Understand business analytics from a holistic perspective before creating models of your respective business.

1. Business Analytics
Creating frontiers for Business

2. Course
 Duration: 3 Days 8 hours per day
 Participants: 18-20
 Course Outline:
 Day 1 : Introduction to Business Analysis
 Day 2 : Learning the Tools for Business Analysis
 Day 3: Understanding the Process of Business Analysis

3. Day 1

4. What is Business Analysis?
 How to improve business
 Using Analytics for improvement
 Gaining insights from business
 Continuous improvement

6. Types of Business Analysis
 Business Analysis
 Process Analysis
 Systems Analysis
 IT Business Analysis
 Data Analysis

7. Business Analyst Project Manager
BA Vs PM
 Conducts Business
Analysis
 Discovers Problem And
Delivers Solutions
 Dynamic Scope Of Work
 Focus on Requirements
Management
 Manages Project
 Solutions To Problem
 Defined Scope Of Work
 Focus on Schedule, Cost
and Resource

8. Requirements of BA
Assisting with the business case
Planning and monitoring
Eliciting requirements
Requirements organization
Translating and simplifying requirements
Requirements management and communication
Requirements analysis

9. BA Techniques
 SCOT
 MOST
 SCRS
 PESTLE
 PORTERS 5 FORCE

10. SCOT
Strengths Challenges
Opportunities Threats

11. Individual Activity

12. MOST
Mission
Objective
Strategy
Tactic

13. Individual Activity

14. SCRS
Strategy
Current
Status
Requireme
nts
Solution

15. Individual Activity

16. PESTLE
 P – Political
 E – Economic
 S – Social
 T – Technology
 L – Legal
 E – Environment

17. Group Activity

18. Porter’s 5 Force
Bargaining Power
of Customers
 Buyer Concentration
 Buyer Volume
 Buyer Switching Costs
 Buyer Information
 Ability to integrate backward
 Substitute products
 Price/ Total Purchases
 Product Differences
 Brand Identity
 Impact of Quality/ Performance
 Buyer Profits
Threat Of New Entry
 Economies of Scale
 Proprietary Product Differences
 Brand Identity
 Switching Costs
 Capital Requirements
 Access to distribution
 Absolute Cost Advantages
 Government Policy
 Expected Retaliation
Bargaining Power
of Suppliers
 Differentiation of inputs
 Switching Costs
 Presence of substitute inputs
 Supplier Concentration
 Importance of Volume to supplier
 Cost Relative to Total Purchase
 Impact of inputs on cost or
difference
 Threat of forward integration
Threat of
Substitutes
 Differentiation of inputs
 Relative Price Performance of
Substitutes
 Switching Costs
 Buyer Propensity to substitute
Existing
Competitors
 Industry Growth
 Fixed Costs/ Value Add
 Over Capacity
 Product Differences
 Brand Identity
 Switching costs
 Concentration and Balance
 Informational Complexity
 Diversity of Competitors
 Corporate Stakes
 Exit Barriers

19. Data & Analytics
 What is data?
 How is data related to business?
 Real Life Analytics
 Understanding the data

20. Data
Information in raw or unorganized form (such as alphabets,
numbers, or symbols) that refer to, or represent, conditions,
ideas, or objects. Data is limitless and present everywhere in
the universe.
Reference:
http://www.businessdictionary.com/definition/data.html

21. Edward Deming quote
'In God we trust, everyone else bring data"

22. Data and business
 business needs valuable data and insights
 Understanding your target audience and customers
preferences
 Data Analytics is a combination of all the processes and tools
related to utilizing and managing large/Small data sets
 Integrating physical and digital shopping spheres
 Assessing Trends and measures to reduce deviations

23. Data and business
1. Improved Service Level Performance
2. Better Order Fulfillment
3. Improved Supplier Management
4. Maximise Customer Value
5. Driving Down Costs
6. Improved Advertising
7. Better Product Management

24. Data
data
‘deɪtə’
noun
the quantities, characters, or symbols on which
operations are performed by a computer, which may
be stored and transmitted in the form of electrical
signals and recorded on magnetic, optical, or
mechanical recording media.

