What is meant by Analytics Introductions to Business Analytics Difference between Business Analysis and Business Analytics

1. Analytics
• Analytics can range from a simple exploration into how many sales of a particular
product were made last year to a complex neural network model predicting which
customers to target for next year’s marketing campaign.
• In ‘Competing on analytics’, Thomas Davenport defines analytics as “the extensive use of
data, statistical and quantitative analysis, exploratory, predictive models, and fact-based
management to drive decisions and actions.”
• In layman’s terms it can be defined as “the analysis of data to draw hidden insights to
aid decision making”.
• There is a little bit of analyst in everyone. Analytics is an integral part of most
businesses.
• You do not need to be an “analyst” to do analysis. Analytics is an essential skill for
running any kind of business successfully.
• Common applications of analytics include the study of business data using statistical
analysis in order to discover and understand historical patterns with an eye to
predicting and improving business performance in the future.

2. Data: Basic Definition
• Data: Data is a set of values of qualitative or quantitative variables. It
is information in raw or unorganized form. It may be a fact, figure,
characters, symbols etc.
• Information: Meaningful or organized data is information.
• Analytics: Analytics is the discovery , interpretation, and
communication of meaningful patterns or summery in data.
• Data Analytics (DA) is the process of examining data sets in order to
draw conclusion about the information it contains.
• Analytics is not a tool or technology, rather it is the way of thinking
and acting on data.

5. Types of analytics
1. Descriptive Analytics (“What has happened?”) (Data aggregation,
summary, data mining)
2. Predictive Analytics (“What might happen?”) (Regression, LSE,MLE)
3. Prescriptive Analytics (“What should we do?”) (Optimization,
Recommendation)

6. Variable Types
• Variable
• Numerical
• Continuous (ex: height, weight, profit.)
• Discrete ( # items, population, count of students etc)
• categorical
• Nominal categorical ( location, caste, gender )
• Ordinal categorical ( Grade, hotness, coldness etc.)

7. Role of Data Analytics:
• Gather Hidden Insights – Hidden insights from data are gathered and then
analyzed with respect to business requirements.
• Generate Reports – Reports are generated from the data and are passed
on to the respective teams and individuals to deal with further actions for a
high rise in business.
• Perform Market Analysis – Market Analysis can be performed to
understand the strengths and weaknesses of competitors.
• Improve Business Requirement – Analysis of Data allows
improving Business to customer requirements and experience.

9. Analytics Life Cycle
• 1. Problem Identification
• 2. Hypothesis formulation
• 3. Data Collection
• 4. Data Exploration/preparation
• 5. Model Building
• 6. Model Validation and Evaluation

10. Analytics Life Cycle(Cont..)
• 1. Problem Identification
• The problem is a situation which is judged to be corrected or solved
• Problem can be identified through
• 1. Comparative/benchmarking studies
• 2. Performance Reporting
• 3. Asking some basic questions
a) Who are affected by the problem?
b) What will happen if problem is not solved?
c) When and where does the problem occur?
d) Why is the problem occurring
e) How are the people currently handling the problem?

11. Analytics Life Cycle(Cont..)
2. Hypothesis formulation
1. Frame the questions which need to be answered.
2. Develop a comprehensive list of all possible issues related to the problem.
3. Reduce the list by eliminating duplicates and combining overlapping issues.
4. Using consensus building get down to a major issue list.
3. Data Collection
Data collection techniques are
1. Using data that is already collected by others
2. Systematically selecting and watching characteristics of people, objects, and events.
3. Oral questioning respondents either individually or as a group
4. Collecting data based on answers provided by the respondents in written format.

12. Analytics Life Cycle(Cont..)
4. Data Exploration
1. Importing data
2. Variable Identification
3. Data Cleaning
4. Summarizing data
5. Selecting subset of data
5. Model Building
• Building a Model is a very iterative process because there is no such thing
as final and perfect solution.
• Many of the machine learning and statistical techniques are available in
traditional technology platform

13. Analytics Life Cycle(Cont..)
• 6. Model validation and Evaluation
• Like model building the process of validating model is also a iterative process.
There are so many ways …
• Confusion Matrix.
• Confidence Interval.
• ROC curve
• Chi Square.
• Root Mean Square Error
• Gain and Lift Chart.

14. Data Analytics
• Data Analytics refers to the techniques used to analyze data to
enhance productivity and business gain.
• Data is extracted from various sources and is cleaned and categorized
to analyze various behavioral patterns.
• The techniques and the tools used vary according to the organization
or individual.

15. Role of Data Analytics
• Gather Hidden Insights – Hidden insights from data are gathered and
then analyzed with respect to business requirements.
• Generate Reports – Reports are generated from the data and are
passed on to the respective teams and individuals to deal with further
actions for a high rise in business.
• Perform Market Analysis – Market Analysis can be performed to
understand the strengths and weaknesses of competitors.
• Improve Business Requirement – Analysis of Data allows
improving Business to customer requirements and experience.
•

16. Data analyst vs. data scientist
• While data analysts and data scientists may be commingled on analytics
teams, their roles differ considerably.
• Data analysts seek to describe the current state of reality for their
organizations by translating data into information accessible to the
business.
• They collect, analyze, and report on data to meet business needs. The role
includes identifying new sources of data and methods to improve data
collection, analysis, and reporting.
• Data scientists, on the other hand, are often engaged in long-term
research and prediction, while data analysts seek to support business
leaders in making tactical decisions through reporting and ad hoc queries.

