Business Intelligence MBA II ND SEMESTER
Business Intelligence MBA II ND SEMESTER
Business Intelligence MBA II ND SEMESTER
ON
“ BUSINESS INTELLIGENCE ”
SUBMITTED BY:
RISHAB GARG
This is to certify that I have completed this project titled “Business Intelligence.” under the
guidance of Mr. Yashwant Kumar in the partial fulfillment for the award of degree of Masters
Of Business Administration at Bharati Vidyapeeth Deemed University School Of Distance
Education, Delhi. This is an original piece of work and I have not submitted it earlier elsewhere.
RISHAB GARG
At the very outset I wish to thank Mr. Yashwant Kumar, for giving me the opportunity to
participate in this interesting research project, which helped me to gain insights into the
infrastructural research on “Business Intelligence.”
I am grateful to him to have spared his time and showing the patience to answer my queries.
The Kindness shown by him in spite of being so busy with his work is highly appreciated.
I would also like to thank all my fellow colleagues who supported me all the time. ThisEnsured
the prompt of this project.
(Rishab Garg)
Business intelligence (BI) has become an expected business competency for improving decision-
making effectiveness. Leading enterprises are establishing competency in aspects of BI such as
decision modeling and support so that all workers, managers and executives can take the most
effective action in a given business situation.
Enterprises demonstrating quantifiable success with BI travel an evolutionary path, starting with
basic data and analytical tools, and culminating with BI that has become a full-blown
competency intrinsic to the business’s culture.
Traditionally, business intelligence (BI) has been used for performance reporting and as a
planning and forecasting tool by the few people in the enterprise who use historical data to
gain insight into the future. There is no one definition of BI, so IT and the business may
understand it differently: IT usually sees BI as a tool; the business sees BI as information.
Moreover, neither may see BI’s value as simply enabling workers to make impactful
decisions at the moment of need.
LIST OF TABLES
Chapter-1 1-22
INTRODUCTION
Chapter-2
RESEARCH METHODOLOGY
23-24
Chapter-3
Chapter-4
CONCLUSION 31-32
Chapter-5
RECOMMENDATIONS 33-34
Chapter-6
BIBILIOGRAPHY 37
ANNEXURE
38- 40
CHAPTER 1
INTRODUCTION
Business intelligence (BI) refers to the collection and analysis of data in order to produce
insights that will improve a company’s processes. There is a lot packed into that definition and,
as a result, a lot of the confusion around BI stems from the assumption that it stops with analysis.
Although the distinction gets muddy sometimes, business intelligence can be thought of as the
end goal of business analytics because it produces the actionable insights a business needs to
make informed decisions. In order to do this, effective business intelligence needs to meet four
major criteria:
1. Accuracy
This refers to the accuracy of the data inputs as well as outputs. The two are, of course,
related. Any system that requires analysis can fall prey to the garbage in, garbage
out (GIGO) problem, in which tainted data can ruin results, even when the analytical
model is sound. In order to get accurate answers (output), the data going in must be
accurate and relevant to the questions the business is seeking to answer.
It is often impractical to try to dump all the data produced by a company into an
analytical model and expect it to make sense of everything from production numbers to
employees' marital status. This is why human discretion is often used to select the data
that is relevant to a particular problem. That said, this selection can be over-exercised or
simply done wrong, bringing us back to the GIGO problem.
2. Valuable Insights
Not all insights are valuable. Knowing the handedness (left or right) of the majority of
your customers may be useful for a baseball glove manufacturer, but would be of less use
to a shoe manufacturer. Although crunching all the data to find out something that was
previously unknown can be satisfying, BI should offer concrete insights. For example, if
analysis showed a sports store that many customers who purchased baseball gloves also
purchased running shoes, the owner could rearrange the store displays to cluster shoes
and gloves for customer convenience, or separate them to different corners of the store to
maximize the chances of browsing.
3. Timeliness
Getting accurate and valuable insight is only half the battle. Business intelligence must
also be able to deliver those insights at the right time. If the aforementioned sports store
only discovers the glove and running shoe correlation in December rather than at the start
of the buying trend, it may lose the opportunity to capitalize on that information.
