The document discusses the significance of data science for organizations, particularly during economic downturns, emphasizing its role in solving business problems and the need for professionals to adapt their skills. It highlights common challenges in data science projects, such as misidentifying problems and spending excessive time on data cleaning. Key recommendations include mastering knowledge application, learning non-core skills, and staying updated with technology to effectively leverage data analytics.

1. Reality Check: What’s the Value of
Data Science for Organizations?
Ganes KesariColloquium, Apr 2020

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DATA SCIENCE:
WHAT’S THE VALUE
REALITY CHECK:
HOW TO THRIVE?
IT’S A RECESSION.
WHY DATA NOW?

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INTRODUCTION
Ganes Kesari
Co-founder & Head of Analytics
“Simplify Data Science for all”100+ Clients
Insights as Stories
@kesaritweets Help start, apply and adopt Data Science

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DATA SCIENCE:
WHAT’S THE VALUE?
IT’S A RECESSION.
WHY DATA NOW?
REALITY CHECK: HOW
TO THRIVE?

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Senior Data ScientistPrincipal AI StorytellerChief Data Wizard
FEELING LUCKY? HERE’S A DATA SCIENCE TITLE GENERATOR!
Data
Statistical
ML
AI
Chief
Principal
Senior
Junior
Associate
Deputy
Assistant
Scientist
Engineer
Analyst
Designer
Developer
Designer
Storyteller
Ninja
Chef
Wrangler
Evangelist
Rock Star
Wizard
Alchemist
Vanity keywords Areas Activities

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BUZZWORDS AND BUSTED BUDGETS

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THE JOURNEY FROM DATA TO DECISIONS
Data Engineering
MaturityPhases
Data Science
Data as
‘Culture’
Data
Collection
Data Storage
Data
Transformation
Reporting Insights Consumption Decisions
Source: Article – When and how to build out your data science team

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THE JOURNEY FROM DATA TO DECISIONS
Data Engineering Data Science
Data
Collection
Data Storage
Data
Transformation
Reporting Insights Consumption
MaturityPhases
Source: Article – When and how to build out your data science team
Data as
‘Culture’
Decisions

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REPORTING: DESCRIPTIVE SUMMARIES
2019 Boston Chicago Detroit New York
Month Price Sales Price Sales Price Sales Price Sales
Jan 10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58
Feb 8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76
Mar 13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71
Apr 9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84
May 11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47
Jun 14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04
Jul 6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25
Aug 4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50
Sep 12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56
Oct 7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91
Nov 5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89
Average 9.0 7.50 9.0 7.50 9.0 7.50 9.0 7.50
Variance 10.0 3.75 10.0 3.75 10.0 3.75 10.0 3.75
Revenue numbers from four Cities

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INSIGHT: PREDICTING TELCO CUSTOMER CHURN
Tenure (months)
0 - 12 36+12-36
Data Usage >
1.5 GB
01
YN
Bill > $65
0
N Y
• Simple Decision-tree model offered ~30% reduction in churn
• Advanced black-box models offered ~50%, but with low explainability
0Low Risk
1
High Risk
Source: Gramener

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INSIGHT: IDENTIFYING QUALITY OF LIFE FROM SATELLITE IMAGES
Source: https://qol.gramener.com/

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CONSUMPTION: WHEN ARE PEOPLE BORN IN THE US?
Source: https://gramener.com/posters/Birthdays.pdf
..so, conceptions
might happen here
Very high births..
Love the Valentine’s?
Too busy holidaying?
Avoid April
Fool’s Day?
Unlucky 13th?
More births
Fewer births

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More births
CONSUMPTION: WHAT’S THE BIRTH PATTERN IN INDIA?
Source: https://gramener.com/posters/Birthdays.pdf
Fewer births
Most births in
the first half
A striking birth pattern seen on the 5th, 10th,
15th, 20th and 25th of each month…
Very low births
Aug onwards
Why? Birthdates are ‘changed’ to
aid early school admissions
.. this is a typical
indication of fraud!

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CONSUMPTION: DECODING SHAKESPEARE’S SONNETS
Source: https://gramener.com/shakespeare/

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INSIGHT + CONSUMPTION: DATA STORIES FROM THE WORLD BANK
Source: World bank storytelling, by Gramener

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DATA CULTURE: WHEN DATA DRIVES THE ENTIRE ORGANIZATION
Source: Netflix.com; Slides from InfoQ– ML Infra at Netflix

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DATA SCIENCE:
WHAT’S THE VALUE?
IT’S A RECESSION.
WHY DATA NOW?
REALITY CHECK: HOW
TO THRIVE?

