Business intelligence (BI) encompasses processes and technologies that transform raw data into actionable business insights, essential for informed decision-making. The increasing competition and the evolving business environment drive the need for BI, which differs from data analytics by focusing primarily on historical data for strategic decisions. The history of BI traces back to its first usage in the 1860s, evolving through technological advancements like the invention of the hard disk and the emergence of data warehouse solutions to enhance data analysis capabilities.

1. BUSINESS
INTELLIGENCE
MODULE 1

2. Meaning
Business Intelligence is a set of processes, architectures, and
technologies that convert raw data into meaningful information that
drives profitable business actions.
Business Intelligence (BI) refers to technologies, applications and
practices for the collection, integration, analysis, and presentation of
business information.
The purpose of Business Intelligence is to support better business
decision making.
Essentially, Business Intelligence systems are data-driven Decision
Support Systems (DSS).

3. Why Business Intelligence
Increased competition
Increased competition is a major reason why businesses need to use business intelligence.
Change in business environment
The business environment is constantly changing, and businesses need to be able to adapt
to these changes in order to stay ahead of the competition.
Bombardment of Information
Businesses are bombarded with information from a variety of sources, and it can be
difficult to know what information is important and how to use it effectively.

4. Why Business Intelligence
The question now is what to do with this data and how to use it in the best possible
way.
Difference between Data Analytics and Business Intelligence
• BI is the process of studying data in order to draw conclusions about the information it
contains.
• BI- Focuses on historical data and present performance to inform future decisions.
• Data Analytics: Has a broader scope, encompassing past, present, and potentially future
data.
• BI is all about presenting data for informed decision-making, while data analytics is more
about uncovering hidden insights from data.
• In practice, businesses often use both BI and data analytics together to get a complete
picture.
BI involves the strategic decision-making based on that data.

5. History of Business
Intelligence
• The first use of the term “business intelligence” was by Mr.
Richard Miller Devens, an American author in his book
Cyclopaedia of Commercial and Business Anecdotes, first
published in 1865.
• He used it to describe how Sir Henry Furnese, a successful
banker, profited from information by actively gathering and
acting on it before his competition.
• This pointed out the fact that it was more reliable to use data
and empirical evidence, rather than gut instinct, to develop a
business strategy.
• The idea was further enhanced by others who saw value in
information.

6. History of Business Intelligence (cont'd)
During the last decade of the 1800s, that is 1890, Frederick Taylor introduced scientific management with time
studies that analyzed production techniques and laborers’ body movements to find greater efficiencies that
boosted industrial production.
Taylor became a consultant to Henry Ford, who in the early 1900s started measuring the time each component of
his Ford Model T took to complete on his assembly line. His work and his success revolutionized the manufacturing
industry worldwide.

7. BI powered by
Computers
• Electronic computers were not developed till 1930s but
were quickly developed during World War II, as part of
the effort by the allies to crack German codes.
• Up until the 1950s, computers relied mostly on
punchcards to store data. These were huge piles of
cards with tiny holes in them, which would store the
information to be processed by the computers. In 1956,
however, IBM invented the first hard disk drive, making
it possible to store large amounts of information with
greater flexibility of access.

8. BI powered by Computers (cont'd)
• In 1958, IBM researcher Hans Peter Luhn published a historical paper called A Business Intelligence
System. He wrote about the potential of a system for “selective dissemination” of documents to “action
points” based on “interest profiles.” His work has remarkable significance even to this day since he
predicted several business intelligence trends which are cutting-edge nowadays, as the ability for
information systems to learn and predict based on user interests. Today we call it machine learning. Luhn
is popularly recognized as the father of business intelligence.
• Even though the concept proposed by Luhn caught the attention of several interested parties, the idea was
considered too expensive at the time to have any practical use. More technological progress was needed to
make it an economically viable solution.

9. BI powered by Computers (cont'd)
In the next decade, computer use exploded. Each computer was a gigantic machine which occupied the entire
floor of a building and had to be managed by several high-skilled engineers to function properly. Experts again
tackled the idea of using computers to extract conclusions from the data, but the main problem was that there
was no centralized method available to bring together all the data in one place. Data, by itself, could not generate
any insights. To solve this challenge, the first database management systems were designed. Later, they would
simply be called databases. This new tool provided its value, being used to finally make conclusions from the
available data.

10. 1970 onwards.... Big
players enter.... IBM, SAP,
Siebel
• Lower prices for storage space and better databases allowed
for the next generation of business intelligence solutions.
• Data warehouses are databases designed to aggregate lots of
data from other sources of data (mostly other databases),
allowing a much deeper analysis with the ability to cross-
reference these different sources. It was still, however, too
technical and expensive. Reports needed to be run and
maintained by a host of expensive IT technical staff.
• In the 90s, data warehouse costs declined as more
competitors entered the market and more IT professionals
got acquainted with the technology. This was the period of
“Business Intelligence 1.0.”

11. ETL and OLAP tools
ETL (extract, transform, and load)
a set of tools, similar to a programming language,
that made it easier to design the flow of data
within a data warehouse.
OLAP (online analytical processing)
helped to create different visualization options for
the queried data, empowering the analysts to
extract better conclusions from the information at
hand.