25. Understanding data
 Unbiased Data
 Achieve a large sample
 Ask the right questions (Quantitative & Qualitative)
 Interpret the data correctly
 Consider Margin of Error
 Create a Data Checklist
 Use Statistical Analysis

26. Activity (25 Minutes)
Objective: To collect data to benefit the learning of Business
Analytics
Target Group: Attendees of the workshop
Team Size: 4-5 Members

27.  Video
The Blind Men and the Elephant

28. Data Modeling
 Types of Data Models
 Data Modeling
 Data Modeling Process
 Constructing Data Models

29. Types of Data models

30. Hierarchical data model
General
Manager
Manager –
Operations
Assistant
Manager
Executive
Officer
Manager –
Accounts
Assistant
Manager
Executive
Officer
Manager -
HR

31. Relational Model

32. Network model

33. object oriented model

34. Object-relational model

35. Data Modeling
 Data Modeling is a method of defining and analyzing data
requirements needed to support the business functions of an
enterprise.
 Data modeling is the act of exploring, understanding and
designing data-oriented structures. You identify entity types
their purpose and then relationships among them.
 Structuring the data to understand it’s value
 Pictorial and visual aides to help in the data framing

36. Exercise (1 hour)
 Create your data base on the models shared
 Object is to learn the patterns of the persons who are
attending this course
 What is the objective of collecting the data?
 What are the data points you will collect?
 What is the interpretation of the data collected?

37. Points to be noted
 Identify entities and classify them into their types
 Identify attributes of each entity
 Define and apply naming conventions
 Create standards for commonly used attributes
 Identifying relationships among entities
 Applying data model patterns
 Create and assigning key attributes
 Perform Normalization to maintain data integrity and reduce
redundancy
 De-normalizing to improve performance

38. Day 2

39. Analytics
 Descriptive analytics – Past Data
 Predictive analytics – Future data
 Prescriptive analytics – Past and Future data
 Diagnostic analytics – Root Cause Analysis

41. Predictive Analytics

42. Correlation
 The correlation is one of the most common and most useful
statistics. A correlation is a single number that describes the
degree of relationship between two variables. Let's work
through an example to show you how this statistic is
computed.
 Seek the height of each individual in your class and correlate
the data with a histrogram.

43. Regression
 Regression analysis is a statistical process for estimating
the relationships among variables. It includes many
techniques for modeling and analyzing several variables,
when the focus is on the relationship between a dependent
variable and one or more independent variables (or
'predictors').

44. Scatter Plots
• Scatter plots are used to establish relationship between two
characteristics.
• The Factor which supposed to determine the value of other factor is
called Independent variable.
• Factor which is assumed to be impacted by the other factor is called
Dependent variable.
• In the equation, y = f (x).
• X is assiumed to affect the value of y. ie., y is depending on x
• Hence, x is the independent variable and y is dependent variable.
Always plot independent variable in x axis.
In this example, each dot
shows one person's weight
versus his height.

45. Relationships

46. Regression Analysis – Linear
Model
 A linear relationship simply means that a change of a given size in x
produces a proportionate change in y.
 Described by the line equation
y = a + bx

47. Regression - Exercise
A Gold ornaments selling company is interested in increasing the
sales of it’s outlet and cost is a concern. The company has collected
data from it’s past sales on the number of products sold and period it
sold it in. The company is looking to increase their sales in the coming
year.
Chart the data points and data to be captured from the customers to
improve the sales based on past data.

