Recently, I was invited to imarticus.org's Bangalore branch to deliver a guest lecture on Analytics for its students. The session was around going through last 5 years of professional journey in advance analytics area to consolidate my key learnings and share with students. Would love to blog in detail on this topic someday with free time. However, Sharing the deck which I shared with students as of now

1. 14 Mistakes/Learnings
of
Analytics Journey
By Kumar Saurabh

2. 14 Mistakes/Learnings of Analytics Journey
Business & Strategy Analytical & ML
Tackling Too Much Too Fast
Identification of the right
business problem is at the core
of successful analytics projects.
Failure to Clearly Define
Objectives
Failure to Plan for Deployment
Excluding Domain Subject-
Matter Experts
Losing sight of the bigger
picture and the biggest picture
is the business
Correlation and causation
Ignorance of outliers
Underestimate the complexity of
computation and cost , solutions
that lack business viability
Focus on default statistical
accuracy w/o ROI & interpretation
Concentrating on algorithm than
data, right variables and
exploratory data analysis
Insufficient research on problem
Not sure about the quality
of data and how it is
captured
Expecting a perfect data
and working on imperfect
data which could fail
Data
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3. 14 Mistakes/Learnings of Analytics Journey
• Tackling Too Much Too Fast
• Identification of the right business problem is at the core of successful
analytics projects.
• Failure to Clearly Define Objectives
• Failure to Plan for Deployment
• Excluding Domain Subject-Matter Experts
• Losing sight of the bigger picture and the biggest picture is the business
• Correlation and causation
• Ignorance of outliers
• Underestimate the complexity of computation and cost , solutions that
lack business viability
• Focus on default statistical accuracy w/o ROI & interpretation
• Concentrating on algorithm than data, right variables and exploratory
data analysis
• Insufficient research on problem
• Not sure about the quality of data and how it is captured
• Expecting a perfect data and working on imperfect data which could fail
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2
3
4
5
6
7
8
12
13
14
11
10
9
AI Based Software Bug
Failure Prediction
Next Best Offer for M&E
Market Mix Models
Employee Absenteeism
Forecasting
Employee Churn
Prediction
Customer Lifetime
Management Analytics
1
2
3
6
7
8
4
9,10
10
11
13
14
12
5

4. AI Based Software Bug Failure Prediction
• Tackling Too Much Too Fast
• Identification of the right business problem is at the core of successful
analytics projects.
• Failure to Clearly Define Objectives
• Failure to Plan for Deployment
• Excluding Domain Subject-Matter Experts
• Losing sight of the bigger picture and the biggest picture is the business
• Correlation and causation
• Ignorance of outliers
• Underestimate the complexity of computation and cost , solutions that
lack business viability
• Focus on default statistical accuracy w/o ROI & interpretation
• Concentrating on algorithm than data, right variables and exploratory
data analysis
• Insufficient research on problem
• Not sure about the quality of data and how it is captured
• Expecting a perfect data and working on imperfect data which could fail
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3
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7
8
12
13
14
11
10
9
AI Based Software Bug
Failure Prediction
Next Best Offer for M&E
Market Mix Models
Employee Absenteeism
Forecasting
Employee Churn
Prediction
Customer Lifetime
Management Analytics
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3
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7
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9,10
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5

5. AI Based Software Bug Failure Prediction
Business Objective
Data
Not sure about the quality of data
and how it is captured
Tackling Too Much Too Fast
Configuration data of software,
topology data of software, events data
of software.
Data collection started 6 months
back. Customers open their
environment on a daily basis and data
is captured through APIs
A product manager of a software
company has launched a value added
offering to its customers where it
wants to provide better software
experience by proactive deflection of
software bugs
Could there be data sanctity
and data quality issues?
Could there be data
consistency issues?
Could there be data longevity
issue?
Are there initial milestones to
be achieved before building AI
system?
What are the chances AI would
fail?
Why AI would fail?
Analytics Operationalization is
a journey not the 1st step