17. Definition of BA

18. Evolution of Business Analytics
• However, the modern evolution of analytics began with the
introduction of computers in the late 1940s and their development
through the 1960s and beyond
• Early computers provided the ability to store and analyze data in ways
that were either very difficult or impossible to do so manually.
• This facilitated the collection, management, analysis, and reporting of
data, which is often called business intelligence (BI), a term that was
coined in 1958 by an IBM researcher, Hans Peter Luhn.

19. Business intelligence software can answer
• “How many units did we sell last month?”
• “What products did customers buy and how much did they spend?”
“How many credit card transactions were completed yesterday?”

20. Conti….
• Statistics has a long and rich history, yet only rather recently has it been
recognized as an important element of business, driven to a large extent by
the massive growth of data in today’s world.
• Statistical methods allow us to gain a richer understanding of data that
goes beyond business intelligence reporting by not only summarizing data
succinctly but also finding unknown and interesting relationships among
the data.
• Statistical methods include the basic tools of description, exploration,
estimation, and inference, as well as more advanced techniques like
regression, forecasting, and data mining.

21. Conti
• Much of modern business analytics stems from the analysis and
solution of complex decision problems using mathematical or
computer-based models—a discipline
• known as operations research, or management science. Operations
research (OR) was born from efforts to improve military operations
prior to and during World War II.
• After the war, scientists recognized that the mathematical tools and
techniques developed for military applications could be applied
successfully to problems in business and industry.

22. Conti….
• A significant amount of research was carried on in public and private
think tanks during the late 1940s and through the 1950s
• As the focus on business applications expanded, the term
management science (MS) became more prevalent. Many people use
the terms operations research and management science
interchangeably, and the field became known as Operations
Research/Management Science (OR/MS).

23. Conti….
• Many OR/MS applications use modeling and optimization—
techniques for translating real problems into mathematics,
spreadsheets, or other computer languages, and using them to find
the best (“optimal”) solutions and decisions.
• INFORMS, the Institute for Operations Research and the
Management Sciences, is the leading professional society devoted to
OR/MS and analytics, and publishes a bimonthly magazine called
Analytics

24. Conti…
• Decision support systems (DSS) began to evolve in the 1960s by
combining busines intelligence concepts with OR/MS models to
create analytical-based computer systems to support decision
making. DSSs include three components:
1. Data management. The data management component includes databases
for
storing data and allows the user to input, retrieve, update, and manipulate data.
2. Model management. The model management component consists of various
statistical tools and management science models and allows the user to easily
build, manipulate, analyze, and solve models.
3. Communication system. The communication system component provides the
interface necessary for the user to interact with the data and model
management
components

25. DSSs have been used for many applications,
including
• Pension fund management,
• Portfolio management,
• Work-shift scheduling,
• Global manufacturing and
• Facility location,
• Advertising-budget allocation,
• Media planning,
• Distribution planning,
• Airline operations planning,
• Inventory control, library
management,
• Classroom assignment,
• Nurse scheduling,
• Blood distribution,
• Water pollution control,
• Energy planning

27. Conti…
• Modern business analytics can be viewed as an integration of BI/IS,
statistics,and modeling and optimization
• data mining is focused on better understanding characteristics and
patterns among variables in large databases using a variety of statistical
and analytical tools
• Simulation and risk analysis relies on spreadsheet models and statistical
analysis to examine the impacts of uncertainty in the estimates and their
potential interaction with one another on the output variable of interest.

28. • what-if analysis—how specific combinations of inputs that reflect key
assumptions will affect model outputs.
• What-if analysis is also used to assess the sensitivity of optimization
models to changes in data inputs and provide better insight for
making good decisions.
• Visualizing data and results of analyses provide a way of easily
communicating data at all levels of a business and can reveal
surprising patterns and relationships

29. • The power of analytics in a personal computing environment was
noted some 20 years ago
• By business consultants Michael Hammer and James Champy, who
said, “When accessible data is combined with easy-to-use analysis
and modeling tools, frontline workers
• Although many good analytics software packages are available to
professionals, we use Microsoft Excel and a powerful add-in called
Analytic Solver Platform

30. Business Analytics Is Important ?
1. Enhance Customer Experience
2. Make Informed Decisions
3. Reduce Employee Turnover
4. Improve Efficiency
5. Identify Frauds

31. Business Analytics Is Important ?
6. Cut Manufacturing Costs
7. Make The Most Of Your Investment
8. Improved Advertising
9. Better Product Management
10. Tackle Problems
11. Accelerate Through Uncertainty
12. Conduct A Competitor Analysis

32. Business analytic process