This document provides an overview of business analytics concepts. It defines business analytics as the skills, technologies, and practices used to explore past business performance and gain insights to drive planning. The document outlines a business analytics model with layers moving from strategic goals to technical data sources. It describes types of analytics including descriptive, predictive, and prescriptive. Finally, it discusses the importance of business analytics in decision making, understanding data, providing results, and managing data.

1. BUSINESS ANALYTICS
C. SURENDHRANATHA REDDY
DEPARTMENT OF MANAGEMENT

2. Introduction to Business Analytics
Name of the course : Business Analytics
Course code : BBA203B63
Number of credits : 3
Marks for the course : 75 marks
: 50 marks for ESE + 25 marks for CIA

3. Business Analytics: BBA203B63
Chapter No Chapter name
1 Introduction to Business Analytics
2 Types of Business Analytics
3 Digital Data and Data Warehouse
4 Risk Return Measurement

4. Unit 1
Introduction to
Business Analytics

5. Unit 1:
Business analytics: definition, evolution, nature,
scope;
Business analytics model
Link between strategy and business analytics
Moving ahead with analytics.

6. Business Analytics
What is Analytics?
Analytics is the process of discovering, interpreting, and
communicating significant patterns in data.
Applying analytics in context to business scenarios is called as
Business Analytics.

7. Business Analytics
Business analytics (BA) refers to the skills, technologies, and practices
for continuous iterative exploration and investigation of past business
performance to gain insight and drive business planning.
Business analytics focuses on developing new insights and
understanding of business performance based on data and statistical
methods.
Business analytics makes extensive use of analytical modeling and
numerical analysis, including explanatory and predictive modeling, and
fact-based management to drive decision making.

8. Steps to Business Analytics
Data
driven
decisions
Analysis
to generate
insights
Processing
Data
Collecting
Data

9. Business Analytics Scenario
A customer likes to have coffee at a popular coffee restaurant in city
He used to visit the restaurant quite often.
The customer used to order a particular type of coffee on regular basis with
same spending on each occasion
He used to sped approximately 30-40 minutes on every visit
He started receiving the notifications from coffee shop about offers on other
type of coffee and the eateries.
He is also offered some compliments on increased consumption of coffee.
The coffee shop has enabled the customer to use free internet at the shop

10. Business Analytics Scenario
This big coffee company used its loyalty card program to gather individualized
purchase data of millions of its customers.
The coffee company can predict the purchases and offer the customer likely to
be interested products based on the data generated over a period of time.
With this information, it was able to achieve its goal of identifying the pattern
in a customer’s purchase and then suggest to him/her offers through mobile
devices which the company believed the particular customer may take up.

11. History of Business Analytics
1865 - Staying ahead
Mr. Richard Miller Devens described in his book how Sir Henry
Furnese, a banker, was always one step ahead by actively gathering
information and acting on it before any of his competitors.
Late 1800s - Introduction of Scientific Management
Frederick Taylor introduced the first-ever system of business
analytics in the USA. The approach was called scientific
management. The purpose of the system was to analyze the
production techniques and laborer's body movements to identify
greater efficiencies.

12. History of Business Analytics
Early 1900s - Transformation of the Manufacturing Industry
Henry Ford used scientific management in order to measure the
performance of assembly line in manufacturing Ford Model T.
1950s - First hard drive disk by IBM
Computers had a massive demand during World War II. Until then
punch cards or tapes were used to store information. In 1956, the
tech giant, IBM invented the first hard disk drive which allowed
users to save a vast amount of data with better flexibility.

13. History of Business Analytics
Late 1980s - Emergence of Business Intelligence
Business intelligence solutions emerged. However, there was a
considerable amount of data available but not a centralized place to
store it. Ralph Kimball and Bill Inmon proposed strategies to build
data warehouses (DW).
Early 2000's - Relational Databases
Companies like SAP, Microsoft, SAS and IBM introduced various
solutions and software with relational databases.

14. History of Business Analytics
2005-2020: Bread and Butter for Companies
Technologies like cloud computing and Artificial Intelligence
have emerged to cater to the needs of industry.
Now - Core competence
With the internet available to almost everyone and the
increasing data, and emergence of cloud computing many
business have established their competence in business
analytics.

15. Workplace Analytics for Collaborations
Based on a survey it is found that
•People located closer in a building are more likely to collaborate
•A distance of 100 feet may be no better than several miles
•Even at short distances, 3 feet vs. 20 feet, there is an effect of
decreased collaboration with increased distance

16. Microsoft
At technology giant Microsoft, collaboration is key to a productive,
innovative work environment.
Following a 2015 move of its engineering group's offices, the
company sought to understand how fostering face-to-face
interactions among staff could boost employee performance and save
money.
Microsoft’s Workplace Analytics team hypothesized that moving
the 1,200-person group from five buildings to four could improve
collaboration by cutting down on the number of employees per
building and reducing the distance that staff needed to travel for
meetings.

