[Applause] my Marge was in the middle of a
my Marge was in the middle of a
my Marge was in the middle of a nationwide census its first one in 30
nationwide census its first one in 30
nationwide census its first one in 30 years suddenly the advanced technology
years suddenly the advanced technology
years suddenly the advanced technology backbone of the entire census completely
backbone of the entire census completely
backbone of the entire census completely failed any guesses
failed any guesses
failed any guesses this technology pencils the country had
this technology pencils the country had
this technology pencils the country had run out of pencils an emergency
run out of pencils an emergency
run out of pencils an emergency international procurement of 170,000
international procurement of 170,000
international procurement of 170,000 pencils was made an entire workforce was
pencils was made an entire workforce was
pencils was made an entire workforce was hired just that they could sharpen
hired just that they could sharpen
hired just that they could sharpen pencils fast enough so that the census
pencils fast enough so that the census
pencils fast enough so that the census could be completed on time and when the
could be completed on time and when the
could be completed on time and when the data from the Census finally came in the
data from the Census finally came in the
data from the Census finally came in the country was in for a big shock mine ma
country was in for a big shock mine ma
country was in for a big shock mine ma had previously thought its population
had previously thought its population
had previously thought its population with 60 million people when the data
with 60 million people when the data
with 60 million people when the data came in turned out there were only 51
came in turned out there were only 51
came in turned out there were only 51 million people in Myanmar the government
million people in Myanmar the government
million people in Myanmar the government had been planning every budget every
had been planning every budget every
had been planning every budget every intervention for 9 million people 15
intervention for 9 million people 15
intervention for 9 million people 15 percent of its population that just did
percent of its population that just did
percent of its population that just did not exist sounds like a story from the
not exist sounds like a story from the
not exist sounds like a story from the early 1900’s right after all today we
early 1900’s right after all today we
early 1900’s right after all today we live in the world of unprecedented big
live in the world of unprecedented big
live in the world of unprecedented big data the world of Facebook Twitter
data the world of Facebook Twitter
data the world of Facebook Twitter Google this was the year 2014 2014 the
Google this was the year 2014 2014 the
Google this was the year 2014 2014 the same year the Germany use big data to
same year the Germany use big data to
same year the Germany use big data to win the FIFA World Cup it’s crazy how
win the FIFA World Cup it’s crazy how
win the FIFA World Cup it’s crazy how big data is used today to solve some
big data is used today to solve some
big data is used today to solve some kinds of problems and not others as a
kinds of problems and not others as a
kinds of problems and not others as a college student in Singapore I interned
college student in Singapore I interned
college student in Singapore I interned at Goldman Sachs where I saw how
at Goldman Sachs where I saw how
at Goldman Sachs where I saw how real-time data was used every second to
real-time data was used every second to
real-time data was used every second to make both important and not so important
make both important and not so important
make both important and not so important decisions in the finance world but
decisions in the finance world but
decisions in the finance world but whatever other was worth like health
whatever other was worth like health
whatever other was worth like health care education environment crime data
care education environment crime data
care education environment crime data just isn’t used in the same way and so
just isn’t used in the same way and so
just isn’t used in the same way and so my co-founder Warren and I founded
my co-founder Warren and I founded
my co-founder Warren and I founded social cops to empower our world leaders
social cops to empower our world leaders
social cops to empower our world leaders with the information that they need in
with the information that they need in
with the information that they need in real time to make some of the most
real time to make some of the most
real time to make some of the most complex decisions facing our world we
complex decisions facing our world we
complex decisions facing our world we move to India after we graduated and
move to India after we graduated and
move to India after we graduated and spent an entire you’re doing pilots to
spent an entire you’re doing pilots to
spent an entire you’re doing pilots to try and understand
try and understand
try and understand it was so easy to use data and football
it was so easy to use data and football
it was so easy to use data and football but not in decisions that mattered we
but not in decisions that mattered we
but not in decisions that mattered we found that the single biggest bottleneck
found that the single biggest bottleneck
found that the single biggest bottleneck was the data itself in most places in
was the data itself in most places in
was the data itself in most places in the developing world data is still
the developing world data is still
the developing world data is still recorded on registers like these most of
recorded on registers like these most of
recorded on registers like these most of this data will never make it to a
this data will never make it to a
this data will never make it to a digital format even if this data does
digital format even if this data does
digital format even if this data does make a to a digital format
