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“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.
90%
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
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
Insights Output : Examples
Data as
Culture’
Data
Transformation
Consumption
MaturityPhases
“Language of Data Scientist”
7
Consumable Insights output : Examples
https://gramener.com/securities/
MaturityPhases
“Language of Decision Maker”
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
INSIGHT STORY
DATA
GRAMENER
COMBINES
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
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
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
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
32https://tcdata360.worldbank.org/stories/tech-entrepreneurship/
LINKGuided Storytelling with Exploration
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
Amit Kapoor, http://narrativeviz.com/playbook
37
EMBRACE THE
PROCESS
1
Data Storytelling = Insights + Narratives
38
2
Information
Designers
Data
Storytellers
Behavioral
Psychologists
EMBED DESIGN SKILLS
https://towardsdatascience.com/the-3-missing-roles-that-every-data-science-team-needs-to-hire-97154cc6c365
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
40
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.
41
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
§ 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
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
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
45
@naveengattu
Thank You!
gramener.com /naveengattu
gramener.com/solutions
Feel free to contact me at naveen.gattu@gramener.com

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Data Storytelling - Game changer for Analytics

  • 1. “War is 90% information” Napoleon Bonaparte
  • 2. 2 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.
  • 3. “50% of data science projects will never get consumed… Reference: Gartner
  • 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”
  • 7. 7 Consumable Insights output : Examples https://gramener.com/securities/ MaturityPhases “Language of Decision Maker”
  • 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
  • 11. Why Stories? Stories are | emotional Stories are | memorable Stories are | impactful
  • 12. 100+ Clients @naveengattu Naveen Gattu Co-founder & COO We bridge the DATA CONSUMPTION gap Storytelling for Analytics INSIGHT STORY DATA GRAMENER COMBINES
  • 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
  • 15. Let’s See Examples of Each My aim is to plant ideas of what’s possible
  • 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
  • 18. 18 GOOGLE SUGGEST: INDIA’S RELIGIONS LIN K
  • 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.
  • 26. 26 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
  • 27. 27 Challenges Women Face – An Interactive Narrative Best of the Visualization Web, Sep 2018 LINK
  • 28. 28 Data-Driven Comics Can be Embedded in BI Tools LINK
  • 29. 29 These were automated using Comicgen A data-comic library we developed. GRAMENER.COM / COMICGEN /
  • 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
  • 40. 40 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.
  • 41. 41 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