in the middle of the night,

and he comes across a guy under a street lamp

who's looking at the ground and moving from side to side,

and the cop asks him what he's doing.

The guys says he's looking for his keys.

So the cop takes his time and looks over

and kind of makes a little matrix and looks

for about two, three minutes. No keys.

The cop says, "Are you sure? Hey buddy,

are you sure you lost your keys here?"

And the guy says, "No, no, actually I lost them

down at the other end of the street,

but the light is better here."

There's a concept that people talk about nowadays

called big data, and what they're talking about

is all of the information that we're generating

through our interaction with and over the Internet,

everything from Facebook and Twitter

to music downloads, movies, streaming, all this kind of stuff,

the live streaming of TED.

And the folks who work with big data, for them,

they talk about that their biggest problem is

we have so much information,

the biggest problem is, how do we organize all that information?

I can tell you that working in global health,

that is not our biggest problem.

Because for us, even though the light

is better on the Internet,

the data that would help us solve the problems

we're trying to solve is not actually present on the Internet.

So we don't know, for example, how many people

right now are being affected by disasters

or by conflict situations.

We don't know for really basically any of the clinics

in the developing world, which ones have medicines

and which ones don't.

We have no idea of what the supply chain is for those clinics.

We don't know -- and this is really amazing to me --

we don't know how many children were born,

or how many children there are in Bolivia

or Botswana or Bhutan.

We don't know how many kids died last week

in any of those countries.

We don't know the needs of the elderly, the mentally ill.

For all of these different critically important problems

or critically important areas that we want to solve problems in,

we basically know nothing at all.

And part of the reason why we don't know anything at all

is that the information technology systems

that we use in global health to find the data

to solve these problems is what you see here.

And this is about a 5,000-year-old technology.

Some of you may have used it before.

It's kind of on its way out now, but we still use it

for 99 percent of our stuff.

This is a paper form, and what you're looking at

is a paper form in the hand of a Ministry of Health nurse

in Indonesia who is tramping out across the countryside

in Indonesia on, I'm sure, a very hot and humid day,

and she is going to be knocking on thousands of doors

over a period of weeks or months,

knocking on the doors and saying, "Excuse me,

we'd like to ask you some questions.

Do you have any children? Were your children vaccinated?"

Because the only way we can actually find out

how many children were vaccinated in the country of Indonesia,

what percentage were vaccinated, is actually not

on the Internet but by going out and knocking on doors,

sometimes tens of thousands of doors.

Sometimes it takes months to even years

to do something like this.

You know, a census of Indonesia

would probably take two years to accomplish.

And the problem, of course, with all of this is that

with all those paper forms — and I'm telling you

we have paper forms for every possible thing.

We have paper forms for vaccination surveys.

We have paper forms to track people who come into clinics.

We have paper forms to track drug supplies,

blood supplies, all these different paper forms

for many different topics,

they all have a single common endpoint,

and the common endpoint looks something like this.

And what we're looking at here is a truckful o' data.

This is the data from a single vaccination coverage survey

in a single district in the country of Zambia

from a few years ago that I participated in.

The only thing anyone was trying to find out

is what percentage of Zambian children are vaccinated,

and this is the data, collected on paper over weeks

from a single district, which is something like a county

in the United States.

You can imagine that, for the entire country of Zambia,

answering just that single question

looks something like this.

Truck after truck after truck

filled with stack after stack after stack of data.

And what makes it even worse is that

that's just the beginning,

because once you've collected all that data,

of course someone's going to have to --

some unfortunate person is going to have to type that into a computer.

When I was a graduate student, I actually was

that unfortunate person sometimes.

I can tell you, I often wasn't really paying attention.

I probably made a lot of mistakes when I did it

that no one ever discovered, so data quality goes down.

But eventually that data hopefully gets typed into a computer,

and someone can begin to analyze it,

and once they have an analysis and a report,

hopefully then you can take the results of that data collection

and use it to vaccinate children better.

Because if there's anything worse

in the field of global public health,

I don't know what's worse than allowing children on this planet

to die of vaccine-preventable diseases,

diseases for which the vaccine costs a dollar.

And millions of children die of these diseases every year.

And the fact is, millions is a gross estimate because

we don't really know how many kids die each year of this.

