Lesions and topics covered in module.

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
- Change in business environment
- Bombardment of Information
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
the process of studying data in order to draw conclusions about the
information it contains
BI involves the strategic decision-making based on that data.

4. 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.

5. 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.

6. BI powered by Computers
Electronic computers were not developed till 1930s but were
quicklydeveloped 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.

7. 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.

8. 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.

9. 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.”

10. Asking different questions was still very expensive. Hence some new
tools and “building blocks” were developed to speed the process of
different queries:
• 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.
To this day, both ETL and OLAP tools are still a crucial part of business
intelligence solutions.

11. Business Intelligence segments/components
Decisions
Optimization - Choosing the best alternative
Data mining - models for learning from data
Data exploration - Statistical analysis and visualization
Data warehouse/Data mart - Multidimensional cube analysis
Data sources - operational data, documents and external data

12. Decisions
Optimization
Data Mining
Data Exploration
Data warehouse/Data mart
Data source

13. Date 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. The sources consist of data belonging to operational
systems, but may also include unstructured documents, such as emails and data
received from external providers. 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
datamarts

14. Data exploration - At the third level of the pyramid 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 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. For instance, consider the sales manager of a
company who notices that revenues in a given geographic area have
dropped for a specific group of customers. Hence, he might want to set
his hypothesis by using extraction and visualization tools, and then
apply a statistical test to verify that his conclusions are adequately
supported by data.

15. 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. Unlike the
tools described at the previous level of the pyramid, 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 - optimization models allows to determine the best
solution out of a set of alternative actions, which is usually fairly
extensive and sometimes even infinite.

16. 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 level, 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 level.
Finally, the activities of decision makers responsible for the application domain
appear dominant at the top level.

17. Information is knowledge communicated about a particular fact or
circumstance.
Intelligence is all about finding out information, determining what it
means – and then using it to take action.
Information is everywhere – whether it’s daily news, online blogs or
conversations between friends – Intelligence refers to the information
not freely available, or within the public domain.
Intelligence – is typically privileged information intended for a
particular audience. The art of intelligence to collect this privileged
and/or protected information, and use it to our benefit.

18. Business intelligence is a set of concepts, methods and processes to
improve business decisions using information from multiple sources
and applying experience and assumptions to develop an accurate
understanding of business dynamics. It is the gathering, management
and analysis of data to produce information that is distributed to
people throughout the organization to improve strategic and tactical
decisions.
The value chain begins with the data resource. Information is
developed from the data resource to support the knowledge
environment of an intelligent learning organization. Data is the raw
material for information which is the raw material for the knowledge
environment. Knowledge is the raw material for business intelligence
that supports business strategies.

20. Data is the individual raw facts that are out of context, have no
meaning and are difficult to understand.
Information is data filled with meaning, relevance and purpose. A set
of data in context is a message that only becomes information when
one or more people are ready to accept that message as relevant to
their needs.
Knowledge environment promotes the exchange of information to
create knowledge. Management of an environment where people
generate tacit knowledge,render it into explicit knowledge and feed it
back to the organization.
(Tacit knowledge is all the knowledge that is in people's heads or the
heads of a community of people, such as an organization)

21. The knowledge environment and business intelligence, collectively, are
the human resource that uses information to support the business
strategies. It is the human resource that possesses the business
intelligence, the intelligence and the wisdom to support business
strategies. Information is the link between the data resource and the
human resource.
Eg for data: TOTSAL 475 crores
Eg for information: “The total sales for the Bangalore Branch in July
2017 amounted to 475 crores”

23. Business Intelligence system
Business intelligence (BI) combines business analytics, data mining,
data visualization, data tools and infrastructure, and best practices to
help organizations to make more data-driven decisions.
Factors of Business Intelligence system
* Technologies
* Analytics
* Human Resources

24. • Technologies - Hardware and software technologies with network
connectivity have facilitated the development of Business Intelligence
systems. Use of advanced algorithms, state-of-the-art graphical
visualization techniques, featuring real time animations and keeping
the processing time within the reasonable range.
• Analytics - Use of mathematical models and analytical methodologies
play a key role in information enhancement and knowledge extraction
from the data available inside the organization.

