow in reacting.
Great dependence on IT.
25. OLTP (Online Transaction Processing)
It is a popular data processing system in today’s enterprise. It is
characterized by a large number of short transactions (INSERT, UPDATE, and
This system has detailed day to day transaction data which keeps
changing on everyday basis. In OLTP database, there is detailed and current
OLTP is a category of data processing that is focused on transaction-
oriented tasks. OLTP typically involves inserting, updating, and/or deleting
small amounts of data in a database. OLTP mainly deals with large number of
transactions by a large number of users.
Purchasing a book online
Booking an airline ticket
Financial transaction system
Features of OLTP
It is one of the hottest technologies of the 90’s.
It stores current data.
It is transaction oriented.
There are transactions that involve small amounts of data.
A large number of users are there.
It facilitates and manages transaction oriented applications, typically for
data entry and retrieval transactions in a number of industries, including
banking, airlines, supermarkets and manufactures.
It facilitates the development of online transaction applications.
OLTP transactions are usually very specific in the task that they perform,
and they usually involve a single record or small selection of records.
OLAP is often used to provide analytics on data that was captured via an
Easy and best solution for online shoppers.
Very easy to use as simple as fill a form and the rest will be taken care of
by the web and database servers.
26. Online banking is completely based in online transaction processing
You can access anything on the web and choose to buy it because all
financial transaction methods are supported by this system.
At times, there occur millions and millions of requests at a time which
gets difficult to handle.
During purchases even if the servers hang for few seconds a large
number of transactions get affected, in turn affecting the organizations’
Databases store all user data and account information, if these servers
are hacked, it could lead to financial and personal problems.
Electricity problem is another issue.
The fundamental of operation of online transaction system is atomicity.
Atomicity ensures that if any step fails in the process of the transaction,
the entire transaction must fail.
OLTP Vs OLAP
We can divide IT systems into transactional (OLTP) and analytical
(OLAP). In general we can assume that OLTP systems provide source
data to data warehouses, whereas OLAP systems help to analyze it.
27. OLTP (On-line Transaction Processing) is characterized by a large number of
short on-line transactions (INSERT, UPDATE, and DELETE). The main emphasis
for OLTP systems is put on very fast query processing, maintaining data
integrity in multi-access environments and an effectiveness measured by
number of transactions per second. In OLTP database there is detailed and
current data, and schema used to store transactional databases is the entity
model (usually 3NF).
OLAP (On-line Analytical Processing) is characterized by relatively low volume
of transactions. Queries are often very complex and involve aggregations. For
OLAP systems a response time is an effectiveness measure. OLAP applications
are widely used by Data Mining techniques. In OLAP database there is
aggregated, historical data, stored in multi-dimensional schemas (usually star
The following table summarizes the major differences between OLTP and OLAP
Features OLAP OLTP
Consolidated Data: Data
comes from various
Operational Data: OLTPs are
original source of the data.
Basic It is used for data analysis It is used to manage very large
number of online short
It uses data warehouse It uses traditional DBMS
Processing is little slow In Milliseconds
Normalization Tables in OLAP database are
Tables in OLTP database are
Query Mostly select operations Insert, Update, and Delete
information from the
Data Integrity OLAP database does not get
frequently modified. Hence,
data integrity is not an issue.
OLTP database must maintain
data integrity constraint.
Data quality The data in OLAP process
might not be organized.
The data in the OLTP database
is always detailed and
Audience It is a customer orientated
It is a market orientated
User type Used by Data knowledge
users like workers, managers,
It is used by Data critical users
like clerk, DBA & Data Base
Purpose Designed for analysis of
business measures by
category and attributes.
Designed for real time
This kind of Database allows
only hundreds of users.
This kind of Database users
allows thousands of users.
Challenge An OLAP cube is not an open
SQL server data warehouse.
knowledge and experience is
essential to manage the OLAP
Data Warehouses historically
have been a development
project which may prove
costly to build.
Process It ensures that response to
the query is quicker
It provides fast result for daily
Characteristic It lets the user create a view
with the help of a
It is easy to create and
DATA MODEL FOR OLAP
A multi dimensional can exist in the form of a star schema,
A Snowflake schema
A Star model
It is the simplest of data warehousing schema. It consists of a large
central table (called fact table) with no redundancy. The central table
is being referred by a number of dimension tables. The schema graph
29. looks like a starburst. The star schema is always very effective for
In the star schema, the fact table is usually in 3NF of higher form of
normalization. All the dimension tables are usually in de-normalized manner,
and the highest form of normalization they are usually present in is 2NF.
The dimension tables are known as lookup or reference tables.
The following figure shows the snowflake model. There is a central fact table
connected to four dimensions. The ‘product’ dimension is further normalized
to ‘product category’ dimension. Similarly, the ‘employee’ dimension is further
normalized to the ‘department’ dimension. By now, you would have guessed
that normalization of the dimension tables definitely helps in reducing
redundancy; however it adversely impacts the performance as more joins will
needed to execute a query.
30. DATA MODEL FOR OLTP
An OLTP system adopts an Entity Relationship (ER) model. Entity Relationship
(ER) Modelling is a logical design technique whose main focus is to reduce data
redundancy (Idleness). It is basically used for transaction capture and can
contribute in the initial stage of constructing a data warehouse. The reduction
in the data redundancy solves the problems of inserting, deleting and updating
data. The whole process ended up with creation of lots of tables and joins
between these tables. It results a massive spider web of joins between tables.
31. The figure shows an Entity Relationship (ER) data model for OLTP. We
have considered following three Entities;
Employee (Employee ID is the primary key).
Employee Address (Employee ID is a foreign key referencing to the
Employee ID attribute of Employee entity.)
Employee Pay History (Employee ID is a foreign key referencing to the
Employee ID attribute of Employee entity).
For these entities, we see the following Relationships;
a. There is a (1: M cardinality) between Employee and Employee Address
entities. This means that an instance of Employee entity can be related with
multiple instances of Employee Address entity.
b. There is also (1: M cardinality) between Employee and Employee Pay History
entities. This means that an instance of Employee entity can be related with
multiple instances of Pay History entity.
The degree of relationship (also known as cardinality) is the number of
occurrences in one entity which are associated (or linked) to the number of
occurrences in another. There are three degrees of relationship, known as:
I. one-to-one (1:1)
II. one-to-many (1: M)
III. many-to-many (M: N)
One-to-Many (1: M): It is, where one occurrence in an entity relates to many
occurrences in another entity. For example, taking the employee and
department entities shown on the previous page, an employee works in one
department but a department has many employees. Therefore, there is a one-
to-many relationship between department and employee.