48. Regression Statistics
Multiple R 0.392016326
R Square 0.1536768
Adjusted R Square 0.133034771
Standard Error 28.00614766
Observations 43
ANOVA
df SS MS F Significance F
Regression 1 5839.325274 5839.325274 7.444849439 0.009326246
Residual 41 32158.11659 784.344307
Total 42 37997.44186
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 54.38338048 4.655821798 11.68072638 1.26915E-14 44.98075761 63.78600335 44.98075761 63.78600335
Unit Cost -0.003113089 0.001140942 -2.72852514 0.009326246 -0.005417268 -0.00080891 -0.005417268 -0.00080891

49.  Multiple R. This is the correlation coefficient. It tells you how strong the linear relationship is.
For example, a value of 1 means a perfect positive relationship and a value of zero means no
relationship at all. It is the square root of r squared (see #2).
 R squared. This is r2, the Coefficient of Determination. It tells you how many points fall on the
regression line. for example, 80% means that 80% of the variation of y-values around the
mean are explained by the x-values. In other words, 80% of the values fit the model.
 Adjusted R square. The adjusted R-square adjusts for the number of terms in a model. You’ll
want to use this instead of #2 if you have more than one x variable.
 Standard Error of the regression: An estimate of the standard deviation of the error μ. This
is not the same as the standard error in descriptive statistics! The standard error of the
regression is the precision that the regression coefficient is measured; if the coefficient is large
compared to the standard error, then the coefficient is probably different from 0.
 Observations. Number of observations in the sample.

50. EXPLAINED PART TWO: ANOVA
 SS = Sum of Squares.
 Regression MS = Regression SS / Regression degrees of
freedom.
 Residual MS = mean squared error (Residual SS / Residual
degrees of freedom).
 F: Overall F test for the null hypothesis.
 Significance F: The significance associated P-Value.

51. INTERPRET REGRESSION COEFFICIENTS
 Coefficient: Gives you the least squares estimate.
 Standard Error: the least squares estimate of the standard error.
 T Statistic: The T Statistic for the null hypothesis vs. the alternate
hypothesis.
 P Value: Gives you the p-value for the hypothesis test.
 Lower 95%: The lower boundary for the confidence interval.
 Upper 95%: The upper boundary for the confidence interval.

52. Time Series Forecasting
 An ordered sequence of values of a variable at equally spaced
time intervals
 Can be used for:
 Sales Forecasting
 Budgetary Analysis
 Stock Market Analysis
 Yield Projections
 Process and Quality Control
 Inventory Studies
 Workload Projections

53.  https://www.youtube.com/watch?v=gHdYEZA50KE

54. Standard Deviation

55. Hypothesis Testing
 Hypothesis testing is a statistical procedure in which a choice is made
between a null hypothesis and an alternative hypothesis based on
information in a sample.
 The null hypothesis, denoted H0, is the statement about the population
parameter that is assumed to be true unless there is convincing
evidence to the contrary.
 The alternative hypothesis, denoted Ha, is a statement about the
population parameter that is contradictory to the null hypothesis, and is
accepted as true only if there is convincing evidence in favor of it.

56. Hypothesis Testing
 Null hypothesis stands true until and otherwise proved wrong.
 Accused is considered as Innocent until and otherwise proven
guilty.

57. Hypothesis Testing
 We want to take a practical problem and change it to a statistical
problem
 We use relatively small samples to estimate population parameters
 There is always a chance that we can select a “weird” sample
 Sample may not represent a “typical” set of observations
 Inferential statistics allows us to estimate the probability of getting a
“weird” sample

58. Why do Hypothesis Testing?
 To determine whether there is a difference between processes
 To validate process improvements (to prove that the process
improvements done are yielding results)
 To identify the factors which impact the Mean or Standard deviation

59. Errors
Type-1 (a) Error:
Rejecting the Null hypothesis when it is true
a-value indicate the probability of Type-1 error happening
Type-2 (b) Error:
Failing to reject Null hypothesis when it is false
b-value indicates the probability of Type-2 error happening

60. Hypothesis & Risk
 When accepting or rejecting a hypothesis, we do so with a known
degree of risk and confidence
 To do so, we specify in advance of the investigation the magnitude
of decision risk and test sensitivity which is acceptable

61. Hypothesis and Decision Risk
At  level of significance, the degree of confidence in our decision
Is (1- ) which is called confidence coefficient.