There are two parts to timeliness: the timeliness of the data going in and the timeliness of
the insights coming out. Businesses have different decision time frames depending on
what they do. A retail outlet will likely want to be feeding very timely sales information
into BI with the hope of getting timely insights to be implemented on a monthly, weekly
or even daily basis. Longer-term operations like an oil and gas exploration and
production company may only be interested in insights on a quarterly or yearly basis.
4. Actionable
The final hurdle for any type of business intelligence is to provide insights that can be
acted upon. To some extent, this means gaining an understanding of practical constraints.
For example, virtually any company could become more efficient if it had unlimited
capital to upgrade of all its equipment. So, good business intelligence should identify the
upgrade that will produce the most return or, better yet, other utilization schemes that
would make the most of existing assets. In other words, business intelligence should
provide insight beyond what is obvious and work within a company's unique constraints
to deliver actionable ideas designed to improve a business's processes and, ultimately, its
profitability.
Today it’s understood as a set of analyses that derive value and insight from data.
“Throughout Holland, Flanders, France, and Germany, he maintained a complete and perfect
train of business intelligence,” Devens writes of Furnese. “The news…was thus received first by
him.”
Furnese ultimately used this advance knowledge to duplicitous ends and became renowned as a
corrupt financier. The idea of gathering information on business conditions, however, was a seed
that would grow.
Origins and Development until 1958
Technology did not advance to the point where it could be considered an agent of business
intelligence until well into the 20th century.
It was with the 1958 publication of a landmark article on the subject, written by IBMcomputer
scientist Hans Peter Luhn, that the potential of BI was recognized.
The article, titled “A Business Intelligence System”, described “an automatic system…developed
to disseminate information to the various sections of any industrial, scientific, or government
organization.” In the wake of the post-World War II boom, such sectors required a way to
organize and simplify the rapidly growing mass of technological and scientific data.
Luhn also cited Websters Dictionary definition of intelligence: “the ability to apprehend the
interrelationships of presented facts in such a way as to guide action towards a desired goal.”
Essentially, this cut to the core of what BI is: a way to quickly and easily understand huge
amounts of information so that the best possible decisions can be made.
Luhn’s work did more than introduce and expand the possibilities of a concept. His research
established methods that were built upon to create some of IBM’s touchstone analytical systems.
With the advent of computers in the business world, companies finally had an alternative to
storing data on paper.
IBM’s invention of the hard disk in 1956 revolutionized data storage. Floppy discs, laser discs,
and other storage technologies meant that just as more and more data was being created, so too
were there more and more places to store it.
This spawned the creation of the first database management systems, collectively referred to as
decision support systems (DSS). By the 1970’s a few BI vendors popped up with tools that made
accessing and organizing this data possible.
But it was a new and clumsy technology. Most importantly, it was very difficult to use.
A 1988 international conference aimed to streamline data processes. The Multiway Data
Analysis consortium, held in Rome, was a landmark in simplifying BI analysis.
The modern phase of business intelligence began immediately after the 1988 conference.
In 1989 Gartner analyst Howard Dresner again brought the phrase “business intelligence” into
the common vernacular. He employed it as a general term to cover the cumbersome-sounding
names for data storage and data analysis, names like DSS and executive information system
(EIS).
Competition from more vendors in the field led to advances including data warehouses. This new
tool improved the flow of data as it moved from operational systems to decision support.
Data warehousing drastically cut the time it took to access data. Data that traditionally had been
stored in multiple places was now all in a single location.
Along with this development came supplemental facets of data warehousing that are staples of
BI today. These included Extract, Transform, and Load (ETL) tools and Online Analytical
Processing (OLAP) software.
In later years, this phase of development became known as business intelligence 1.0.
As business intelligence became a commonly known phrase in the late 1990’s and early 2000’s,
dozens of new vendors hit the market.
During this period, there were two basic functions of BI: producing data and reports, and
organizing it and visualizing it in a presentable way.
Yet there remained two significant issues holding back this developing phase of the technology:
complexity, and time.
Too many projects were owned by the IT department, meaning that most users were still not
capable of executing BI tasks on their own. Existing BI tools had not been developed with
anyone but experts in mind, and extensive analytics training was required to gain insights.
And because data was siloed, it took more time to formulate and deliver reports to decision
makers.
