This document discusses the importance of data storytelling and provides recommendations for data leaders. It argues that data stories are more memorable and impactful than raw data or facts. The document outlines four patterns for telling data stories and provides examples. It recommends that organizations embed design skills, automate storytelling, and embrace storytelling as part of the data insights process. Telling data stories can help people really understand data intuitively and aid decision making.
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Source: "Decisive Action: How Businesses Make Decisions and How They Could Do It Better," The Economist, Intelligence Unit.
90%
Proportion of business
decision makers would
prioritize gut feel over
data if there was a
contradiction between
the two.
4. 4
Roadblocks to Success – Gartner CDAO survey
Credit: Gartner
“Consumption of Data as key enabler ”
5. 5
Data Engineering
ActivitiesMaturityPhases
Data Science
Data as
‘Culture’
Data Collection Data Storage
Data
Transformation
Reporting Insights Consumption Decisions
LOGS, IOT
INT/EXTERNAL
STAGE/STREAM
SQL, SPARK..
UN/STRUCTURED
DATA LAKE..
CLEANING
ETL
PREPARATION
AGGREGATES
METRICS/KPI
REPORTS
ML
EDA
AI
Info Design
Narrative
Data Stories
WORKFLOWS
CHANGE MGMT
ACTIONS
Driving Data Supply Driving Data Value
Maturity Levels with Data
6. 6
Insights Output : Examples
Data as
Culture’
Data
Transformation
Consumption
MaturityPhases
“Language of Data Scientist”
8. 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. 9
CDOs Must Address Hearts & Minds to Drive Data Value
Data-driven
culture
Business
valueCDO
Credit: Gartner
14. 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
16. 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
20. 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. 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. 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. 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. 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. 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.
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EUROPEAN BREWERY IDENTIFIED €15 M COST SAVINGS AFTER CONSOLIDATING VENDORS
WATCH A 4-MINUTE VIDEOSEE LIVE DEMO
A leading European brewery’s plants purchased
commodity raw materials from several vendors each –
and had low volume discounts.
Plants also placed multiple orders placed every week,
leading to higher logistics cost.
When plant managers were shown the data, they
objected, saying “That’s not always the case.” Or,
“That’s the only way– no one else does better.”
Gramener built a custom analytics solution that
sourced their SAP order data, automatically identified
which plants ordered which commodities the most
from multiple vendors – and when.
It showed how each plant performed compared to
peers – shaming those with poor performance.
With this, they identified savings of €15 m — which
the plant managers couldn’t refute.
€15 m 40%
savings potential identified
annually
vendor based reduction
identified
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Challenges Women Face – An Interactive Narrative
Best of the Visualization
Web, Sep 2018
LINK
30. 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
31. 31
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. 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. 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
39. 39
AUTOMATE
STORYTELLING
3
Reports in plain
English with visuals
Ø Wealth reports
Ø Patient reports
Ø Loyalty point usage
Ø School report cards
NARRATIVES
Visual Insights
delivered to Inbox
Ø Customer segments
Ø Viewership shifts
Ø Geo-demographics for
geographic zones.
INFOGRAPHIC ALERTS
Engage through
emotions from Comics
Ø Price forecast
Ø Revenue forecasts
Ø Capacity utilization
Ø Viewership forecast
COMICGEN
Insights delivered as
Automated Videos
Ø Type detection
Ø Root cause drivers
Ø Factor correlation
Ø Cross-tabulation
DATA VIDEOS
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AT GRAMENER, OUR FOCUS IS ON NARRATES INSIGHTS FROM DATA AS STORIES
Stories are
memorable, viral
NUMBERS
ARE NOT
ENOUGH
STORIES EXPLAIN THEM
Delays are due to fragile
cargo. Trained staff and
forklifts reduce risk of
breakage, and hence reduce
delay.
Insights are
useful, non-
obvious, Big
FACTS ARE NOT USEFUL
E.g. Delay in cargo delivery
grew 8% last quarter.
INSIGHTS ENABLE ACTION
Lack of forklifts and fewer
trained staff led to the delay.
Improving these can reduce
cargo delay by 15%.
40
INSIGHT STORY
DATA
GRAMENER
COMBINES
These are memorable. People act on them.
They go viral. This enables collective action.
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WE HELP PEOPLE REALLY UNDERSTAND DATA – LOGICALLY, AND INTUITIVELY
41
We use technology to automate Analysis, Visuals
and Narration
INSIGHTS
Extract meaning using
automated patterns
AI & MACHINE
LEARNING SERVICES
VISUAL
NARRATIVES
STORYTELLING
Creative ThinkingCritical Reasoning
SOFTWARE
THROUGH
SERVWARE: augmenting human
intelligence with technology
STORYTELLING
Binding visuals together into a
logical story
42. 42
§ 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
43. 43
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
44. 44
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