VISUALISING DATA

S Anand

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 dashboardsthat 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.

GramenerA data analytics and visualisation company

Consider an Organizational Sales report shown alongside

It shows performance of 4 branches with average price and sales across 4 cities

Each of the branches change prices every month with a corresponding change in the sales value

Basic analytics of these numbers reveal consistent performance across 4 branches.

Further, these sales figures have a consistent Correlation and Linear regression across all cities

2010 Bangalore DelhiHyderaba

dMumbai

MonthPric

eSale

sPric

eSale

sPric

eSale

sPric

eSale

s

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 13.012.7

48.0 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 19.012.5

0

Sep 12.010.8

412.0 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

WHY VISUALISE?

BECAUSE NUMBERS DON’T TELL THE FULL STORYPlotting the same data shows markedly different behaviour.

Bangalore sales has generally increased with price.

Hyderabad has a perfect increase in sales with price, except for one aberration.

Delhi, however, shows a decline in sales as price is increased beyond a certain point.

Mumbai sales fluctuated a lot despite a constant price, except for one month.

DETECTING FRAUD

“We know meter readings are incorrect, for various reasons.

We don’t, however, have the concrete proof we need to start the process of meter reading automation.

Part of our problem is the volume of data that needs to be analysed. The other is the inexperience in tools or analyses to identify such patterns.

ENERGY UTILITY

This plot shows the frequency of all meter readings from Apr-2010 to Mar-2011. An unusually large number of

readings are aligned with the tariff slab boundaries.

This clearly shows collusion of some form with the customers.

Apr-10May-10Jun-

10Jul-10Aug-10Sep-

10Oct-

10Nov-10Dec-

10Jan-

11Feb-

11Mar-

11217 219 200 200 200 200 200 200 200 350 200 200250 200 200 200 201 200 200 200 250 200 200 150250 150 150 200 200 200 200 200 200 200 200 150150 200 200 200 200 200 200 200 200 200 200 50200 200 200 150 180 150 50 100 50 70 100 100100 100 100 100 100 100 100 100 100 100 110 100100 150 123 123 50 100 50 100 100 100 100 100

0 111 100 100 100 100 100 100 100 100 50 500 100 27 100 50 100 100 100 100 100 70 1001 1 1 100 99 50 100 100 100 100 100 100

This happens with specific customers, not randomly. Here are such customers’ meter readings.

Section Apr-10May-10Jun-10Jul-10

Aug-10

Sep-10

Oct-10Nov-10

Dec-10

Jan-11

Feb-11

Mar-11

Section 1 70% 97% 136% 65% 110% 116% 121% 107% 114% 88% 74% 109%Section 2 66% 92% 66% 87% 70% 64% 63% 50% 58% 38% 41% 54%Section 3 90% 46% 47% 43% 28% 31% 50% 32% 19% 38% 8% 34%Section 4 44% 24% 36% 39% 21% 18% 24% 49% 56% 44% 31% 14%Section 5 4% 63% -27% 20% 41% 82% 26% 34% 43% 2% 37% 15%Section 6 18% 23% 30% 21% 28% 33% 39% 41% 39% 18% 0% 33%Section 7 36% 51% 33% 33% 27% 35% 10% 39% 12% 5% 15% 14%Section 8 22% 21% 28% 12% 24% 27% 10% 31% 13% 11% 22% 17%Section 9 19% 35% 14% 9% 16% 32% 37% 12% 9% 5% -3% 11%

If we define the “extent of fraud” as the percentage excess of the 100 unitmeter reading, the value varies considerably across sections, and time

New section manager arrives

… and is transferred

out

… with some explainable anamolies.

Why would these

happen?

CONTRACTFARMING

MONITORING COSTS

“Our raw material cost varies considerably across farms, though we share best practices.

We have over 5,000 farms. The raw material cost report is a 75-page Excel report that no one reads.

Also, we gain no insights as to how the productivity changes over time

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?

 

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

FINDING PATTERNS

Which securities move together?

How should I diversify?

What should I sell to reduce risk?

What’s a reliable predictor of a security?

SECURITIES

68% correlation between AUD &

EUR

Plot of 6 month daily AUD - EUR

values

Block of correlated currencies

… clustered hierarchically

… that move counter-cyclically

to indices

EDUCATION

VISUALISING CHANGE

WEATHER

What was the weather in India like…

THE LAST 100 YEARS?

VIDEO

http://youtu.be/WT0Aq41BaOQ

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