Only expert technical experts were able to utilize advanced data analysis software. Tools began
to evolve to cater to non-technical users, but it happened slowly.
The dawn of the 21st century marked a distinct turning point, as technologies developed to
address issues of both complexity and speed. They were also bolstered by the onset of Cloud-
based programs that expanded and simplified the reach of BI platforms.
BI 2.0 included a host of different technologies such as real-time processing, which incorporated
information from events as they happened into data warehouses, allowing companies to make
decisions based on the most recent information available.
Other technologies that came into play included self-service access for non-expert users,
meaning that employees could now complete projects without interference from the IT
department.
The exponential growth of the Internet supported and advanced these developments, in part
through the genesis of social networking tools. Facebook, Twitter, and blogs gave users very
simple and very quick ways of sharing ideas and opinions.
It also provided a way for users to review methods and software, and more broadly disseminate a
basic understanding of the different uses of business intelligence. The more that people
communicated, the more that they understood.
By 2005, the increasing interconnectivity of the business world meant that companies needed
real-time information, for a host of reasons. Chiefly they needed to keep abreast of the
competition, and understand what their consumers wanted and what they thought of their
company.
Empowering End Users into the Modern Day
The agility and speed of the mid-2000s business intelligence platform has undergone an intense
refining process.
Tool specification, expanding self-service options, and improving visualization are three of the
most important traits of the next frontier of BI evolution.
BI tools in the present day are often designed with a very specific industry in mind, be it
healthcare, law enforcement, or even professional sports. Known as “software verticalization,”
this growth of industry-specific tools has contributed significantly to increased adoption of
business intelligence.
Self-service tools and visualization features rely on one another for their growth.
The big data revolution and explosion of the Internet left organizations with more data than
before. Each person creates increasingly large amounts of information. Over 204 million emails
are sent per minute.
Companies required even more visualization tools to actionably make sense of it.
Visualization tools began to evolve to include the end-user even more. More platforms
empowered users to complete self-service access, meaning that they could explore and utilize
their data on their own, without training.
As more companies offered these capabilities, unique, cutting-edge attributes became the only
way to stay ahead of the curve.
One way to achieve both was through cloud BI, which hosts the software on the Internet,
reducing storage costs and making access to organizational data and insights faster and more
convenient.
The BI Process
So what exactly is being done in the black box of business intelligence? The business
intelligence process is very similar to the Deming cycle. It has four broad steps that loop over
and over (the buzzword for this is continuous improvement, or Kaizen)
1. Data Gathering: Data sources are identified, and the data is collected and converted into a
format that can be analyzed.
2. Analysis and Action: The data is analyzed and a course of action is taken.
3. Measurement: The results of the action are measured using a chosen model.
4. Feedback: The results of the action are used as another data point to make ongoing
improvements to the BI process.
Business Intelligence in Action
BI is a Deming cycle applied across an organization and all of its business lines. It is usually
facilitated by technology. In this view, the software merely helps make this process much easier
to implement and allows for a larger sample of data to be included in the analysis. At the end of
the day, however, BI is only effective if it is trusted and used to guide human decisions. That
said, the leaps BI has made in guiding large organizations has helped give it a considerable
amount of credibility in the world of business. This means many companies want BI – even if
they don’t entirely understand it.
Advertising
Business intelligence has become an important asset for all companies that want to reach the next
level. What is business intelligence? Business intelligence (BI) is a technology-driven process
for analysing data and presenting actionable information to help executives, managers and other
corporate end users make informed business decisions. It comprises the strategies and
technologies used by enterprises for the data analysis of business information.
Business intelligence as a process can be free, but the tools that will allow you to automate the
process are something that will require investments. Some areas where business intelligence can
be used in small businesses are:
Customer Satisfaction
Business intelligence can help boost your company’s ability to analyse consumer trends. With
the help of business intelligence tools, it is easy to identify the demand and supply trends
amongst your target audience, and specifically in your industry. This is beneficial since getting
such great insights into consumer behavior can help a business plan out it's strategy, production,
manufacturing, and budgeting accordingly.
The reason this helps increase customer satisfaction is based on past and current trends,
businesses offer products and services to their customers the way they like it. Business
intelligence is helping improve relationships between businesses and customer, thus
affecting customer relationship management.
