“War is 90% information”
Napoleon Bonaparte

2
Source: "Decisive Action: How Businesses Make Decisions and How They Could Do It Better," The Economist,
Intelligence Unit.
%
Proportion of
business decision
makers would
prioritize gut feel
over data if there
was a contradiction
between the two.

“ 50% of data
science
projects will
never get
consumed…
Reference: Gartner

4
Roadblocks to Success – Gartner CDAO survey
Credit: Gartner
“Consumption of Data as key enabler ”

5
Data Engineering
ActivitiesMaturityPhases
Data Science
Data as
‘Culture’
Data
Collection
Data
Storage
Data
Transformatio
n
Reporting Insights Consumption Decisions
LOGS, IOT
INT/EXTERN
AL
STAGE/STREA
M
SQL, SPARK..
UN/STRUCTUR
ED
DATA LAKE..
CLEANING
ETL
PREPARATIO
N
AGGREGATE
S
METRICS/K
PI
REPORTS
ML
EDA
AI
Info Design
Narrative
Data Stories
WORKFLOWS
CHANGE
MGMT
ACTIONS
Driving Data Supply Driving Data Value
Maturity Levels with Data

6
Insights Output : Examples
Data as
Culture’
Data
Transformation
Consumption
MaturityPhases

7
Consumable Insights output : Examples
https://gramener.com/securities/
Maturit
y
Phase
s

8
Data generation and analysis are not sufficient.
“Cohesive Consumption of Data”
Most decision-making
discussions assume that only
senior executives make
decisions or that only senior
executives’ decisions matter.
This is a dangerous mistake…
It’s clearly a budget!
It has a lot of numbers in it!
Peter F Drucker George W Bush

9
CDOs Must Address Hearts & Minds to Drive Data Value
Data-driven
culture
Business
valueCDO
Credit: Gartner

10
Humans are pattern-seeking
story-telling animals.

Why Stories?
Stories are | emotional
Stories are | memorable
Stories are | impactful

100+ Clients
@naveengattu
Naveen Gattu
Co-founder & COO
We bridge the DATA CONSUMPTION
gap
Storytelling for
Analytics

13
FRAMEWORK RECOMMENDATIONSCASE STUDIES

14
Just
EXPOSE
the data to me
EXHIBIT
to me what is happening
with the data
EXPLAIN
to me why it’s happening
Allow me to
EXPLORE
and figure it out
Low effort High effort
High effort
Low effort
Creator
Consumer
There are Four Ways of Telling Data Stories

Let’s See Examples of Each
My aim is to plant ideas of
what’s possible

16
Just
EXPOSE
the data to me
EXHIBIT
to me what is happening
with the data
EXPLAIN
to me why it’s happening
Allow me to
EXPLORE
and figure it out
Low effort High effort
High effort
Low effort
Creator
Consumer

17
INFORMATION IN ROWS & COLUMNS

18
GOOGLE SUGGEST: INDIA’S RELIGIONS
LIN
K

19
GOOGLE SUGGEST: AUSTRALIA’S RELIGIONS
LIN
K

20
Just
EXPOSE
the data to me
EXHIBIT
to me what is happening
with the data
EXPLAIN
to me why it’s happening
Allow me to
EXPLORE
and figure it out
Low effort High effort
High effort
Low effort
Creator
Consumer

21
This is a dataset (1975 – 1990) that
has been around for several years,
and has been studied extensively. Yet,
a visualization can reveal patterns
that are neither obvious nor well
known.
For example,
• Are birthdays uniformly distributed?
• Do doctors or parents exercise the C-section option to
move dates?
• Is there any day of the month that has unusually high or
low births?
• Are there any months with relatively high or low births?
Very high births in September. But
this is fairly well known. Most
conceptions happen during the
winter holiday season.
Relatively few births during
the Christmas &
Thanksgiving holidays, as
well as New Year and
Independence Day.
Most people prefer not to
have children on the 13th of
any month, given that it’s
an unlucky day.
Some special days like April
Fool’s day are avoided, but
Valentine’s Day is quite
popular.
More births Fewer births … on average, for each day of the year (from 1975 to 1990)
Let’s Look at 15 Years of US Birth Data
Education
LINK
Fraud