6. Next Best Offer for M&E
• Tackling Too Much Too Fast
• Identification of the right business problem is at the core of successful
analytics projects.
• Failure to Clearly Define Objectives
• Failure to Plan for Deployment
• Excluding Domain Subject-Matter Experts
• Losing sight of the bigger picture and the biggest picture is the business
• Correlation and causation
• Ignorance of outliers
• Underestimate the complexity of computation and cost , solutions that
lack business viability
• Focus on default statistical accuracy w/o ROI & interpretation
• Concentrating on algorithm than data, right variables and exploratory
data analysis
• Insufficient research on problem
• Not sure about the quality of data and how it is captured
• Expecting a perfect data and working on imperfect data which could fail
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2
3
4
5
6
7
8
12
13
14
11
10
9
AI Based Software Bug
Failure Prediction
Next Best Offer for M&E
Market Mix Models
Employee Absenteeism
Forecasting
Employee Churn
Prediction
Customer Lifetime
Management Analytics
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3
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9,10
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5

7. Next Best Offer for M&E
Business Objective
Data
Expecting a perfect data and
working on imperfect data which
could fail
Failure to Plan for Deployment
Underestimate the complexity of
computation and cost , solutions
that lack business viability
Focus on default statistical
accuracy w/o ROI & interpretation
To create a recommendation system to identify
next best offer for every customer from a given
bouquet of channels, packages and services
Create a recommendation score for every
subscriber and offering
Subscriber Demographic, monthly payment
and monthly subscription data for last 2 years
• What accuracy metric will you
choose?
• How will you capture return on
investment?
• How much time and cost will it take
to execute the model?
• How frequently you would like to
refresh the model?
• What would be the most ideal data?
• Is the existing data sufficient to
learn?
• How the model will be
operationalized?
• How business would like to test the
strength of model?
• How business will justify ROI for
deployment?

8. Market Mix Models
• Tackling Too Much Too Fast
• Identification of the right business problem is at the core of successful
analytics projects.
• Failure to Clearly Define Objectives
• Failure to Plan for Deployment
• Excluding Domain Subject-Matter Experts
• Losing sight of the bigger picture and the biggest picture is the business
• Correlation and causation
• Ignorance of outliers
• Underestimate the complexity of computation and cost , solutions that
lack business viability
• Focus on default statistical accuracy w/o ROI & interpretation
• Concentrating on algorithm than data, right variables and exploratory
data analysis
• Insufficient research on problem
• Not sure about the quality of data and how it is captured
• Expecting a perfect data and working on imperfect data which could fail
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2
3
4
5
6
7
8
12
13
14
11
10
9
AI Based Software Bug
Failure Prediction
Next Best Offer for M&E
Market Mix Models
Employee Absenteeism
Forecasting
Employee Churn
Prediction
Customer Lifetime
Management Analytics
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2
3
6
7
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4
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9. Market Mix Models
Business Objective
Data
Excluding Domain Subject-Matter
Experts
Correlation and causation
• Time series based sales data
• Marketing expense data
through multiple channels
• Customer Feedback data
Create a marketing spend plan to maximize
return on marketing investment by increase
in sales
Sales were growing because the
sector was in a boom phase and
irrespective of marketing expense,
it could have grown. Correlation
but not causation
Correlation goes down once chart
is plotted against difference of sales
in chronological order vs difference
of marketing expenses
Correlation further goes down
when sales is plotted against
marketing expenses with a time lag

10. Employee Absenteeism Forecasting
• Tackling Too Much Too Fast
• Identification of the right business problem is at the core of successful
analytics projects.
• Failure to Clearly Define Objectives
• Failure to Plan for Deployment
• Excluding Domain Subject-Matter Experts
• Losing sight of the bigger picture and the biggest picture is the business
• Correlation and causation
• Ignorance of outliers
• Underestimate the complexity of computation and cost , solutions that
lack business viability
• Focus on default statistical accuracy w/o ROI & interpretation
• Concentrating on algorithm than data, right variables and exploratory
data analysis
• Insufficient research on problem
• Not sure about the quality of data and how it is captured
• Expecting a perfect data and working on imperfect data which could fail
1
2
3
4
5
6
7
8
12
13
14
11
10
9
AI Based Software Bug
Failure Prediction
Next Best Offer for M&E
Market Mix Models
Employee Absenteeism
Forecasting
Employee Churn
Prediction
Customer Lifetime
Management Analytics
1
2
3
6
7
8
4
9,10
10
11
13
14
12
5

11. Employee Absenteeism Forecasting
Business Objective
Data
Losing sight of the bigger picture and
the biggest picture is the business
Ignorance of outliers
Insufficient research on problem
• What is important for business?
• What are potential solutions and
which is more suitable for business?
A manager of an auto manufacturing
plant with 10000 employees want to
know future absenteeism behavior for
better production schedule to meet
demand supply match
• Employee demographic,
absenteeism and performance data
• Why there were outliers?
• Did we miss some important
variables?