62. Prescriptive Analytics

63. FMEA
Failures
are any errors or defects, especially ones that affect the customer,
and can be potential or actual.
Failure Mode
is the ways or modes by which something might fail.
Effect Analysis
refers to studying the consequences of those failures

64. Steps to construct FMEA
 For each of failure modes identified, give scoring (from 1 to 10)
based on below table
Category Criteria Question Highest Score
(10)
Lowest Score
(1)
Severity How severe are the
consequences of this
failure mode?
Fatal / very high No risk
Occurrence How frequently it
happens?
Always Never / Very rare
Detection How quickly the
failure could be
identified by our
system?
We cant identify,
we will come to
know this upon
consumption by
customer
The failure will be
detected as it
happens and will
not move further

65. Failure Mode & Effects Analysis
Process / product FMEA Date (Original)
FMEA Team (Revised)
Project Name Page: Of
Process Actions Results
Process
steps/
requireme
nts
Potential
failure
mode
Potential
effectsof
failure
Severity
Potential
cause(s)
offailure
Occurren
ce
Current
controls
Detection
Risk
priority
number
Recomme
nded
action
Responsi
bilityand
target
completio
ndate
Action
taken
Severity
Occurren
ce
Detection
Risk
priority
number
Text Text Text Number Text Number Text Number calculati
on
Text Text Text Number Number Number Calculati
on

66. Cause and Effect Diagram
This is also known as Ishikawa Diagram or Fish-bone Diagram.
Dr Ishikawa suggested that every problem we face in our shop floor
could be caused by any of these six factors, viz., Machine, Method,
Material, Measurement, Man and Mother Nature.
It is not recommended to add a seventh factor.
Machine Method Material
Measurem
ent
Man Mother
Nature
6 Category of causes Effect
Poor
Process
Capability

67. Cause and Effect Diagram
In true sense, Fishbone diagram is a stand alone tool for root cause
analysis, where brainstorming is the start point, followed as category
brainstorming. Root cause analysis is done by drawing branches to the
main 6 bone of fish.
But in DMAIC, we spilt the functions as
1. Brainstorming or FMEA
2. Fish-bone for categorical analysis and
3. Root Cause analysis using Why-Why Analysis
Hence, after brainstorming, fit every primary cause in appropriate
category on fishbone diagram and ensure all the 6 bones of the fish is

68. Root Cause Analysis using Why-
Why
 Why-Why Analysis is a simple and effective method of finding out
root causes of a problem.
 It is a method of questioning all what we know about the problem
with why (all potential causes we identified in brainstorming or
FMEA).
 This relentless questioning opens up our thinking process and lead
to root cause of the problem.
 Each of the possible causes listed in the brainstorming or FMEA is
questioned and we need to have at least 2 root causes per possible
cause.

69. Prioritisation of Root Causes
 The scoring criteria are as below.
Categor
y
Criteria Question Highest Score
(10)
Lowest Score
(1)
Severity How strongly the root cause
and the problem (project y)
are connected? Or how
much % of problem is
associated with this cause?
They are strongly
related. This root
cause give raise to
100% of the
problem.
Seemingly no
connection
Occurre
nce
How frequently it happens? Always Never / Very rare
Control
lability
How quickly and completely
the team can take actions on
this root cause.
(team’s authority, expertise
and technology)
Team can act right
away. They are
adequately
authorised and
skilful.
Team has to cross
multiple approvals
and also needs
additional
expertise

70. Day 3

71. Test Assumptions

72. Analyzing
 Quantitative Data
 Frequency Analysis
 Difference Analysis
 Qualitative Data
 Descriptive Analysis
 Interpretive Analysis

73. Brain Storming
 SCOT
 Cause and Effect
 Comparative Analysis

74. Data Visualization
 Charts
 Flow Charts
 Graphs
 Pictures
 Map

75. Software
 SAS
 Python Programming
 R programming
 Microsoft Excel
 Google Spreadsheets/ Fusion Tables
 Tableau (Online tool)

76. MaxStat by MaxStat Software
Complete statistics package with intuitive user interface and easily understandable results.
Designed for researchers and students
SPSS by IBM
Predictive Analytics can uncover unexpected patterns and associations and develop models to
guide front-line interaction
Minitab 17 by Minitab
Analyze your data and improve your products and services with the leading statistical software
used for quality improvement worldwide.
DataMelt ("Dmelt") by jWork.ORG
Data analysis, math and data visuzalization program which combines the power of Python and
Java (free)
Analytica by Lumina Decision Systems
Analytica is a powerful, stand-alone application for visual quantitative modeling with a full array of
statistical analysis functions.