25. • Human resources - The human assets of an organization are built up
by the competencies whether as individuals or collectively. The
overall knowledge possessed and shared by these individuals
constitutes the organizational culture. The ability of knowledge
workers to acquire information and then translate it into practical
actions is one of the major assets of any organization, and has a
major impact on the quality of the decision-making process. All the
available analytical tools being equal, a company employing human
resources endowed with a greater mental agility and willing to accept
changes in the decision-making style will be at an advantage over its
competitors.

26. • Analysis - The needs of the organization relative to the development of a
business intelligence system should be carefully identified. It is necessary to
clearly describe the general objectives and priorities of the project, as well as
to set out the costs and benefits deriving from the development of the
business intelligence system.
• Design is aimed at deriving a provisional plan of the overall architecture,
taking into account any development in the near future and the evolution of
the system in the mid term. The project plan will be laid down, identifying
development phases, priorities, expected execution times and costs, together
with the required roles and resources.
• Planning stage includes assessment of the existing data and other external
data to be retrieved from central data warehouse. It includes mathematical
models to be adopted, verifying the efficiency of the algorithms to be utilised
at low cost and minimum capabilities etc

27. • Implementation and control The last phase consists of five main sub-
phases. First, the data warehouse and each specific data mart are
developed. These represent the information infrastructures that will
feed the business intelligence system. In order to explain the meaning
of the data contained in the data warehouse and the transformations
applied in advance to the primary data, a metadata archive should be
created. ETL procedures are set out to extract and transform the data
existing in the primary sources, loading them into the data warehouse
and the data marts. The next step is aimed at developing the core
business intelligence applications that allow the planned analyses to
be carried out. Finally, the system is released for test and usage.

28. Difference between data warehouse and data mart
A data mart is similar to a data warehouse, but it holds data only for a specific
department or line of business, such as sales, finance, or human resources.

29. Real-time business intelligence (RTBI) is a concept describing the
process of delivering business intelligence (BI) or information about
business operations as they occur. Real time means near to zero latency
and access to information whenever it is required.
The speed of today's processing systems has allowed typical data
warehousing to work in real-time. The result is real-time business
intelligence. Business transactions as they occur are fed to a real-time
BI system that maintains the current state of the enterprise. The RTBI
system not only supports the classic strategic functions of data
warehousing for deriving information and knowledge from past
enterprise activity, but it also provides real-time tactical support to
drive enterprise actions that react immediately to events as they occur.

30. "Real-time" means a range from milliseconds to a few seconds (5s)
after the business event has occurred. While traditional BI presents
historical data for manual analysis, RTBI compares current business
events with historical patterns to detect problems or opportunities
automatically. This automated analysis capability enables corrective
actions to be initiated and/or business rules to be adjusted to optimize
business processes.
Latency refers to -
• Data latency; the time taken to collect and store the data
• Analysis latency; the time taken to analyze the data and turn it into
actionable information
• Action latency; the time taken to react to the information and take
action
Real-time business intelligence technologies are designed to reduce all
three latencies to as close to zero as possible

31. SKF, Sweden manufacuring bearings, mechatronics etc, presence in 130
countries and nearly 17000 distributor locations.
• SKF needs to constantly forecast market size and product demand to
adjust their manufacturing
• Traditonally SKF maintained Excel files for forecasting. The cost and
time for maintaining reconciling data was too difficult.
• It took days to produce a simple demand forecast.

32. Introduction of RTBI in SKF
By centralizing their data assets into a single system, SFK was quickly
able to start sharing their data and analyses between a number of
different departments within the organization — including sales,
manufacturing planning, application engineering, business
development, and management. As they produce a large number of
product variants, they are now able to quickly
, no longer needing to debate data integrity between
departments. This allows management to
.