17. Microsoft
This assumption was partially based on an earlier study by
Microsoft, which found that people are more likely to collaborate
when they’re more closely located to one another.
In an article for the Harvard Business Review, the company’s
analytics team shared the outcomes they observed as a result of the
relocation.
Through looking at metadata attached to employee calendars, the
team found that the move resulted in a 46 percent decrease in
meeting travel time. This translated into a combined 100 hours saved
per week across all relocated staff members and an estimated savings
of $520,000 per year in employee time.

18. Microsoft
The results also showed that teams were meeting more often due to
being in closer proximity, with the average number of weekly
meetings per person increasing from 14 to 18. In addition, the
average duration of meetings slightly declined, from 0.85 hours to
0.77 hours. These findings signaled that the relocation both improved
collaboration among employees and increased operational efficiency.
For Microsoft, the insights gleaned from this analysis underscored
the importance of in-person interactions and helped the company
understand how thoughtful planning of employee workspaces could
lead to significant time and cost savings.

19. Microsoft

20. Scope of Business Analytics
Client Relationship Management
Banking
Inventory management
Market analysis
Retail
Pharma
Online Marketing

21. Nature of Business Analytics

22. Business Analytics Model
Business analysis model outlines the steps a business takes to
complete a specific process, such as ordering a product or
onboarding a new hire.

24. Business Analytics Model
Arrows show the underlying layers that are subject to layers
above. Information requirements move from the business-
driven environment down to the technically oriented
environment.
The subsequent information flow moves upward from the
technically oriented environment toward the business-driven
environment

25. Business Analytics Model
Top Layer: In the top layer of the model, in the business-driven
environment, the management specifies a strategy that includes which
overall information elements must be in place to support this strategy.
Second Layer: In the second layer, the operational decision makers’ need
for information and knowledge is determined in a way that supports the
company’s chosen strategy.
Middle Layer: In the middle layer of the model, analysts, controllers,
and report developers create the information and knowledge to be used
by the company’s operational decision makers with the purpose of
innovating and optimizing their day-to-day activities.

26. Business Analytics Model
Second from the bottom: In the second layer from the bottom, in the
technically oriented environment in the data warehouse, the database
specialist or the ETL (extract, transform, load) developer merges and
enriches data and makes it accessible to the business user.
Bottom Layer: In the bottom layer, in the technically oriented
environment, the business’s primary data generating source systems are
run and developed by IT professionals from IT operations and
development.

27. Types of Business Analytics
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics

28. Descriptive Analytics
Descriptive analytics: Interpretation of historical data and KPIs to
identify trends and patterns. This allows for a big picture look of what
happened in the past and what is happening currently using data
aggregation and data mining techniques.
90% of organizations today use descriptive analytics which is the most
basic form of analytics. The simplest way to define descriptive analytics
is that it answers the question “What has happened?”.
The best example to explain descriptive analytics is the results, that a
business gets from the web server through Google Analytics. The
outcomes help understand to know the past if a promotional campaign
was successful or not based on basic parameters like page views.

29. Diagnostic Analytics
Diagnostic analytics: Focuses on past performance to determine which
elements influence specific trends. This is done using drill-down, data
discovering, data mining, and correlation to reveal the cause of specific
events.
Analytics performed on the internal data to understand the “why” behind
what happened is referred to as diagnostic analytics. This kind of analytics is
used by businesses to get an in-depth insight into a given problem provided
they have enough data at their disposal.
For example, eCommerce giants like Amazon can drill the sales and gross
profit down to various product categories like Amazon Echo to find out why
they missed on their overall profit margins.

30. Predictive Analytics
Predictive analytics: Uses statistics to forecast and assess future outcomes
using statistical models and machine learning techniques. This often takes
the results of descriptive analytics to create models that determine the
likelihood of specific outcomes.
Predictive analytics is used by businesses to study the data and ogle into the
crystal ball to find answers to the question “What could happen in the future
based on previous trends and patterns?”
Organizations like Walmart, Amazon, and other retailers leverage predictive
analytics to identify trends in sales based on purchase patterns of customers,
forecasting customer behavior, forecasting inventory levels

31. Prescriptive Analytics
Prescriptive analytics: Uses past performance data to recommend how to
handle similar situations in the future. Not only does this type of business
analytics determine outcomes, but it can also recommend the specific
actions that need to occur to have the best possible result.
Prescriptive analytics is the next step of predictive analytics that adds the
spice of manipulating the future. Prescriptive analytics advises on possible
outcomes and results in actions that are likely to maximize key business
metrics.
Aurora Health Care system saved $6 million annually by using prescriptive
analytics to reduce re-admission rates by 10%.