77. Statwing by Statwing
Statwing chooses statistical tests automatically, then reports results in plain English. Statwing is delightful and efficient analysis.
Stata by StataCorp
Stata statistical software is a complete, integrated statistical software package.
STEM by Princeton National Surveys
Excel macro package for running statistical tests on summary data. Output arranged to easily produce graphs in PowerPoint.
XLSTAT by Addinsoft
Variety of tools to enhance the analytical capabilities of Excel, making it the ideal for data analysis and statistics requirements.
AcaStat by AcaStat Software
Statistical software and instructional aids to help you quickly organize and analyze data.
AlterWind Log Analyzer by AlterWind
A log analyzer tool for determining the basic characteristics of the hits on your site.
Analyse-it by Analyse-it Software
Statistical analysis software for researchers in environmental & life sciences, engineering, manufacturing and education.
Analysis Studio by Appricon
Provides an end to end model generation process designed for fast development, analysis and deployment.

78. ChemStat by Starpoint Software
Full featured RCRA compliant statistical analysis of ground water
data.
CoPlot by CoHort Software
A program for making publication-quality maps, technical
drawings, and 2D and 3D scientific graphs; includes a statistical
add-on.
Decision Analyst STATS by Decision Analyst
Windows-based statistical software for marketing research.

79. Decision Science by Stone Analytics
Embeddable analytic engines designed for integration into a wide variety
of enterprise applications.
Develve by Develve Statistical Software
Statistical software for fast and easy analysis. Basic statistics, Design of
Experiments, Gauge R&R and Sample size calculations.
EasyFit by MathWave Technologies
Data analysis and simulation software with data management, reporting
functionality for probability distribution selection

80. ESBStats by ESB Consultancy
Statistical Analysis and Inference Software Package for Windows.
Forecast Pro by Business Forecast Systems
A standalone analytic tool for business forecasting that combines proven
statistical methods with an intuitive interface.
JMP Statistical Software by JMP Statistical Software
JMP, data analysis software for scientists and engineers, links dynamic data
visualization with powerful statistics, on the desktop.
KnowledgeSTUDIO by Angoss
Business intelligence and predictive analytics software suite with decision tress
and data visualization.

81. MATLAB by The MathWorks
A programming environment for algorithm development, data analysis, visualization, and numerical computation.
MedCalc by MedCalc Software
A complete Windows-based statistical program for biomedical researchers.
Number Analytics by Number Analytics
Provides statistical analytics software for business users, pricing & promotion optimization, conjoint analysis, new
product design.
PolyAnalyst by Megaputer Intelligenc
Offers a comprehensive selection of algorithms for automated analysis of text and structured data.
Predictive Suite by Predictive Dynamix
Computational intelligence software for data mining analysis and predictive modeling.
Scilab by Scilab Enterprises
Open source software for numerical computation providing a computing environment for engineering and scientific
applications.

82. SigmaPlot by Systat Software
Systat Software presents award winning scientific data analysis software.
The R Project by R-Project.Org
Software environment for statistical computing and graphics (free).
Orange by Biolab.Si
Open source data visualization and data analysis for novice and expert (free)
Statistix by Analytical Software
Easy-to-use, comprehensive statistics and data manipulation
Weka by Waikato Machine Learning Group
Machine learning algorithms for data mining tasks software (free).
UNISTAT by UNISTAT
A statistical software package featuring a statistics add-in for Excel data analysis, charting
and presentation-quality reporting