make a to a digital format
make a to a digital format it’s often thought like this
it’s often thought like this
it’s often thought like this unstructured local language impossible
unstructured local language impossible
unstructured local language impossible to extract and even if the data somehow
to extract and even if the data somehow
to extract and even if the data somehow does make it to a consumable format it’s
does make it to a consumable format it’s
does make it to a consumable format it’s often stored in siloed disconnected
often stored in siloed disconnected
often stored in siloed disconnected systems that just never talk to each
systems that just never talk to each
systems that just never talk to each other for example the district of Nagpur
other for example the district of Nagpur
other for example the district of Nagpur alone in Maharashtra has 32 different
alone in Maharashtra has 32 different
alone in Maharashtra has 32 different health data systems and just when you
health data systems and just when you
health data systems and just when you think that it can’t get worse it does
think that it can’t get worse it does
think that it can’t get worse it does the first time that we match two
the first time that we match two
the first time that we match two datasets at a village level in India
datasets at a village level in India
datasets at a village level in India imagine this table with village names
imagine this table with village names
imagine this table with village names table with village names join the two
table with village names join the two
table with village names join the two tables sounds really simple right we got
tables sounds really simple right we got
tables sounds really simple right we got a 15 percent match why because in a
a 15 percent match why because in a
a 15 percent match why because in a country like India one data set would
country like India one data set would
country like India one data set would call Mumbai Mumbai the other Bombay one
call Mumbai Mumbai the other Bombay one
call Mumbai Mumbai the other Bombay one data set would spell pomper now with OB
data set would spell pomper now with OB
data set would spell pomper now with OB the other pompona with appeal matching
the other pompona with appeal matching
the other pompona with appeal matching these data sets together is a complete
these data sets together is a complete
these data sets together is a complete nightmare it was as a search to a
nightmare it was as a search to a
nightmare it was as a search to a solution to some of these problems that
solution to some of these problems that
solution to some of these problems that a data Intelligence Platform at social
a data Intelligence Platform at social
a data Intelligence Platform at social Cox was born we believe that if we could
Cox was born we believe that if we could
Cox was born we believe that if we could somehow bring together all of these data
somehow bring together all of these data
somehow bring together all of these data sets and showcase it to decision-makers
sets and showcase it to decision-makers
sets and showcase it to decision-makers in an easy intuitive fashion it would
in an easy intuitive fashion it would
in an easy intuitive fashion it would help them make more accurate data-driven
help them make more accurate data-driven
help them make more accurate data-driven decisions now well as an organization
decisions now well as an organization
decisions now well as an organization our focus is to empower leaders and
our focus is to empower leaders and
our focus is to empower leaders and decision-makers behind each of these
decision-makers behind each of these
decision-makers behind each of these decisions each of these data points are
decisions each of these data points are
decisions each of these data points are people people whom we may somehow
people people whom we may somehow
people people whom we may somehow sometimes never know people like Sunita
sometimes never know people like Sunita
sometimes never know people like Sunita Sunita is a 35 year old woman mother
Sunita is a 35 year old woman mother
Sunita is a 35 year old woman mother go cooks for her children morning and
go cooks for her children morning and
go cooks for her children morning and night her equipment this this is a Chula
night her equipment this this is a Chula
night her equipment this this is a Chula traditional firewood that’s used in many
traditional firewood that’s used in many
traditional firewood that’s used in many parts of rural India cooking on a Chula
parts of rural India cooking on a Chula
parts of rural India cooking on a Chula is equal to smoking 400 cigarettes and
is equal to smoking 400 cigarettes and
is equal to smoking 400 cigarettes and other but the only alternative for
other but the only alternative for
other but the only alternative for Sunita is to leave her remote tribal
Sunita is to leave her remote tribal
Sunita is to leave her remote tribal village walk 20 kilometres to the
village walk 20 kilometres to the
village walk 20 kilometres to the nearest LPG gas centre pick up a 14 kilo
nearest LPG gas centre pick up a 14 kilo
nearest LPG gas centre pick up a 14 kilo gas cylinder put it on her head and walk
gas cylinder put it on her head and walk
gas cylinder put it on her head and walk back to her home it was in order to
back to her home it was in order to
back to her home it was in order to ensure that Sunita and millions of women
ensure that Sunita and millions of women
ensure that Sunita and millions of women like her get easy access to clean
like her get easy access to clean
like her get easy access to clean cooking fuel that the Ministry of
cooking fuel that the Ministry of
cooking fuel that the Ministry of Petroleum and the three oil companies in
Petroleum and the three oil companies in
Petroleum and the three oil companies in India decided to open the next 10,000
India decided to open the next 10,000
India decided to open the next 10,000 LPG centers to identify where these LPG
LPG centers to identify where these LPG