12. Employee Churn Prediction
• Tackling Too Much Too Fast
• Identification of the right business problem is at the core of successful
analytics projects.
• Failure to Clearly Define Objectives
• Failure to Plan for Deployment
• Excluding Domain Subject-Matter Experts
• Losing sight of the bigger picture and the biggest picture is the business
• Correlation and causation
• Ignorance of outliers
• Underestimate the complexity of computation and cost , solutions that
lack business viability
• Focus on default statistical accuracy w/o ROI & interpretation
• Concentrating on algorithm than data, right variables and exploratory
data analysis
• Insufficient research on problem
• Not sure about the quality of data and how it is captured
• Expecting a perfect data and working on imperfect data which could fail
1
2
3
4
5
6
7
8
12
13
14
11
10
9
AI Based Software Bug
Failure Prediction
Next Best Offer for M&E
Market Mix Models
Employee Absenteeism
Forecasting
Employee Churn
Prediction
Customer Lifetime
Management Analytics
1
2
3
6
7
8
4
9,10
10
11
13
14
12
5

13. Employee Churn Prediction
Business Objective
Data
Failure to Clearly Define Objectives
Focus on default statistical accuracy
w/o ROI & interpretation
Concentrating on algorithm than
data, right variables and exploratory
data analysis
• Why some one churn?
• Does someone churn all of a sudden?
• Is the data structured to process of
churn causality?
• Ever heard of temporal abstraction?
• Data Scientist: Our model is 95%
accurate
• Sponsor: But my churn rate is 2%.
Can you identify that 2?
• Which is best way to present
statistical output for better
interpretation?
• What are the flaws in current
business objective?
• What could be possible point of
disagreements in stated
objective?
Create a score for my employee
churn probability so that I can stop
from my employee to churn
Customer demographic, operational,
performance, reporting and
absenteeism data for last 2 years

14. CLVM Analytics
• Tackling Too Much Too Fast
• Identification of the right business problem is at the core of successful
analytics projects.
• Failure to Clearly Define Objectives
• Failure to Plan for Deployment
• Excluding Domain Subject-Matter Experts
• Losing sight of the bigger picture and the biggest picture is the business
• Correlation and causation
• Ignorance of outliers
• Underestimate the complexity of computation and cost , solutions that
lack business viability
• Focus on default statistical accuracy w/o ROI & interpretation
• Concentrating on algorithm than data, right variables and exploratory
data analysis
• Insufficient research on problem
• Not sure about the quality of data and how it is captured
• Expecting a perfect data and working on imperfect data which could fail
1
2
3
4
5
6
7
8
12
13
14
11
10
9
AI Based Software Bug
Failure Prediction
Next Best Offer for M&E
Market Mix Models
Employee Absenteeism
Forecasting
Employee Churn
Prediction
Customer Lifetime
Management Analytics
1
2
3
6
7
8
4
9,10
10
11
13
14
12
5

15. Customer Lifecycle Management Analytics
Business Objective
Data
Identification of the right business problem is at the core of successful
analytics projects.
What would you recommend to customer within a limited budget among
following options for CLTV Analytics?
• Next Best Offer Analytics
• Customer Churn Analytics
• Customer Lifetime Value Analytics
Use analytics to understand customer
lifecycle and create win-win scenario
for both customer as well as company
leading to better customer
experience, higher revenue and
higher return on investment
CRM data, customer demographic
data, Customer Purchase Behavior
data

16. Why It Happens?
Sponsor
Data Scientist
Technology,
Manager, SME End User1
How analytics will help
me in achieving my KPI?
Why should I spend my
time and what is there in
it for me?
Will it lead to high return on investment for the money I am investing?
How can I build a highly accurate model?
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3
4

17. Thank You