Business intelligence brings a better return on investment for businesses. The reasoning behind
this is simple. The more businesses get an insight into the workings, trends and analytics of their
business processes, the more aware they are. This strategic awareness leads to faster reporting,
lowering the operating costs etc and can help produce products that match the requirements of
the consumers.
BI helps kick in a more effective process or way of working for the business. This in turn
directly affects the revenue generation of a business.
Increases Efficiency
As we get a greater insight into the data recorded and analyzed by BI, we can also expect an
increase in the efficiency of the business, products, and services offered. Why? Because the more
we understand our business needs, the more we can detect early mistakes.
Greater insight into these faults is bound to clear the bottlenecks in the sales funnel, thus
improving the efficiency of your business services/products. This in turn also helps attract more
leads and build a strong foothold in the market.
Apart from being an important contributor in the decision-making process, BI also helps you
gain a competitive edge over competitors in your market by keeping you constantly updated on
the current market trends. By knowing the trends that are prevalent in your industry, your
business and make informed decisions that will ensure that you stay on top and ahead of your
competitors.
The study will help the society to know their efficiency and effectiveness towards
Business Analytics.
Business Analytics analysis the way to improve customer relations within the market .
The study will help the society to work towards the analytics of business so that they
can get right quality and right quantity within the market.
The analytics provided by business intelligence applications are consistent. What business
intelligence must counter is the inconsistency that comes from the human decision-making
process. Different people will look at the data umbrella and see two very different outcomes,
which means an organization must spend time finding middle ground.
Business intelligence may require employees to use personal devices to access this information.
The data collected on a targeted demographic may be considered private information by some
that they don’t want to have used by an organization. Even when all care is taken, there is still a
blending of boundaries within BI that can make some people quite uncomfortable.
If you’re using mobile BI applications, then the threat of hacking can put your sensitive or
proprietary information at risk. Data hacks at Target, Home Depot, and other retailers prove that
other systems aren’t 100% safe either. Unless your system is completely disconnected from an
online portal, the threat of have a security breach is something which must always be proactively
considered.
Even companies that use mobile or Cloud-based solutions for their BI applications may struggle
with the costs involved in data management. Business intelligence as an industry has several
vendors and their software as a service can be very different from one another. Not every vendor
has transparent pricing either. Add in the data overages or premium services and even a cheap
pay-as-you-go system might be too much.
5. Regulations are evolving for business intelligence.
Precedents for litigation over unsecured data are being set right now. Legal sanctions or
regulatory sanctions can even be placed on organizations who experience a data breach. As
technologies change, the regulations also change, and if a company isn’t evolving their BI
applications, they could put themselves at risk for a catastrophic loss should a security event
occur.
Business intelligence is available for most companies today, but not every industry is well-
developed. Some are just starting to embrace what BI applications can provide. Others have been
managing their big data for decades successfully. Not every business will experience this
disadvantage, but those that do could find limited availability for some time to come.
There are numerous business intelligence options available today. Many of them will provide
one specific surface. To get the most out of your big data, you may need to invest into an entire
umbrella of services to make sure your BI is fully integrated.
The pros and cons of business intelligence generally show that the benefits far outweigh the
disadvantages that come from implementing big data solutions. Look for software applications
and interactive tools to get the most out of the information you already have so you can make the
good business decisions that will keep you in the black year after year.
