13. You will be shown a set of numbers
along with a summary (average, etc)
Can you make sense of the figures?
WHY VISUALISE?
14. So is the variance in sales.Variance in price is the same.
Average sales is the same too.Average price is the same.
Take a look at the sales report
alongside. A company has
branches in 4 cities, and each
branch changes the product
price every month. This leads to
a corresponding change in the
sales.
Here is the performance of the
4 branches with their monthly
price and sales for each month.
Looking at the average, the four
branches have an identical
performance.
2010 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
DO THESE FOUR CITIES LOOK IDENTICAL TO YOU?
DO YOU AGREE?
15. ARE THEY REALLY IDENTICAL? CHECK AGAIN…
But in fact, the four cities are
totally different in behaviour.
Boston’s sales has generally
increased with price.
Detroit has a nearly perfect
increase in sales with price,
except for one aberration.
Chicago shows a decline in sales
beyond a price of 10.
New York’s sales fluctuates
despite a nearly constant price.
Boston Chicago
New YorkDetroit
17. WINNING PARTIES
In the 2004 election to Lok
Sabha there were 1,351
candidates from 6 National
parties, 801 candidates from
36 State parties, 898
candidates from officially
recognised parties and 2385
Independent candidates.
The Congress (INC) won
145 seats in the 2004
elections. BJP won 138,
coming a close second.
The constituencies where
each party won is shown
here.
Party BJP BSP CPM INC RJD SP
18. Party BJP BSP CPM INC RJD SPWINNING PARTIES
It is not often easy to see
which party won the overall
elections on a map.
In the previous page, BJP (in
red), which won in
constituencies with a large
physical area (Rajasthan,
Madhya Pradesh), appeared
to have swept the elections.
This cartogram resizes the
constituencies proportional
to the number of voters, and
it’s easier to see that the
Congress (in blue) won
about as many seats as the
BJP.
20. Top 1/3rd
Next 1/3rd
Lowest 1/3rd
Sarvagnanagar: 35.7% out of 107941
(K J George, INC)
Low polling
Low polling
Hosakote: 89.3% out of 141953
What was the polling percentage?
Karnataka, Assembly Elections 2008
23. CRICKET
FASTEST SCORERS
“
I’ve always been curious… who
among India’s prolific one-day
run-getters had the best strike
rate?
Sachin?
Sehwag?
What about the rest of the world?
27. Here are all public Indian companies, grouped by Industry. The size of the
box indicates revenue (2012) and the colour indicates net profit
(red is low, green is high). Click on the group to see companies below.
28. Here are all public Indian companies, grouped by Industry. The size of the
box indicates revenue (2012) and the colour indicates net profit
(red is low, green is high). Click on the group to see companies below.
29. 68% correlation
between AUD & EUR
Plot of 6 month daily
AUD - EUR values
Block of correlated
currencies
… clustered
hierarchically
30. PRE-2009 2009 AND AFTER
Decisions to increase the number of
lanes on highways grew significantly
post-2009, especially as part of the CCI
(Cabinet Committee on Infrastructure)
decisions
A significant rise in the number of
decisions related to the States is
seen post 2009 – in contrast with
the focus on “Central” pre-2009
The number of international
agreements has declined
dramatically between pre-2009 and
post-2009
Decisions related to
intervention, assistance and relief
were almost entirely concentrated in
pre-2009
31. Adult
Educat
ion
Adminisr
ative
Reforms
Agric
ultura
l
Mark
eting
Agricul
tureAnimal
Husban
dry
Coope
rative
Excis
e
Fina
nce
Fishe
ries
Fishe
ries
&
Inlan
d
wate
r
trans
port
Food &
Civil
Supplies
Fore
st
Fuel
Haz &
Wakf
Health
and
family
welfare
Higher
Educati
on
Hom
e Horticu
lture
Hous
ing
Info
rma
tion
&
Tec
hno
logy
Kannad
a &
Culture
Labo
ur
Law
&
Hu
man
Righ
ts
Major &
Medium
Industri
es
Medical
Educatio
n
Medium
and
Large
Industrie
s
Mines
&
Geolo
gy
Minor
Irrigati
on
Muz
rai
P.W.D.