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1. Most Data Science projects solve the wrong Problem..
Tip #1: Master the application of knowledge

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PROFESSIONALS STRUGGLE WITH APPLYING THEIR DATA SKILLS
Scenario Approaches to Apply
Just 1 week’s data (single data point!)
Data for the past 3 weeks
6 month’s data, with moderate patterns
Use heuristics and business
judgement
Simpler techniques - Moving
averages, extrapolations..
Statistics and simple time series
forecasting techniques
2 year’s data with useful signals Advanced techniques with time
series and causal approaches

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GUIDELINES TO APPLY DATA SCIENCE TO BUSINESS PROBLEMS
• What’s the business problem you’re solving?
• Who is your audience and what do they need?
• What data do you have and what approaches
are relevant?
• What insights are important and are they
actionable?
• Take feedback and iterate

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AI IS COMING FOR THE DATA SCIENCE JOBS
AI and automation will
do away with most of
the grunt work in the
data science workflow
today.
Applied knowledge will
keep you relevant for
much longer.

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2. Data Analytics needs a lot more than Data & Analytics..
Tip #2: Learn non-core skills

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DATA SCIENCE SOLUTION: LET’S TAKE THIS EXAMPLE..
Source: World bank storytelling, by Gramener

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..AND BREAK IT DOWN INTO THE BUILDING BLOCKS
Domain
Design
Analytics
Development
• Impact analytics
• Clustering techniques
• Business workflow
• Influencing factors
• Frontend/backend coding
• Data transformation
• User journey
• Visuals & aesthetics
Project
Management
• Piecing it all together
• Change management

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HERE ARE THE 5 ROLES & SKILLS CRITICAL FOR DATA SCIENCE
Data
Translator
ML
Engineer
Information
Designer
Data
Scientist
Data Science
Manager
Comic characters from Gramener Comicgen library
Domain
Design
Analytics
Development
Project
Management
• Domain expertise
• Business analysis
• Solutioning
• Software engineering
• Front/back-end coding
• Data pipelining
• Information design
• User centered design
• Interface/visual design (parts)
• Stats & ML
• Interpret insights
• Scripting skills
• Project management
• Business analysis/solutioning
• Team handling

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3. Data cleaning takes up a majority of time on projects..
Tip #3: Sharpen ability to handle data

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In data science, 80% of the time is spent preparing data,
and the other 20% on complaining about preparing the data!
- Kirk Borne
“

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BE PREPARED FOR DATA TO BE UNSUITABLE FOR ANALYSIS
Source: Kaggle Survey
Gathering and cleaning data is a critical pre-requisite for ‘meaningful’
insights

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LEARN DATA HANDLING AND BUDGET TIME FOR IT IN YOUR WORK
Data
deduplication
Data
standardization
Data
normalization
Quality check
Exploratory
analysis
Data Cleaning
& Preparation

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4. Technology goes obsolete faster in Data Science..
Tip #4: Learn new tools quickly

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WHAT DOES THE DATA TOOLS LANDSCAPE LOOK LIKE?
The tool does not matter. A person’s skill with the tool does.
Pick an ability to learn new tools rapidly
Source: https://mattturck.com/data2019/

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EXAMPLE: WHAT ARE YOUR TOOL OPTIONS TO VISUALIZE DATA?
Code-based
Plug-n-play
Flexibility
Complexity
Google Data Studio
Excel
Google Sheets
Tableau
Raw
Vismio
Datawrapper
Timeline JS
Polestar
Vega
Vega-lite
d3,
matplotlib
C3
High charts
Nvd3
Gramex
ggplot, bokeh
Plotly
Choose tools based on flexibility, your background and tool availability

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Tip #4: Learn new tools quickly
Tip #2: Learn non-core skills
Tip #3: Sharpen ability to handle data
Tip #1: Master the application of knowledge

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DATA SCIENCE:
WHAT’S THE VALUE?
IT’S A RECESSION.
WHY DATA NOW?
REALITY CHECK: HOW
TO THRIVE?

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COVID-19 HAS DISRUPTED THE GLOBAL ECONOMY..
Source: McKinsey – COVID-19 Briefing materials

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..THE US LOST ALL JOBS GAINED SINCE THE GREAT RECESSION
Source: Tax Policy Center
Over 26M jobs lost… …in just 5 weeks
Source: CNBC, Dept of Labor, Bureau of Labor Statistics

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WHAT DOES THE RECESSION MEAN FOR JOBS IN DATA SCIENCE?
Source: McKinsey report – Lives and Livelihoods
Data jobs and specialized professions
are relatively less impacted
Industries with the lowest wages and
lowest educational attainment are hit
the hardest

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HERE’S WHY DATA IS KEY FOR COVID-19 AND THE RECESSION
Enterprises
B
Community
C
Remote workforce & collaboration
Market demand & Cash flows1
2
Supply chain & Logistics3
Identifying vulnerability and contact-tracing
Tracking the COVID-19 patient lifecycle1
2
Predicting infection rates and spread2
Public Health
A
Understand behavioral shifts
Mapping the effectiveness of shutdown1
2
Address people concerns during Covid-193
Source: Gramener – NYC 311 analysisSource: Kinsa Health weather map Source: Gramener – Supply Chain flow

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HOW DO YOU STAY RELEVANT AND GROW IN YOUR CAREER PATH?
Do your own
data projects
Read/Write on
data science
Maintain a
public portfolio
Compete, learn
& re-apply
Source: Article – How to demonstrate your passion for Data

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Thank You!
@kesaritweets
/gkesari
gramener.com
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