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The Future of Data: Predicting & Preventing Emergencies | Michael Martin | TEDxWilmington


Denyce Amberly was a 21 year old mother

Denyce Amberly was a 21 year old mother of two living in South Florida when she

of two living in South Florida when she

of two living in South Florida when she was kidnapped from her home over the

was kidnapped from her home over the

was kidnapped from her home over the course of the next six hours five

course of the next six hours five

course of the next six hours five separate 9-1-1 calls were made including

separate 9-1-1 calls were made including

separate 9-1-1 calls were made including remarkably one by miss lee herself when

remarkably one by miss lee herself when

remarkably one by miss lee herself when she was tied up in the back of her

she was tied up in the back of her

she was tied up in the back of her captors green Camaro and yet on that

captors green Camaro and yet on that

captors green Camaro and yet on that call she pleads for life 17 times and

call she pleads for life 17 times and

call she pleads for life 17 times and yet no one could locate her and

yet no one could locate her and

yet no one could locate her and ultimately this was a story that ended

ultimately this was a story that ended

ultimately this was a story that ended in tragedy every day across the United

in tragedy every day across the United

in tragedy every day across the United States 650,000 Americans call 9-1-1

States 650,000 Americans call 9-1-1

States 650,000 Americans call 9-1-1 facing some of the most challenging

facing some of the most challenging

facing some of the most challenging circumstances of their lives most of

circumstances of their lives most of

circumstances of their lives most of those calls transverse and

those calls transverse and

those calls transverse and infrastructure dating back to the 1960s

infrastructure dating back to the 1960s

infrastructure dating back to the 1960s and 1970s

and 1970s

and 1970s that accommodates almost no data indeed

that accommodates almost no data indeed

that accommodates almost no data indeed the information flow into most 911

the information flow into most 911

the information flow into most 911 networks is limited to 512 bytes of data

networks is limited to 512 bytes of data

networks is limited to 512 bytes of data to put that in perspective this blank

to put that in perspective this blank

to put that in perspective this blank screen behind me has over 30,000 bytes

screen behind me has over 30,000 bytes

screen behind me has over 30,000 bytes of data the first transatlantic cable in

of data the first transatlantic cable in

of data the first transatlantic cable in 1858 between the Queen of England and

1858 between the Queen of England and

1858 between the Queen of England and President Buchanan had 617 bytes of data

President Buchanan had 617 bytes of data

President Buchanan had 617 bytes of data it is amazing in 2017 the technology

it is amazing in 2017 the technology

it is amazing in 2017 the technology that we carry around in our pockets

that we carry around in our pockets

that we carry around in our pockets today precise location real-time helpful

today precise location real-time helpful

today precise location real-time helpful medical data voice texting videos all

medical data voice texting videos all

medical data voice texting videos all these capabilities except when our life

these capabilities except when our life

these capabilities except when our life is on the line and then almost every

is on the line and then almost every

is on the line and then almost every single one of them gets turned off and

single one of them gets turned off and

single one of them gets turned off and we move back in time 150 years the FCC

we move back in time 150 years the FCC

we move back in time 150 years the FCC estimates if we could just get more

estimates if we could just get more

estimates if we could just get more precise location over better

precise location over better

precise location over better infrastructure we could save over 10,000

infrastructure we could save over 10,000

infrastructure we could save over 10,000 lives a year now it is remarkable how

lives a year now it is remarkable how

lives a year now it is remarkable how well 9-1-1 works

well 9-1-1 works

well 9-1-1 works today in light of this infrastructure

today in light of this infrastructure

today in light of this infrastructure and it truly is a testament of the

and it truly is a testament of the

and it truly is a testament of the extraordinary people that have dedicated

extraordinary people that have dedicated

extraordinary people that have dedicated their lives towards helping us in our

their lives towards helping us in our

their lives towards helping us in our times of greatest need working 12-hour

times of greatest need working 12-hour

times of greatest need working 12-hour shifts despite dated infrastructure

shifts despite dated infrastructure

shifts despite dated infrastructure infrastructure lack of funding old

infrastructure lack of funding old

infrastructure lack of funding old technology they successfully managed

technology they successfully managed

technology they successfully managed over 240 million emergencies every

over 240 million emergencies every

over 240 million emergencies every single year four years ago I would

single year four years ago I would

single year four years ago I would become one of those emergencies walking

become one of those emergencies walking

become one of those emergencies walking home late one night in Spanish Harlem in

home late one night in Spanish Harlem in

home late one night in Spanish Harlem in that moment I realize just how difficult