34. Benefits of Business Intelligence
• Fast and accurate reporting: Employees can use templates or customized
reports to monitor KPIs using a variety of data sources, including financial,
operations, and sales data. These reports are generated in real time and use
the most relevant data so businesses can act quickly. Most reports include
easy to read visualizations, such as graphs, tables, and charts. Some BI
software reports are interactive so that users can play with different variables
or access information even faster.
• Valuable business insights: Businesses can gauge employee productivity,
revenue, overall success as well as department-specific performances. It can
uncover strengths and weaknesses since BI tools help organizations
understand what’s working and what isn’t. Setting up alerts is easy and can
help track these metrics and help busy executives stay on top of the KPIs that
matter the most to their business.

35. • Competitive analysis: The ability to manage and manipulate a large amount
of data is a competitive edge in itself. Furthermore, budgeting, planning, and
forecasting is an incredibly powerful way to stay ahead of the competition,
goes way beyond standard analysis, and is also easy to perform with BI
software. Businesses can also track their competitor’s sales and marketing
performance and learn how to differentiate products and services.
• Better data quality: Data is rarely squeaky clean and there are many ways that
discrepancies and inaccuracies can show up – especially with a hacked
together “database”. Businesses that take care of collecting, updating and
creating quality data are typically more successful. With BI software,
companies can aggregate different data sources for a fuller picture of what is
happening with their business.

36. • Increased customer satisfaction: BI software can help companies understand customer
behaviors and patterns. Most companies are taking customer feedback in real time and
this information can help businesses retain customers and reach new ones. These tools
may also help companies identify buying patterns, which help customer experience,
employees anticipate needs and deliver better service.
• Identifying market trends: Identifying new opportunities and building out a strategy with
supportive data can give businesses a competitive edge, directly impact long-term
profitability, and gives the full scope of what is happening. Employees can leverage
external market data with internal data to detect new sales trends by analyzing customer
data and market conditions, as well as spotting business problems.
• Increased operational efficiency: BI tools unify multiple data sources, which help with a
business’s overall organization so that managers and employees spend less time tracking
down information and can focus on producing accurate and timely reports. Armed with
up to date and accurate information, employees can focus on their short and long term
goals and analyze the impact of their decisions.

37. • Improved, accurate decisions: Competitors move quickly and it’s important for companies to
make decisions as quickly as possible. Failure to issues with accuracy and speed could lead to lost
customers and revenue. Organizations can leverage existing data to deliver information to the
right stakeholders at the right time, optimizing time-to-decision.
• Increased revenue: Increasing revenue is an important goal for any business. Data from BI tools
can help businesses ask better questions about why things happened through making
comparisons across different dimensions and identifying sales weaknesses. When organizations
are listening to their customers, watching their competitors, and improving their operations,
revenue are more likely to increase.
• Lower margins: Profit margins are another concern for most businesses. Fortunately, BI tools can
analyze inefficiencies and help expand margins. Aggregated sales data help companies to
understand their customers and empowers sales teams to develop better strategies about where
budgets should be spent.

38. The key general categories of business intelligence applications are:
• Spreadsheets.
• Reporting and querying software: applications that extract, sort,
summarize, and present selected data.
• Online analytical processing (OLAP)
• Digital dashboards.
• Data mining.
• Business activity monitoring.
• Data warehouse.

39. 1. Spreadsheets - Excel features for BI like sort, filter, pivot table, pivot
chart etc (analyse data is an extensive way, arrange or rearrange data in
order to get useful information.
2. Reporting and querying software: applications that extract, sort,
summarize, and present selected data.
3. Online analytical processing (OLAP) - is the technology behind
many Business Intelligence (BI) applications. OLAP is a powerful
technology for data discovery, including capabilities for limitless report
viewing, complex analytical calculations, and predictive “what if”
scenario (budget, forecast) planning.
4. Digital dashboards -A Digital Dashboard is an electronic interface
that aggregates and visualizes data from multiple sources, such as
databases, locally hosted files, and web services. Dashboards allow you
to monitor your business performance by displaying historical trends,
actionable data, and real-time information.