What makes it even more frustrating is that

the data entry part, the part that I used to do as a grad student,

can take sometimes six months.

Sometimes it can take two years to type that information

into a computer, and sometimes, actually not infrequently,

it actually never happens.

Now try and wrap your head around that for a second.

You just had teams of hundreds of people.

They went out into the field to answer a particular question.

You probably spent hundreds of thousands of dollars

on fuel and photocopying and per diem,

and then for some reason, momentum is lost

or there's no money left,

and all of that comes to nothing

because no one actually types it into the computer at all.

The process just stops. Happens all the time.

This is what we base our decisions on in global health:

little data, old data, no data.

So back in 1995, I began to think about ways

in which we could improve this process.

Now 1995, obviously that was quite a long time ago.

It kind of frightens me to think of how long ago that was.

The top movie of the year was

"Die Hard with a Vengeance."

As you can see, Bruce Willis had a lot more hair back then.

I was working in the Centers for Disease Control,

and I had a lot more hair back then as well.

But to me, the most significant thing that I saw in 1995

was this.

Hard for us to imagine, but in 1995,

this was the ultimate elite mobile device.

Right? It wasn't an iPhone. It wasn't a Galaxy phone.

It was a Palm Pilot.

And when I saw the Palm Pilot for the first time, I thought,

why can't we put the forms on these Palm Pilots

and go out into the field just carrying one Palm Pilot,

which can hold the capacity of tens of thousands

of paper forms? Why don't we try to do that?

Because if we can do that, if we can actually just

collect the data electronically, digitally,

from the very beginning,

we can just put a shortcut right through that whole process

of typing,

of having somebody type that stuff into the computer.

We can skip straight to the analysis

and then straight to the use of the data to actually save lives.

So that's actually what I began to do.

Working at CDC, I began to travel to different programs

around the world and to train them in using Palm Pilots

to do data collection instead of using paper.

And it actually worked great.

It worked exactly as well as anybody would have predicted.

What do you know? Digital data collection

is actually more efficient than collecting on paper.

While I was doing it, my business partner, Rose,

who's here with her husband, Matthew, here in the audience,

Rose was out doing similar stuff for the American Red Cross.

The problem was, after a few years of doing that,

I realized I had done -- I had been to maybe

six or seven programs, and I thought,

you know, if I keep this up at this pace,

over my whole career, maybe I'm going to go

to maybe 20 or 30 programs.

But the problem is, 20 or 30 programs,

like, training 20 or 30 programs to use this technology,

that is a tiny drop in the bucket.

The demand for this, the need for data to run better programs,

just within health, not to mention all of the other fields

in developing countries, is enormous.

There are millions and millions and millions of programs,

millions of clinics that need to track drugs,

millions of vaccine programs.

There are schools that need to track attendance.

There are all these different things

for us to get the data that we need to do.

And I realized, if I kept up the way that I was doing,

I was basically hardly going to make any impact

by the end of my career.

And so I began to wrack my brain

trying to think about, you know,

what was the process that I was doing,

how was I training folks, and what were the bottlenecks

and what were the obstacles to doing it faster

and to doing it more efficiently?

And unfortunately, after thinking about this for some time,

I realized -- I identified the main obstacle.

And the main obstacle, it turned out,

and this is a sad realization,

the main obstacle was me.

So what do I mean by that?

I had developed a process whereby

I was the center of the universe of this technology.

If you wanted to use this technology, you had to get in touch with me.

That means you had to know I existed.

Then you had to find the money to pay for me

to fly out to your country

and the money to pay for my hotel

and my per diem and my daily rate.

So you could be talking about 10,000 or 20,000 or 30,000 dollars

if I actually had the time or it fit my schedule

and I wasn't on vacation.

The point is that anything, any system that depends

on a single human being or two or three or five human beings,

it just doesn't scale.

And this is a problem for which we need to scale

this technology and we need to scale it now.

And so I began to think of ways in which I could basically

take myself out of the picture.

And, you know, I was thinking,

how could I take myself out of the picture

for quite some time.

You know, I'd been trained that the way that

you distribute technology within international development

is always consultant-based.

It's always guys that look pretty much like me

flying from countries that look pretty much like this