32. Diet, Attendance and
Academic Performance
Region North South East West
No. of
Students
34 31 40 44
Diet High
Calories
Less
Calories
Moderate
Calories
Fat
Attendance 65% 78% 71% 79%
Academic
Performance
64.5% 72.46% 68.25% 74.23%

33. Strategy and Business Analytics
A strategy is a description of the overall way in which a business
currently is, and is to be, run. It typically covers a year at a time.
 As a rule of thumb, a strategy attempts to handle company issues
in the short run while at the same time trying to create competitive
advantages in the long run.
To be concrete, strategy is developed by defining a number of
specific and measurable targets to be achieved by individual parts
of the organization.

35. Strategy and Business Analytics
 Scenario 1: It is that there is no formal link between strategy and
BA. Companies that are separated in their strategy, without data or with
limited data distributed over a large number of source systems, are typically
unable to make a link between corporate strategy and BA.
 In these companies, data is not used for decision making at a strategic level.
Instead, data is used in connection with ad hoc retrieval to answer concrete
questions.
Many companies have realized that they do not have the data, the staff, or
the technology to perform the task. From a strategic perspective, it is evident
that a maturing process could be initiated. Alternatively, the company just
continues with a business strategy that is not based on information.

36. Example
Small and medium firms do not rely much on system data as they can
take decisions quickly without the help of dat.

37. Strategy and Business Analytics
 Scenario 2: It is that BA supports strategy at afunctional
level. If companies, in connection with the implementation of a
strategy, request that the BA function perform monitoring of
individual functions' achievement of targets, we have
coordination between strategy and BA.
 However, if there is no flow back from BA to the strategic level,
then the BA function is reactive in relation to the strategy
function. In this case, the role of BA is merely to produce reports
supporting the performance of individual departments.

38. Example
It’s no secret airlines use data to track customers’ luggage,
personalize customer offers, boost customer loyalty and optimize
their operations.
At Southwest Airlines, executives are using customer data to
determine what new services will be most popular with customers
and the most profitable.

39. Strategy and Business Analytics
Scenario 3: It is a dialogue between the strategy and the BA
functions. If part in the learning loop, we'll get a BA function that
proactively supports the strategy function.
A learning loop is facilitated when the BA function is reporting
on business targets and is providing analyses of as well as
identifying differences between targets and actuals, with the
objective of improving both future strategies and the individual
departments' performance.

40. Example
Google created the People Analytics Department to help the company
make HR decisions using data, including deciding if managers make
a difference in their teams’ performance.
The department used performance reviews and employee surveys to
answer this question. Initially, it appeared managers were perceived
as having a positive impact. However, a closer look at the data
revealed teams with better managers performed best, are happier and
work at Google longer.

41. Strategy and Business Analytics
 Scenario 4: It is the information as a strategic resource. The
characteristic of the fourth scenario is that information is treated
as a strategic resource that can be used to determine strategy.
 Companies that fit this scenario will systematically, while
analyzing the opportunities and threats of the market, consider
how information, in combination with their strategies, can give
them a competitive advantage.

42. Example
Amazon bases its recommendations on what customers have bought
in the past, the items in their virtual shopping cart, what items the
customer has ranked or reviewed after purchase and what products
the customer has viewed when visiting the site.
Amazon also uses key engagement metrics such as click-through
rates, open rates and opt-out rates to further decide what
recommendations to push to which customers.

43. Elements of Business Analytics
Data mining: Data mining is the strategy of sifting through massive
datasets to uncover patterns, trends, and other truths about data that
aren’t initially visible using machine learning, statistics, and database
systems.
Text mining: Text mining is the process of extracting high-quality
information from the text on apps and throughout the World Wide Web.
Data aggregation: The process of data aggregation consists of
gathering and collecting the data, which is then presented in a
summarized format. Essentially, before it can be analyzed, it needs to be
collected, centralized, cleaned, and then filtered to remove any
inaccuracies or redundancies.

44. Elements of Business Analytics
Forecasting: When business analytics are used to analyze
processes that occurred during a specific period or season,
businesses are provided with a forecast of future events or
behaviors, thanks to historical data.
Data visualization: For all you visual learners out there, data
visualization is an absolute must-have part of business
analytics. It seamlessly takes the information and insights
drawn from your data and presents it in an interactive graph or
chart.

45. Importance of Business Analytics
 Helps in decision making: Business analytics is a methodology or tool to
make a sound commercial decision. Hence it impacts functioning of the
whole organization. Therefore, business analytics can help improve
profitability of the business, increase market share and revenue and
provide better return to a shareholder.
 Helps in understanding data: Facilitates better understanding of
available primary and secondary data, which again affect operational
efficiency of several departments.
 Provides results: Provides a competitive advantage to companies. In this
digital age flow of information is almost equal to all the players. It is how
this information is utilized makes the company competitive.
 Managing data: Converts available data into valuable information. This
information can be presented in any required format, comfortable to the
decision maker.

46. END OF UNIT 1