41. 5. Data mining - is a process used
by companies to turn raw data into
useful information. By using
software to look for patterns in
large batches of data, businesses
can learn more about their
customers to develop more
effective marketing strategies,
increase sales and decrease costs.

42. 6. Business activity monitoring - BAM also called business activity
management, is the use of technology to proactively define and
analyze critical opportunities and risks in an enterprise to maximize
profitability and optimize efficiency. The BAM paradigm can be used to
evaluate external as well as internal factors.

43. Business activity monitoring

44. 7. Data warehouse - a data warehouse, also known as an enterprise
data warehouse, is a system used for reporting and data analysis, and is
considered a core component of business intelligence. DWs are central
repositories of integrated data from one or more disparate sources.

45. Data warehouse

46. Role of BI in modern business
BI is a software solution that consists of a set of tools intended for
extracting the essential information from big data. BI includes
interactive operational reports, dashboards, geo-mapping, data
analysis management tools of different kinds. One of the main fields of
application of BI technologies is the effective measurement and
analysis of KPIs and metrics across all levels of a business organization.
BI dashboards display multiple charts and reports on a single page.
Such software solutions help to review and analyze the most important
metrics in-a-click. BI software solutions can provide both summary and
in-depth views of performance.

47. Intuitive dashboard as the best way of data
visualization for business

48. Besides reviewing the real-time data, it generates advanced reports.
Reporting tools allow using different types of queries helping to answer
specific business questions. Custom-made BI solutions allow creating
reports with the use of built-in templates which helps to save time.
Also, nowadays, BI software is usually cloud-based. This feature enables
users of your organization to access reporting tools from any device
which results in higher mobility.

49. Fully integrated reporting module

50. This business intelligence system implements technologies that allow
providing users with interactive visualizations which helps to simplify
the analysis and allows comparing performance indicators across the
local market. It helps business organizations to detect patterns by
visually navigating data or applying guided advanced analytics.

52. The Main Benefits of Using BI Solutions in Business
• In a promptly changing world, a reliable risk management solution is the
must. The use of a BI solution designed according to the features of business
can significantly mitigate the risks by quickly and efficiently detecting market
trends and informing about the change in customer behavior patterns. Also, BI
solutions can help to ensure compliance with statutory and regulatory
requirements which will be helpful for the companies that have many
branches across different countries.
• Modern BI solutions created with the use of cutting-edge technologies can
improve the effectiveness of business processes which will result in increased
incomes for company. Company can reduce ongoing costs and optimize the
use of the existing resources, thanks to detailed reports on the performance
of each departments. Analyzing the data gathered by the organization, we
can understand growth patterns and define where to invest.

53. • Improved customer satisfaction is another benefit of using modern BI systems. Real-
time understanding of clients’ preferences and expectations will help companies to
develop an efficient insight-based market strategy. BI solutions can help to determine
profitability across different regions and products. This data will enable companies to
find new opportunities and adapt marketing campaigns accordingly.
• The implementation of BI solutions in modern business can become helpful companies
of any size. The adoption of such systems will help companies to make decisions based
on the most relevant and accurate data. BI software allows business to:
• define a growth strategy
• increase revenue
• create a more effective business model
• get a consolidated perspective of clients
• gain a competitive advantage

54. ETL -
Extraction - Data are extracted from the available internal and external
sources.
Transformation -The goal of the cleaning and transformation phase is
to improve the quality of the data extracted from the different sources,
through the correction of inconsistencies, inaccuracies and missing
values.
Loading - Finally, after being extracted and transformed, data are
loaded into the tables of the data warehouse to make them available to
analysts and decision support applications.