LPG centers to identify where these LPG centers should be opened they partnered
centers should be opened they partnered
centers should be opened they partnered with us now we have to consider several
with us now we have to consider several
with us now we have to consider several things on one side these LPG centers
things on one side these LPG centers
things on one side these LPG centers were operated by a small entrepreneurs
were operated by a small entrepreneurs
were operated by a small entrepreneurs and so they needed to be profitable and
and so they needed to be profitable and
and so they needed to be profitable and sustainable on the other hand we needed
sustainable on the other hand we needed
sustainable on the other hand we needed to ensure that every one of the 1.2
to ensure that every one of the 1.2
to ensure that every one of the 1.2 billion people living in India get
billion people living in India get
billion people living in India get access to an LPG Center within 10
access to an LPG Center within 10
access to an LPG Center within 10 kilometers of their homes now usually
kilometers of their homes now usually
kilometers of their homes now usually this decision would take over two years
this decision would take over two years
this decision would take over two years involve thousands of field officers and
involve thousands of field officers and
involve thousands of field officers and hands on maps like these we wanted to
hands on maps like these we wanted to
hands on maps like these we wanted to transform the way this decision was made
transform the way this decision was made
transform the way this decision was made through a data Intelligence Platform we
through a data Intelligence Platform we
through a data Intelligence Platform we started by bringing together the data
started by bringing together the data
started by bringing together the data that we already had we partnered with
that we already had we partnered with
that we already had we partnered with the oil companies and pulled together
the oil companies and pulled together
the oil companies and pulled together data from scattered internal data
data from scattered internal data
data from scattered internal data systems about sales and profitability
systems about sales and profitability
systems about sales and profitability and supply demand a number of consumers
and supply demand a number of consumers
and supply demand a number of consumers but typical market data would only care
but typical market data would only care
but typical market data would only care about profitability it would never put
about profitability it would never put
about profitability it would never put an IP G center near Sunita’s home and so
an IP G center near Sunita’s home and so
an IP G center near Sunita’s home and so we mapped every one of India six hundred
we mapped every one of India six hundred
we mapped every one of India six hundred and forty thousand villages every dot on
and forty thousand villages every dot on
and forty thousand villages every dot on the screen is a village in India we
the screen is a village in India we
the screen is a village in India we project together with six hundred
project together with six hundred
project together with six hundred external data sources about population
external data sources about population
external data sources about population as
as
as infrastructure LPG penetration and just
infrastructure LPG penetration and just
infrastructure LPG penetration and just when we thought that we had all the data
when we thought that we had all the data
when we thought that we had all the data we needed we realized we didn’t we
we needed we realized we didn’t we
we needed we realized we didn’t we didn’t actually know the exact locations
didn’t actually know the exact locations
didn’t actually know the exact locations of the existing LPG centers and so
of the existing LPG centers and so
of the existing LPG centers and so within a month seventeen thousand
within a month seventeen thousand
within a month seventeen thousand distributors across India downloaded our
distributors across India downloaded our
distributors across India downloaded our Android app submitted their location
Android app submitted their location
Android app submitted their location coordinates and map every LPG Center in
coordinates and map every LPG Center in
coordinates and map every LPG Center in the country and with that we finally had
the country and with that we finally had
the country and with that we finally had all the data that we needed but it
all the data that we needed but it
all the data that we needed but it wasn’t enough now we actually had to
wasn’t enough now we actually had to
wasn’t enough now we actually had to make these data sets talk to each other
make these data sets talk to each other
make these data sets talk to each other this is where the intelligence of a
this is where the intelligence of a
this is where the intelligence of a platform kicked in so it knows that Palm
platform kicked in so it knows that Palm
platform kicked in so it knows that Palm Bay changes name to Mumbai in 1995 and
Bay changes name to Mumbai in 1995 and
Bay changes name to Mumbai in 1995 and pompona is often spiritus you know
pompona is often spiritus you know
pompona is often spiritus you know pumper now by many people using all
pumper now by many people using all
pumper now by many people using all these intelligent algorithms we were
these intelligent algorithms we were
these intelligent algorithms we were finally able to bring all of this data
finally able to bring all of this data
finally able to bring all of this data together and make a talk to each other
together and make a talk to each other
together and make a talk to each other and now the last step do we actually
and now the last step do we actually
and now the last step do we actually open their Fiji centers I’m zooming into
open their Fiji centers I’m zooming into
open their Fiji centers I’m zooming into the district’s under guard one of 640 in