that moment I realize just how difficult

that moment I realize just how difficult it can be to get out your cell phone

it can be to get out your cell phone

it can be to get out your cell phone dial a number and have a coherent

dial a number and have a coherent

dial a number and have a coherent conversation about where you’re located

conversation about where you’re located

conversation about where you’re located what’s occurring and the help that you

what’s occurring and the help that you

what’s occurring and the help that you need at the time I was working in

need at the time I was working in

need at the time I was working in venture capital so I thought this is a

venture capital so I thought this is a

venture capital so I thought this is a challenge that technology can solve so I

challenge that technology can solve so I

challenge that technology can solve so I headed off to Harvard University for

headed off to Harvard University for

headed off to Harvard University for grad school to work on that challenge in

grad school to work on that challenge in

grad school to work on that challenge in Cambridge I met Nick Horlick who was

Cambridge I met Nick Horlick who was

Cambridge I met Nick Horlick who was completing his PhD in nuclear

completing his PhD in nuclear

completing his PhD in nuclear engineering at MIT and it had some

engineering at MIT and it had some

engineering at MIT and it had some similar experiences as me so over an

similar experiences as me so over an

similar experiences as me so over an orange juice at Dunkin Donuts we put out

orange juice at Dunkin Donuts we put out

orange juice at Dunkin Donuts we put out a plan on how we were gonna solve this

a plan on how we were gonna solve this

a plan on how we were gonna solve this challenge we were too naive computer

challenge we were too naive computer

challenge we were too naive computer nerds and it was with that naive

nerds and it was with that naive

nerds and it was with that naive optimism that I headed out to my first

optimism that I headed out to my first

optimism that I headed out to my first time in one dispatch center in rural

time in one dispatch center in rural

time in one dispatch center in rural Massachusetts and that day would forever

Massachusetts and that day would forever

Massachusetts and that day would forever change how I thought about this

change how I thought about this

change how I thought about this challenge

challenge

challenge I was listening in on my second call

I was listening in on my second call

I was listening in on my second call when this mother called hysterical her

when this mother called hysterical her

when this mother called hysterical her son had just committed suicide and as

son had just committed suicide and as

son had just committed suicide and as she screamed for an ambulance I had to

she screamed for an ambulance I had to

she screamed for an ambulance I had to take off my headset and walk outside

take off my headset and walk outside

take off my headset and walk outside into the calm spring morning as I

into the calm spring morning as I

into the calm spring morning as I collected myself that nine one one call

collected myself that nine one one call

collected myself that nine one one call taker stayed on the phone with that

taker stayed on the phone with that

taker stayed on the phone with that mother for the next 12 minutes as she

mother for the next 12 minutes as she

mother for the next 12 minutes as she successfully figured out her location

successfully figured out her location

successfully figured out her location dispatched an ambulance and stayed on

dispatched an ambulance and stayed on

dispatched an ambulance and stayed on the phone until it arrived this is not

the phone until it arrived this is not

the phone until it arrived this is not like any other challenge this is the

like any other challenge this is the

like any other challenge this is the most intense experience that most of us

most intense experience that most of us

most intense experience that most of us will ever experience in our lives

will ever experience in our lives

will ever experience in our lives repeated 650,000 times

repeated 650,000 times

repeated 650,000 times day I remember driving home and calling

day I remember driving home and calling

day I remember driving home and calling up my dad and saying I need to borrow

up my dad and saying I need to borrow

up my dad and saying I need to borrow your Prius for the entire summer because

your Prius for the entire summer because

your Prius for the entire summer because I need to go meet as many 9-1-1 centers

I need to go meet as many 9-1-1 centers

I need to go meet as many 9-1-1 centers as possible to actually understand this

as possible to actually understand this

as possible to actually understand this challenge so that summer in grad school

challenge so that summer in grad school

challenge so that summer in grad school I would drive over 3,500 miles meeting

I would drive over 3,500 miles meeting

I would drive over 3,500 miles meeting with hundreds of 9-1-1 centers across

with hundreds of 9-1-1 centers across

with hundreds of 9-1-1 centers across the country again and again this

the country again and again this

the country again and again this industry opened their arms and answered

industry opened their arms and answered

industry opened their arms and answered a thousand ignorant questions about how

a thousand ignorant questions about how

a thousand ignorant questions about how data float into their systems how they

data float into their systems how they

data float into their systems how they managed these extremely intense moments

managed these extremely intense moments

managed these extremely intense moments down to precision each night I call back

down to precision each night I call back

down to precision each night I call back Nick and repeat back my findings and

Nick and repeat back my findings and

Nick and repeat back my findings and working with a growing engineering team

working with a growing engineering team

working with a growing engineering team out of the Harvard Innovation Lab we

out of the Harvard Innovation Lab we

out of the Harvard Innovation Lab we iterate on new technology new

iterate on new technology new

iterate on new technology new architecture and we bring it out to my

architecture and we bring it out to my

architecture and we bring it out to my next meetings for testing ultimately

next meetings for testing ultimately

next meetings for testing ultimately we’d interact with over 4,000 first

we’d interact with over 4,000 first

we’d interact with over 4,000 first responders who would beta test this

responders who would beta test this

responders who would beta test this technology and along the way we learn

technology and along the way we learn

technology and along the way we learn just how complicated this system is that

just how complicated this system is that

just how complicated this system is that came together over the last 50 years a

came together over the last 50 years a

came together over the last 50 years a system where you have over 6,000

system where you have over 6,000

system where you have over 6,000 different 9-1-1 centres running 25,000