Parlia
mentar
y
Affairs
and
Human
Rights
Plan
ning
Planni
ng
and
Statist
ics
Primary
and
Secondary
Education
Primary
Educati
on
Pris
on
Pub
lic
Libr
ary
Reve
nue
Rural
Developme
nt and
Panchayat
Raj
Rural
Wate
r
Suppl
y
Rural
Water
Supply
and
Sanitat
ion
Seri
cult
ure
Smal
l
Scale
Indu
strie
s
Small
Indust
ries
Social
Welfar
e
Suga
r
Textil
e
Touri
sm
Tran
sport
Transp
ortatio
n
Urban
Develo
pment
Water
Resourc
es
Woman &
Child
Developm
ent
Youth
and
Sports
Yout
h
Servi
ce &
Spor
ts
BJP focus
JD(S)
focus
INC focus
What topics did parties focus on during questions?
Karnataka, 2008-2012
32. P.W.D.
Health and
family
welfare
Reven
ue
Rural
Developme
nt and
Panchayat
Raj
Social
Welfar
e
Urban
Develo
pment
Water
Resour
ces
Minor
Irrigati
on
Fuel
Hous
ing
Agric
ulture
Primary
Educati
on
Primary and
Secondary
Education
Woman &
Child
Developme
nt
Higher
Educati
on
Hom
eCoope
rative
Fore
st
Adminisra
tive
Reforms
Labo
ur
Food &
Civil
Supplies
Tour
ism
Fina
nce
Animal
Husba
ndry
Transpo
rtation
Hortic
ulture
Muzr
ai
Haz &
Wakf
Trans
portMedical
Educatio
n
Medium
and Large
Industries
Excis
e
Major &
Medium
Industrie
s
Kannad
a &
Culture
Text
ile
Fishe
ries
Parliam
entary
Affairs
and
Human
Rights
Adult
Educati
on
Rural
Water
Supply
and
Sanitati
on
Mines
&
Geolog
y
Small
Industr
ies
Youth
and
Sports
Suga
r
Planni
ng and
Statisti
cs
Agricul
tural
Marke
ting
Rural
Water
Supply
Fisher
ies &
Inland
water
trans
port
Small
Scale
Indus
tries
Yout
h
Servi
ce &
Sport
s
Seric
ultur
e
Law
&
Hum
an
Righ
ts
Priso
n
Plan
ning
Info
rma
tion
&
Tec
hnol
ogy
Publ
ic
Libr
ary
What topics did the young & old focus on during questions?
Karnataka, 2008-2012
Young Old
34. The only other such times were
Feb 23, 2008 (28 decisions) &
Dec 26, 2008 (23 decisions).
Nearly two-thirds of decisions
are taken on Thursday
sessions, which is also visible
on the calendar alongside.
UPA's best cabinet performance was last
Friday, with a record 23 decisions taken in a
single day, including some long pending key
reform measures.
PARLIAMENT DECISIONS (CABINET + CCEA* + CCI**)
* CCEA: Cabinet Committee on Economic Affairs
** CCI: Cabinet Committee on Infrastructure
Mon 63 5%
Tue 56 4%
Wed 105 8%
Thu 854 65%
Fri 223 17%
Sat 6 0%
35.
36. EDUCATION
PREDICTING MARKS
What determines a child’s marks?
Do girls score better than boys?
Does the choice of subject matter?
Does the medium of instruction matter?
Does community or religion matter?
Does their birthday matter?
Does the first letter of their name matter?
37. District
Gender G B
Month Sep Nov Oct Dec Aug Feb Mar Jan Apr May Jul Jun
Caste OTHERS CAT-1 ST SC
Govt False True
Medium E K U MHLT
WHAT INFLUENCES STUDENTS’ MARKS?
43. Based on the results of the 20 lakh
students taking the Class XII exams
at Tamil Nadu over the last 3
years, it appears that the month you
were born in can make a difference
of as much as 120 marks out of
1,200.
