Business Analytics, data research

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Flt Lt Renu Lamba, PhD
Associate professor, Graphic Era
Deemed to be University, Department
of Management Studies
Data and Business Analytics

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Thanks for Inviting Me

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Flt Lt Renu Lamba, PhD is an Associate Professor,
at Graphic Era Deemed to be University, Dehradun
She has received her PhD in Management,
Financial Inclusion through Microfinance: A
Role comparison of MFIs & RRBs, from
Punjab Engineering College, Chandigarh and
Bachelor in Technology from NIT Raipur.
Her research interests include leveraging of
Technology to achieve Financial Inclusion,
social and Rural entrepreneurship, Design
Thinking and Innovation.
Prior to taking up education as her full-time
career she worked in corporate sector, mainly
in operations. She is from the pioneering
batches of Indian Air Force where she was
taking care of telecommunication network for
the ground defence.

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Understanding the expectation from the course
Data and Business Analytics

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Good –Bad 2 x 2 Matrix
Where do you think your organization/department lies in this matrix?
Results
Processes
Bad Good
Bad
Good
1
2
4
3

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Good –Bad 2 x 2 Matrix
Where do you think your organization/department lies in this matrix?
Results
Processes
Bad Good
Bad
Good
?
√
?
Lucky!
Opportunities for
improvements!

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A Problem with Problem Solving
IUMRINC TO GONGUJSIONS
JUMPING TO CONCLUSION

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A Problem with Problem Solving
IUMRINC TO GONGUJSIONS
JUMPING TO CONCLUSION

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A Problem with Problem Solving
JUMPING TO CONCLUSION
Problem
Impressions and
assumptions
The
solutions
Facts
Facts
Facts
Facts
Assumptions
Our natural tendency?

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• Analytics: Analytics is science of manipulating
data by applying models and statistical formulae
on data to find insights.
Insights: The key factors that help us solve various
problems. (Helps us to grow our business, take right
forecast the future sales and technology etc)
This is called business Analytics
What is Data & Business Analytics

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Analytics is about visualisation, charts, dashboard and much more…

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For Example

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What are the different terminology
that you hear around analytics
• Business Analytics
• Business Intelligence
• Decision Science
• Big Data
• Data Analytics
• Data Analysis
• Data Mining etc…
They are though different or differently defined,
but for most part you have to understand that they all
support the same goal, and that is
Turning data into useful insight

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Types of Analytics
Descriptive Analytics - What has happened
 What was our sale
 What was our market share
 Why did the talent pool leave the tech company
Predictive Analytics - What might happen
 What are the future predictions
Prescriptive Analytics – What should we do
So based on the predictions of the future, what
course of action a business should take
So more value is added as we go down

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Lets take an example
DESCRIPTIVE
ANALYTICS
• What were our sales?
• What was our market
Share?
• What product was most
popular?
PREDICTIVE
ANALYTICS
• What are the expected
sales?
• What is the expected
market share
PRESCRIPTIVE
ANALYTICS
• What product should we
market to them?

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Analytics Lifecycle-Scientific Method of Analytics

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Do Not Rush Or Else You Will Crash

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Business Understanding
What is business problem? What is your
business goal!! For example
 Optimising Pricing-Boost Revenue
 Segment Customer- Customise Product
 Pinpoint Bottlenecks – Supply Chain

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Data Understanding
Data availability, quality, granularity,
frequency, its cost etc
Data
we
Have
Data
we
Need

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Data Understanding
To understand and explore data we often use a
“sand box”-which is a safe place to explore
data and that’s because we don’t want to mess
up with the live data-we don’t disturb the
“Production”

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Sl no Name Gender City DOB
1 Praveen Female 10-01-1999
2 Sultan M Delhi
21st September
2001
Sl no Name Gender City DOB
1 Praveen Female Dehradun 10-01-1999
2 Sultan Male Delhi 21-09-2001
Data Preparation

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Modeling
Model is a simplified description of a system or
process to assist calculations and Predictions
Model mimics the real world
So in analytics we are making models that help us
make predictions, these models need to be simple
and reliable

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Modeling
Examples
A model that predicts the likelihood that a car
insurance customer will get into car accident next
year
or
A pharmaceutical company predicts that the flu in
the coming season is going to be rampant

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Modeling
 Exploratory analysis
 Variable selection
Model it and fine tune it
around that model

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Software, on which we have to develop our capabilities-that
helps to build models
SPSS
AMOS PLS
Excel
Level-0
Level-1
Level-2

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Evaluation and Deployment
 How effective the model is?
 Will it be able to make the
right predictions?
Are we prepared to launch it?

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Which foundation statistical methods we should know to start a
quantitative research? 1/3
Basis Tests

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Multivariate Methods
Which foundation statistical methods we should know to start a
quantitative research? 2/3

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Multivariate Methods
Which foundation statistical methods we should know to start a
quantitative research? 3/3

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Desired approach to analysis

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Thankyou
Q n A