different 9-1-1 centres running 25,000

different 9-1-1 centres running 25,000 different software systems talking to

different software systems talking to

different software systems talking to 60,000 first responder agencies over

60,000 first responder agencies over

60,000 first responder agencies over five million first responders in the

five million first responders in the

five million first responders in the United States we learned we had to get

United States we learned we had to get

United States we learned we had to get data exactly in the right place at the

data exactly in the right place at the

data exactly in the right place at the right time or else we could actually do

right time or else we could actually do

right time or else we could actually do more harm than good in the process the

more harm than good in the process the

more harm than good in the process the end result after four years is this

end result after four years is this

end result after four years is this transformative platform where we can

transformative platform where we can

transformative platform where we can ultimately ingest data from any

ultimately ingest data from any

ultimately ingest data from any connected device and pass it directly

connected device and pass it directly

connected device and pass it directly into the hands of 911 and first

into the hands of 911 and first

into the hands of 911 and first responders wearables powered by this

responders wearables powered by this

responders wearables powered by this technology now transmitting real-time

technology now transmitting real-time

technology now transmitting real-time health and medical data during a heart

health and medical data during a heart

health and medical data during a heart attack connected cars in a crash

attack connected cars in a crash

attack connected cars in a crash transmit their precise location how many

transmit their precise location how many

transmit their precise location how many people are in the vehicle airbag

people are in the vehicle airbag

people are in the vehicle airbag deployments rollover velocity at impact

deployments rollover velocity at impact

deployments rollover velocity at impact schools equipped with this technology

schools equipped with this technology

schools equipped with this technology now stream real-time video during an

now stream real-time video during an

now stream real-time video during an active shooter event one analysis we did

active shooter event one analysis we did

active shooter event one analysis we did with experts at Harvard and MIT

with experts at Harvard and MIT

with experts at Harvard and MIT estimated between one to seven minutes

estimated between one to seven minutes

estimated between one to seven minutes faster response time a 2 to 10 percent

faster response time a 2 to 10 percent

faster response time a 2 to 10 percent reduction in mortality rates over 10,000

reduction in mortality rates over 10,000

reduction in mortality rates over 10,000 potential lives saved every single year

potential lives saved every single year

potential lives saved every single year in the United States but what became

in the United States but what became

in the United States but what became really interesting is not only how the

really interesting is not only how the

really interesting is not only how the data itself would impact

data itself would impact

data itself would impact individual incident but how at a macro

individual incident but how at a macro

individual incident but how at a macro scale we could now start to understand

scale we could now start to understand

scale we could now start to understand patterns and trends in the data behind

patterns and trends in the data behind

patterns and trends in the data behind me is one snapshot of emergency calls

me is one snapshot of emergency calls

me is one snapshot of emergency calls through our platform during a few weeks

through our platform during a few weeks

through our platform during a few weeks in June of this year

in June of this year

in June of this year the Department of Transportation

the Department of Transportation

the Department of Transportation estimates that over 90 percent of car

estimates that over 90 percent of car

estimates that over 90 percent of car accidents are preventable we all know if

accidents are preventable we all know if

accidents are preventable we all know if it’s snowing out that we’re going to

it’s snowing out that we’re going to

it’s snowing out that we’re going to have more car accidents but because

have more car accidents but because

have more car accidents but because historically we don’t know exactly when

historically we don’t know exactly when

historically we don’t know exactly when and where those car accidents occur we

and where those car accidents occur we

and where those car accidents occur we couldn’t really statistically relate

couldn’t really statistically relate

couldn’t really statistically relate them to contributing factors like road

them to contributing factors like road

them to contributing factors like road conditions weather event data traffic

conditions weather event data traffic

conditions weather event data traffic flow patterns working with a teen of

flow patterns working with a teen of

flow patterns working with a teen of data scientist out of Harvard we started

data scientist out of Harvard we started

data scientist out of Harvard we started to study this data and build predictive

to study this data and build predictive

to study this data and build predictive models and simulations this is one

models and simulations this is one

models and simulations this is one example of winter storm Jonas and how we

example of winter storm Jonas and how we

example of winter storm Jonas and how we predict emergency calls would have came

predict emergency calls would have came

predict emergency calls would have came out during that event so for the past

out during that event so for the past

out during that event so for the past hundred years we’ve thought about

hundred years we’ve thought about

hundred years we’ve thought about emergencies in one way they happen and

emergencies in one way they happen and

emergencies in one way they happen and when they do you call for help you call

when they do you call for help you call

when they do you call for help you call your neighbor you call the bucket

your neighbor you call the bucket

your neighbor you call the bucket brigade you call 9-1-1 but thanks to an

brigade you call 9-1-1 but thanks to an

brigade you call 9-1-1 but thanks to an extraordinary community of people that

extraordinary community of people that

extraordinary community of people that have come together over the last four

have come together over the last four

have come together over the last four years that paradigm is about to forever

years that paradigm is about to forever

years that paradigm is about to forever shift from a world of emergency response

shift from a world of emergency response

shift from a world of emergency response to one of prediction and preemption

to one of prediction and preemption

to one of prediction and preemption thank you

thank you

thank you [Applause]

”

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