22
The Pattern in India is Quite Different
This is a birth date dataset that’s obtained
from school admission data for over 10
million children. When we compare this
with births in the US, we see none of the
same patterns.
For example,
• Is there an aversion to the 13th or is there a local cultural
nuance?
• Are holidays avoided for births?
• Which months have a higher propensity for births, and
why?
• Are there any patterns not found in the US data?
Very few children are born
in the month of August, and
thereafter. Most births are
concentrated in the first half
of the year.
We see a large number of
children born on the 5th,
10th, 15th, 20th and 25th of
each month – that is, round
numbered dates.
Such round numbered
patterns a typical indication
of fraud. Here, birthdates
are brought forward to aid
early school admission.
More births Fewer births … on average, for each day of the year (from 2007 to 2013)
Education
LINK
Fraud

23
How should you hedge your Portfolio?
68% correlation between AUD &
EUR
Plot of 6-month
daily AUD - EUR
values
Block of
correlated
currencies
… clustered hierarchically using
“Hierarchical Agglomerative Clustering”
Algorithm
LINK

24
Just
EXPOSE
the data to me
EXHIBIT
to me what is happening
with the data
EXPLAIN
to me why it’s happening
Allow me to
EXPLORE
and figure it out
Low effort High effort
High effort
Low effort
Creator
Consumer

25
Financial Reporting Narratives LINK
Financial ServicesNarrativesFinancePlatform
A key problem in financial reporting is
annotating drivers of variance. For e.g.:
• Which account caused the largest
increase in assets?
• Was this the primary cause, or one
among many?
• Were there other accounts that
mitigated its effect?
These are what a financial analyst
manually analyzes, adding annotations
to the report. But this is automatable.
This natural language generator by
Gramener applies these simple rules:
• If there's more than one driver,
mention the top driver.
• If the second driver counteracts the
first driver's effect, mention it.
• Or, if the second driver has 78% of the
influence on the first, mention it
The annotations are similar to a human’s,
but without human error. It sets a
starting point for exploration, letting
people focus on review rather than
execution.

26
Vendor Consolidation & Procurement Analytics
LIN
K

27
Challenges Women Face – An Interactive Narrative
Best of the Visualization
Web, Sep 2018
LINK

28
Data-Driven Comics Can be Embedded in BI Tools LINK

29
These were
automated
using
Comicgen
A data-comic
library we
developed.
GRAMENER.COM / COMICGEN /

30
Just
EXPOSE
the data to me
EXHIBIT
to me what is happening
with the data
EXPLAIN
to me why it’s happening
Allow me to
EXPLORE
and figure it out
Low effort High effort
High effort
Low effort
Creator
Consumer

31https://tcdata360.worldbank.org/stories/tech-entrepreneurship/
LIN
KGuided Storytelling with Exploration

32
Process Optimization, Supported by Augmented Narratives
Navigation filters
Process flow
diagram indicating
bottlenecks &
volume of requests
Automated analysis
to identify areas
which need work
and which can
create maximum
impact
LINK

33
Just
EXPOSE
the data to me
EXHIBIT
to me what is happening
with the data
EXPLAIN
to me why it’s happening
Allow me to
EXPLORE
and figure it out
Low effort High effort
High effort
Low effort
Creator
Consumer
There are Four Ways of Telling Data Stories

34
By 2025, data stories will be the most widespread way
of consuming analytics
&
75% of stories will be automatically generated using
augmented analytics techniques.
Reference: Gartner report , Augmented Analytics: Teaching Machines to Tell Data Stories to
Humans

35
RECOMMENDATIONS
FOR D&A LEADERS

36
EMBRACE THE
PROCESS
1
Data Storytelling
=
Visualization +
Narrative + Context

37
2
Information
Designers
Data
Storytellers
Behavioral
Psychologist
s
EMBED DESIGN
SKILLS
https://towardsdatascience.com/the-3-missing-roles-that-every-data-science-team-needs-to-hire-97154cc6c365