June borns
score the lowest
The marks shoot
up for Aug borns
… and peaks for
Sep-borns
120 marks out of
1200 explainable
by month of birth
An identical pattern was observed in 2009 and 2010…
… and across districts, gender, subjects, and class X & XII.
“It’s simply that in Canada the eligibility
cutoff for age-class hockey is January 1. A
boy who turns ten on January
2, then, could be playing alongside
someone who doesn’t turn ten until the
end of the year—and at that age, in
preadolescence, a twelve-month gap in
age represents an enormous difference in
physical maturity.”
-- Malcolm Gladwell, Outliers
46. We handle terabyte-size data via non-traditional analytics and visualise it in real-time.
Gramener visualises
your data
Gramener transforms your data into concise dashboards
that make your business problem & solution visually obvious.
We help you find insights quickly, based on cognitive research,
and our visualisations guide you towards actionable decisions.
A data analytics and visualisation company
47. WHAT WE OFFER
PLATFORM CUSTOM APPS SERVICES
Buy & create your
own visualisations
using our library of
visual and analytical
components
WHO’D USE THIS?
If you have a strong
analytics & technology
team, and want to
customise visualisations
based on your needs
We build your
domain-specific BI
solutions to integrate
visualisations with
your platform
WHO’D USE THIS?
If your business needs are
clear, you require regular
visual intelligence, but
prefer to outsource
development
We take your
data, analyse it, and
share insights that
you can re-create
with revised data
yourself
WHO’D USE THIS?
If your needs are unclear, or
ad-hoc, and you need a
partner to help extract
actionable insights quickly
out of existing data.
48. We handle terabyte-size data via non-traditional analytics and visualise it in real-time.
Gramener visualises
your data
Gramener transforms your data into concise dashboards
that make your business problem & solution visually obvious.
We help you find insights quickly, based on cognitive research,
and our visualisations guide you towards actionable decisions.
A data analytics and visualisation company
The earliest data visualisations were seen as far back as the mid-19th century. This is a visualisation prepared by Florence Nightingale for Queen Victoria during England’s war with France. It shows in RED the number of people that died from war wounds, in BLACK the number of people that died from other war related causes and in BLUE the number of people who died due to avoidable hospital diseases. A war is won by people and the main reason England was losing people wasn't bullets or swords but diseases. Florence Nightingale used this visualisation to request funding for hospitals, got it, and England won the war.
In 1854, London suffered from a Cholera epidemic. The popular theory at that time was that cholera was caused by pollution. Dr. John Snow was sceptical about this. By talking to local residents, he identified the source of the outbreak as the public water pump on Broad Street. Dr.Snow used this map to illustrate the cluster of cholera cases around the pump. He also used statistics to illustrate the connection between the quality of the water source and cholera cases. This visual was convincing enough to persuade the local council to disable the well pump by removing its handle,is regarded as the founding event of the science of epidemiology.
This is a map of London drawn purely using data. Every blue dot is a twitter message posted from that location. Every red dot is a photograph on Flickr taken at that location. You can see the structure of the city emerge – the roads, the river Thames, popular areas like the Tower of London, Buckingham palace, and Westminster Abbey highlighted in red, and the popular business districts highlighted in blue. This is despite not using ANY underlying map. There is nothing more here on this image, than hundreds of thousands of data points from Twitter and Flickr.
Who’s the best Indian one-daybatsman? The size represents every run ever scored. The colour represents speed. Red is slow, green is fast.Sehwag’s very fast – but so was Kapil, especially for his time.
This is a drilldown, showing every single match they played.With this, you’ll be able to see who the consistent players are, and where exactly their runs came from.You can also click to see that particular match statistics.
Gramener is a data analtyics and visualisation company.We have the ability to process data at a small and a large scale.We analyse the data to find non-intuitive insights that lie hidden behind it and present it as a visual story that makes those insights obvious in real time.
Gramener is a data analtyics and visualisation company.We have the ability to process data at a small and a large scale.We analyse the data to find non-intuitive insights that lie hidden behind it and present it as a visual story that makes those insights obvious in real time.