LITERATURE REVIEW
Business Intelligence There is another issue with a great number of definitions; they tend to
change after some time, in light of the fact that the way of what they consider changes. This is
the situation with BI for instance. Initially, software business engaged with BI, BI used to be
comprehended as private insight, rather than state or open knowledge. Even after many years, BI
is still used by engineers and programmers (Solberg Søilen, 2015). BI is characterized as
frameworks that gather, change, and present organized information from various sources
lessening the required time to acquire significant business data and enable their efficiency use in
management decision making process (Den Hamer, 2004), permitting dynamic enterprise
information look, recovery, examination, and clarification of the necessities of administrative
choices (Nofal and Yusof, 2013). As indicated by Tyson (1986), BI concentrates on gathering,
process and present information concerning customers, contenders, the business sectors,
technology, and products. Pirttimäki (2007) depicts BI as a procedure that incorporates a series
of activities, being driven by the particular data needs of decision makers and the objective of
achieving competitive advantage. BI is a framework that transforms information into data and
afterward into learning, consequently enhancing company's basic decision-making process
(Singh and Samalia, 2014). BI is characterized as a framework which gathers, changes and
shows organized information -3- from various sources. BI is a system and an answer that helps
decision makers to comprehend the economic circumstance of the firm (Nofal et al., 2013). BI is
termed to as a set of numerical and methodological models for examination utilized for
extracting data and valuable information from raw information for utilizing confused basic
leadership prepare (Vercellis, 2013). Similarly, Wixom and Watson (2010, p.14) mention that
―Business intelligence (BI) is a broad category of technologies, applications, and processes for
gathering, storing, accessing, and analyzing data to help its users make better decisions.‖ We can
upgrade the bits of knowledge gave by BI applications—particularly by utilizing information
mining procedures, through simulation and modeling of real world under a "systems thinking"
approach, enhancing forecasts, and adding to a superior comprehension of the business
progression of any organization (Raisinghani, 2004). BI helps administrators by breaking down
information from various resources in better basic leadership at both tactical and strategic level,
for customary utilization, conventional data frameworks farewell, yet for hierarchical and
functional planning; new tools are required for business analysis (Rasoul and Mohammad, 2016).
2. Data, Information, and Knowledge
In BI context, we always see the word data, information, and knowledge which could lead us
getting confused on its use and implication. Carlo (2009) distinguishes their definition.
Information: It refers to the result of extraction and processing activities carried out on data, and
it appears meaningful for those who receive it in a specific domain. Knowledge: It is formed
from information which is used to make decisions and develop the corresponding actions. Hence,
we could say that knowledge consists of information that puts to work into a specific domain,
and it is enhanced by the experience and competence of decision makers in tackling and solving
complex problems. 3. Business Intelligence Architectures Carlo (2009) uses the following
pyramid to describe how business intelligence system is constructed.
Data sources: The sources mostly consist of data belonging to operationalize systems, but may
also include unstructured data, such as emails, and data received from external providers. -4-
Data warehouse/Data mart: Data warehouses are used to consolidate different kinds of data into a
central location using a process known as extract, transform and load (ETL) and standardize
these results across systems that are allowed to be queried. Data marts are generally small
warehouses that focus on information on a single department, instead of collecting data across a
company. They limit the complexity of databases and are cheaper to implement than full
warehouses. Data exploration: Data exploration is a passive BI analysis consisting of query and
reporting systems, as well as statistical method.
Data mining: Data mining is active BI methodologies with the purpose of information and
knowledge extraction from data.
Optimization: Optimization model allows us to determine the best solution out of a set of
alternative actions, which is usually fairly extensive and sometimes even infinite.
Decisions: 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.
4. Business Intelligence Capabilities One underlying theme that is evident through the research
is that BI used in an organization should be suited for decision making, which in turn contributes
to BI success (Clark, Jones & Armstrong, 2007). However, many scholars gained that this
success is yet to be realized by many organizations (Hostmann, Herschel, & Rayner, 2007). BI
capacities are basic capacities that help organizations enhance both its adjustment to change and
its execution (Watson & Wixom, 2007). Many researchers state that failure in adopting BI in an
organization because of an absence of fit -5- between organization’s BI and its characteristics
and objectives. An organization that has made progress with their BI usage have attempted to
guarantee that their BI is steady with their corporate business targets and much research on BI
achievement concentrates on the alignment amongst BI and business targets (McMurchy, 2008).
However, little is known about the part BI abilities play in accomplishing this objective. In-spite,
the fact that there is a collection of research tending to BI abilities, it has remained to a great
extent quiet on the part of BI capacities in accomplishing the important match amongst BI and
the decision environment in which it is implemented.
CHAPTER- 2
Research Methodology
RESEARCH OBJECTIVE
Research design
The Research available is descriptive so as to describe how business is usefull for the growth of
To do a research always we use sources of data collection. But according to the project I have
used both Primary and Secondary data.
Data source Primary (field survey)
Secondary(Internet,Catalogues,Broache
rs.)
Random sampling
Sampling Method
60 units
Sample Size
Data Analysis:
CHAPTER – 3