A DATA SCIENCE COMPANY

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.

S ANAND, GRAMENER

HOW YOU CAN GET

INSIGHTS FROM DATA

OVERCOMING COMMON OBJECTIONS ON READINESS

DATA

ANALYSIS VISUALS

INSIG

HTS REPORTS

EXPLORATION

ISEVERYWHERE

DATA

ANALYSIS VISUALS

INSIG

HTS REPORTS

EXPLORATION

ISEVERYWHERE

COMMON COMPLAINT #1

WE CAN’T DRILL INTO RAW DATA

INDIA: ODI BATTING

IMPACT OF THE BUDGET ON STOCK PRICES

INDIA’S BUDGET: FORECASTING & PLANNING

DATA

ANALYSIS VISUALS

INSIG

HTS REPORTS

EXPLORATION

ISEVERYWHERE

COMMON COMPLAINT #2

WE ALREADY USE CHARTS

TIMES NOW COVERAGE HAD

80%+ VIEWERSHIP

DATA

ANALYSIS VISUALS

INSIG

HTS REPORTS

EXPLORATION

ISEVERYWHERE

COMMON COMPLAINT #3

NOT INTEGRATED IN WORKFLOW

Portfolio Performance Visual

Worldwide$288.0mn

A: Accelerate$68.9mn

B: Build$77.2mn

C: Cut down$141.9mn

Worldwide:$288 mn UK: 87.0

Stores: 34.4

Product 9: 6.2Product 10: 5.4Product 7: 5.1Product 15: 4.8Product 8: 3.1Product 14: 2.1

Partners: 29.2Product 15: 6.7Product 17: 4.1Product 6: 3.4Product 1: 3.2Product 7: 2.9Product 11: 2.4

Direct: 23.5 Product 17: 5.2Product 8: 4.4

Product 16: 4.0Product 14: 2.5Product 1: 2.5

Japan: 71.9 Stores: 25.9 Product 14: 6.0

Product 7: 5.4Product 11: 4.0Product 17: 2.8

Partners:

25.5Pro

duct 8: 8.2

Product 1

1: 3.6

Product 1

6: 3.3

Product 1

: 3.1

Product

9: 2.0

Direct:

20.5

Produ

ct 11

: 5.2

Produ

ct 15

: 4.5

Produ

ct 14

: 2.8

Produ

ct 9:

2.3

China

: 65.6

Partn

ers: 2

7.3

Produ

ct 10

: 8.0

Produ

ct 3:

7.1

Produ

ct 15

: 3.0

Produ

ct 2:

2.1

Produ

ct 8:

2.0

Dire

ct: 19

.6

Produ

ct 3:

5.5

Produ

ct 2:

4.7

Produ

ct 8:

2.6

Produ

ct 17

: 2.1

Stor

es: 1

8.7

Prod

uct 1

0: 5

.4

Prod

uct 1

4: 2

.2

Prod

uct 7

: 2.1

Prod

uct 1

5: 2

.0

Indi

a: 4

6.6

Stor

es: 1

7.5

Prod

uct 1

6: 6

.8

Dire

ct: 1

5.6

Prod

uct 1

0: 3

.4

Prod

uct 1

6: 2

.9

Prod

uct 1

7: 2

.5

Prod

uct 7

: 2.4

Partn

ers:

13.

4Pr

oduc

t 8: 2

.5

Prod

u ct 7

: 2.3

US: 1

7.0

Partn

ers:

6.0

Prod

uct 1

0: 4

.4

Dire

ct: 5

.8Pr

oduc

t 11:

3.9

Sto r

es: 5

. 3Pr

oduc

t 11 :

3.8

The visualization shows the market opportunities across various countries to identify areas of focus. This chart has been built as an interactive-app to present the key findings, while letting user click-through and drill-down to a custom view across 4 different levels.

BANKING DASHBOARD

DATA

ANALYSIS VISUALS

INSIG

HTS REPORTS

EXPLORATION

ISEVERYWHERE

COMMON COMPLAINT #1

WE DON’T HAVE THE TOOLS

Billing fraud at an 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 slab boundaries.

Below is a simple histogram (or frequency distribution) of usage levels. Each bar represents the number of customers with a customers with a specific bill amount (in units, or KWh).

Tariffs are based on the usage slab. Someone with 101 units is billed in full at a higher tariff than someone with 100 units. So people have a strong incentive to stay at or within a slab boundary.

An energy utility (with over 50 million subscribers) had 10 years worth of customer billing data available.

Most fraud detection software failed to load the data, and sampled data revealed little or no insight.

This can happen in one of two ways.

First, people may be monitoring their usage very carefully, and turn of their lights and fans the instant their usage hits the slab boundary.

Or, more realistically, there’s probably some level of corruption involved, where customers pay a small sum to the meter reading staff to ensure that it stays exactly at the slab boundary, giving them the advantage of a lower price.

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?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

THE PATTERN IN INDIA IS QUITE DIFFERENTThis 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?

More births Fewer births … on average, for each day of the year (from 2007 to 2013)

THIS ADVERSELY IMPACTS CHILDREN’S MARKSIt’s a well established fact that older children tend to do better at school in most activities. Since many children have had their birth dates brought forward, these younger children suffer.

The average marks of children “born” on the 1st, 5th, 10th, 15th etc. of the month tend to score lower marks. • 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?