the district’s under guard one of 640 in
the district’s under guard one of 640 in India the one that Sunita lives in these
India the one that Sunita lives in these
India the one that Sunita lives in these are all the LPG distribution centers in
are all the LPG distribution centers in
are all the LPG distribution centers in the district these are all the villages
the district these are all the villages
the district these are all the villages in Sondergaard now some of these
in Sondergaard now some of these
in Sondergaard now some of these villages already have access to LPG
villages already have access to LPG
villages already have access to LPG centers these are the ones that we need
centers these are the ones that we need
centers these are the ones that we need to provide access to now where is the
to provide access to now where is the
to provide access to now where is the ideal LPG center ideally a place that is
ideal LPG center ideally a place that is
ideal LPG center ideally a place that is centrally accessible a marketplace that
centrally accessible a marketplace that
centrally accessible a marketplace that Sunita already travels to it should have
Sunita already travels to it should have
Sunita already travels to it should have maybe a bank or an ATM good electricity
maybe a bank or an ATM good electricity
maybe a bank or an ATM good electricity good infrastructure a good road so that
good infrastructure a good road so that
good infrastructure a good road so that the truck could easily go in and out it
the truck could easily go in and out it
the truck could easily go in and out it was all of these factors that our
was all of these factors that our
was all of these factors that our algorithm used to compute volumes of
algorithm used to compute volumes of
algorithm used to compute volumes of data and pinpoint the exact final
data and pinpoint the exact final
data and pinpoint the exact final locations to open LPG centers and that’s
locations to open LPG centers and that’s
locations to open LPG centers and that’s how a Center was commissioned in the
how a Center was commissioned in the
how a Center was commissioned in the village guru India just four kilometers
village guru India just four kilometers
village guru India just four kilometers away from Sunita house
this is a picture Sunita when she got a
this is a picture Sunita when she got a TV in her house for the first time she
TV in her house for the first time she
TV in her house for the first time she doesn’t need to use a jeweler anymore
for the past three years at social cops
for the past three years at social cops we’ve had a chance to leverage our data
we’ve had a chance to leverage our data
we’ve had a chance to leverage our data intelligence platform and transform the
intelligence platform and transform the
intelligence platform and transform the way organizations make decisions from
way organizations make decisions from
way organizations make decisions from anvil strategic plans to real-time
anvil strategic plans to real-time
anvil strategic plans to real-time course corrections today a Union
course corrections today a Union
course corrections today a Union Minister is using a platform to track a
Minister is using a platform to track a
Minister is using a platform to track a national program every day every morning
national program every day every morning
national program every day every morning on his tablet an FMCG company is
on his tablet an FMCG company is
on his tablet an FMCG company is prioritizing 2,000 locations out of
prioritizing 2,000 locations out of
prioritizing 2,000 locations out of 200,000 to run an ice cream campaign a
200,000 to run an ice cream campaign a
200,000 to run an ice cream campaign a philanthropic foundation is targeting an
philanthropic foundation is targeting an
philanthropic foundation is targeting an eight million dollar investment in
eight million dollar investment in
eight million dollar investment in agriculture and district collectors
agriculture and district collectors
agriculture and district collectors across India supported by the Tata
across India supported by the Tata
across India supported by the Tata trusts are targeting government benefits
trusts are targeting government benefits
trusts are targeting government benefits to citizens who need them the most
to citizens who need them the most
to citizens who need them the most imagine a world a world where we can
imagine a world a world where we can
imagine a world a world where we can capture criminals at the scene of crime
capture criminals at the scene of crime
capture criminals at the scene of crime not yours later a world where we could
not yours later a world where we could
not yours later a world where we could predict if a child is going to drop out
predict if a child is going to drop out
predict if a child is going to drop out of school before even she knows it a
of school before even she knows it a
of school before even she knows it a world where we can reroute cars in real
world where we can reroute cars in real
world where we can reroute cars in real time to prevent the traffic congestion
time to prevent the traffic congestion
time to prevent the traffic congestion that we all hate so much a world where
that we all hate so much a world where
that we all hate so much a world where we can eradicate a disease as it breaks
we can eradicate a disease as it breaks
we can eradicate a disease as it breaks out not thousands of deaths later a
out not thousands of deaths later a
out not thousands of deaths later a world where data is not just used to
world where data is not just used to
world where data is not just used to incorrectly predict the next president
incorrectly predict the next president
incorrectly predict the next president but where we use data to truly improve
but where we use data to truly improve
but where we use data to truly improve the lives of billions of people thank
the lives of billions of people thank
the lives of billions of people thank you
”
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