38
AUTOMATE
STORYTELLING
3
Reports in plain
English with visuals
 Wealth reports
 Patient reports
 Loyalty point usage
 School report cards
NARRATIVES
Visual Insights
delivered to Inbox
 Customer segments
 Viewership shifts
 Geo-demographics for
geographic zones.
INFOGRAPHIC ALERTS
Engage through
emotions from Comics
 Price forecast
 Revenue forecasts
 Capacity utilization
 Viewership forecast
COMICGEN
Insights delivered as
Automated Videos
 Type detection
 Root cause drivers
 Factor correlation
 Cross-tabulation
DATA VIDEOS

39
Amit Kapoor, http://narrativeviz.com/playbook

40
We Narrate Insights as Data Stories
INSIGH
T
STORY
DATA
GRAMENER
COMBINES
Insights drive
action via learning
Stories change
users via emotion
ANALYSIS IS NOT USEFUL
“We get too many
analyses & reports. We
don’t get insights!”
— Every client we’ve
met
INSIGHTS ARE…
USEFUL. Can you act on it?
NON-OBVIOUS. Did you know it
before?
SIGNIFICANT. Is its impact large?
ANALYSIS ISN’T
EXPLANATION
STORIES HAVE AN…
AUDIENCE. Who are your users?
INTENT. Why are you saying
this?
EMOTION. Why should they care?
These are memorable. People act on them.
They go viral. This enables collective action.
“Don’t tell me what you did.
Tell me what to do. Make
me care!”
— Every client we’ve met

41
We Work Through our People, Process & Platform
PEOPLE
PROCESS
PLATFORM
PROCESS TO STANDARDIZE
Patterns of insights and stories emerge.
We standardize ways of systematically extracting
good insights and narrating engaging stories.
PLATFORM TO AUTOMATE
Once the process stabilizes, humans need not
perform the repetitive task. Gramex, our platform,
rapidly creates custom applications that discover
insights and narrates stories.
PEOPLE TO DISCOVER
Discovering insights and narrating stories is an art.
We work with clients to build this capability
THESE ARE INTER-DEPENDENT
Typically, every engagement has a component of all
three.
Introduction

42
In Doing this, We Bridge the Data Consumption Gap
INSIGHTS
Extract meaning using
automated patterns
AI & MACHINE
LEARNING SERVICES
VISUAL
NARRATIVES
STORYTELLING
Creative ThinkingCritical Reasoning
SOFTWARE
THROUGH
SERVWARE: augmenting human
intelligence with technology
We use technology to automate Analysis, Visuals and Narration
STORYTELLING
Binding visuals together
into a logical story
Introduction

43
Or 100+ Clients are Spread Across All Verticals
MEDIA & MARKETING PUBLIC SECTOR & NGO
TECHNOLOGY & CONSULTING BANKING & FINANCIAL SERVICES
PHARMA & HEALTHCARE MANUFACTURING & AGRICULTURE OTHERS

44
 What are the most critical skills needed in your data science team?
 What roles should you plan to hire and where should you scout for talent?
 Tips and tricks for hiring your data science team, presented with real-world
examples?
 What are the essentials for seeding a culture of data?
 How to form ‘data’ habits in your workforce?
 Best practices to show when and how you can get started on this journey
 Key reasons why data science projects fail
 How to identify your projects and prioritize them
 A standard 3-step framework for building your data science roadmap
Get Business ROI from
Data Science
ADVISORY
WORKSHOPS
Create your custom Data Science Roadmap
Build a Data Science Team to deliver Business Value
Data Culture to promote Data-Driven decision making
How to

45
Recap : Data Storytelling
• Industry Case studies
• 4E Patterns
Storytelling Patterns
• Build Data Science
Teams
• Data Science Roadmap
• Data Driven Culture
Data Advisory
workshop
Why Stories
• Aids Decision Making
• Insights as Data
Stories
Recommendations
• Build Storytelling skills
• Process
• Automate Storytelling

46
What Next?
• Read these
• Storytelling with data
• Resonate
• Show & Tell
• Data visualization society
Feel free to contact me at Naveen.gattu@gramener.com
• Practice storytelling
• Understand the context systematically
• Review chart annotations with colleagues
• Interact with experts outside your circle
• Automate this in your dashboards
Reach out for inspiration or help

47
@naveengattu
Thank
You!
gramener.com /naveengattu
gramener.com/solutions
Feel free to contact me at Naveen.gattu@gramener.com