Higher marks Lower marks… on average, for children born on a given day of the year (from 2007 to 2013)

DEPLOY

MODERNTOOLS

ANALYSIS ISEVERYWHERE

COMMON COMPLAINT #1

WE DON’T HAVE THE TOOLS

COMMON COMPLAINT #2

WE DON’T GET INSIGHTS

RSASEXCELPYTHONDATABASESML SERVICES

68% correlation between AUD &

EUR

Plot of 6 month daily AUD - EUR

values

Block of correlated currencies

… clustered hierarchically

RESTAURANT: PRODUCT SALES CORRELATION

DATA

ANALYSIS VISUALS

INSIG

HTS REPORTS

EXPLORATION

ISEVERYWHERE

COMMON COMPLAINT #1

WE DON’T HAVE DATA

We have internal information. Getting

information from outside is our challenge. There’s

no way of doing that.

– Senior EditorLeading Media Company

“

India’s religions

United Kingdom’s religions

AUGMENT YOUR

DATASOURCES

DATA ISEVERYWHERE

COMMON COMPLAINT #1

WE DON’T HAVE DATACOMMON COMPLAINT #2

THE DATA ISN’T STRUCTURED

CRM DATASALES DATAPRICING DATACALL RECORDSWEB LOG DATAVENDOR INVOICESSOCIAL MEDIA DATACLICKTHROUGH DATACOMPETITOR RESEARCHCUSTOMER TRANSACTIONS…

CENSUS DATAE-COMMERCE PRICESCOMMODITY PRICESSTOCK MARKET DATAFINANCIAL REPORTINGSOCIAL MEDIA DATAMOBILE PENETRATIONAADHAR DATACOURT CASE BRIEFSSHAPE FILES…

Recruiting top quality developers is always a problem. We decided to use an algorithmic approach and pulled out the social network of developers on Github (a social network for open source code).

In this visualisation, each circle is a person. The size of the circle represents the number of followers. Larger circles have more followers (but not in proportion – it’s a log scale.)

The circle’s colour represents the city the programmer’s live in. This visual is a slice showing the tale of two cities: Bangalore and Singapore

Two people are connected if one follows the other. This leads to a clustering of people in the form of a network.

Here, you can see that Bangalore and Singapore are reasonably well connected cities. Bangalore has more developers, but Singapore has more popular ones (larger circles).

However, the interaction between Bangalore and Singapore are few and far between. But for a few people across both cities, like:

… etc.

Sudar, Yahoo!Anand C, ConsultantKiran, HasgeekAnand S, Gramener

Mugunth, Steinlogic Honcheng, buUukSau Sheong, HP LabsLim Chee Aung

Bangalore

Singapore

1 follower

100 followers

A follows B (or)

B follows A

Most followed in Bangalore

Most followed in Singapore

Ciju CherianLin JunjieAmudhi Sebastian

There are, of course, a number of smaller independent circles – people who are not connected to others in the same city. (They may be connected to people in other cities.)

Apart from this, there are a few small networks of connected people – often people within the same company or start-up – who form a community of their own.

THE SOCIAL TALE OF TWO CITIES: BANGALORE & SINGAPORE

Tata TeleservicesTata Consultancy Services

Tata Business Support ServicesTata Global BeveragesTata Infotech (merged)

Tata Toyo RadiatorHoneywell Automation India

Tata CommunicationsA G C Networks

Tata Technologies

Tata ProjectsTata PowerTata FinanceIdea CellularTata MotorsTata SonsTata SteelTayo RollsTata SecuritiesTata CoffeeTata Investment Corp

A J EngineerH H MalghamH K SethnaKeshub MahindraRavi KantRussi ModySujit Gupta

A S BamAmal GanguliD B EngineerD N GhoshM N BhagwatN N KampaniU M Rao

B MuthuramanIshaat Hussain

J J IraniN A Palkhivala

N A SoonawalaR Gopalakrishnan

Ratan TataS Ramadorai

S Ramakrishnan

DIRECTORSHIPS AT THE TATASEvery person who was a Director at the Tata Group is shown here as an orange circle. The size of the circle is based on the number of directorship positions held over their lifetime.

Every company in the Tata Group is shown here as a blue circle. The size of the circle is based on the number of directors the company has had over time.Every directorship relation is shown by a line. If a person has held a directorship position at a company, the two are connected by a line.The group appears to be divided into two clusters based on the network of directorship roles.

Prominent leadersbridge the groups

Second group of companies

First group of companies

Some directors are mainly associated with the first group of companies

Some directors are mainly associated with the second group of companiesWe’ve used network diagrams to detect terrorism, corporate fraud,

product affinities and behavioural customer segmentation

WHAT DO FINANCIAL ANALYSTS ASK IBM VS MSFT?

How does Mahabharata, one of the largest epics with 1.8 million words lend itself to text analytics?

Can this ‘unstructured data’ be processed to extract analytical insights?

What does sentiment analysis of this tome convey?

Is there a better way to explore relations between characters?

How can closeness of characters be analysed & visualized?

VISUALISING THE MAHABHARATA

DATA ISEVERYWHERE

EXTRACT THE

META DATA

AUGMENT YOUR

DATASOURCES

COMMON COMPLAINT #2

THE DATA ISN’T STRUCTURED

COMMONWHO, WHAT, WHEN, WHERETEXTTEXT KEYWORDSSENTIMENTIMAGEVISUAL RECOGNITIONAUDIO / CALLSTRANSCRIPTSMOOD ANALYSIS

THE CAPABILITIES AREIN YOUR REACH TODAY

EXPLORE THE ART OF DATA

S ANAND s.anand@gramener.comCEO, GRAMENER 9741552552