Your companys data could help end world hunger Mallory Freeman
June 2010.
I landed for the first time
in Rome, Italy.
I wasn’t there to sightsee.
I was there to solve world hunger.
(Laughter)
That’s right.
I was a 25-year-old PhD student
armed with a prototype tool
developed back at my university,
and I was going to help
the World Food Programme fix hunger.
So I strode into the headquarters building
and my eyes scanned the row of UN flags,
and I smiled as I thought to myself,
“The engineer is here.”
(Laughter)
Give me your data.
I’m going to optimize everything.
(Laughter)
Tell me the food that you’ve purchased,
tell me where it’s going
and when it needs to be there,
and I’m going to tell you
the shortest, fastest, cheapest,
best set of routes to take for the food.
We’re going to save money,
we’re going to avoid
delays and disruptions,
and bottom line,
we’re going to save lives.
You’re welcome.
(Laughter)
I thought it was going to take 12 months,
OK, maybe even 13.
This is not quite how it panned out.
Just a couple of months into the project,
my French boss, he told me,
“You know, Mallory,
it’s a good idea,
but the data you need
for your algorithms is not there.
It’s the right idea but at the wrong time,
and the right idea at the wrong time
is the wrong idea.”
(Laughter)
Project over.
I was crushed.
When I look back now
on that first summer in Rome
and I see how much has changed
over the past six years,
it is an absolute transformation.
It’s a coming of age for bringing data
into the humanitarian world.
It’s exciting. It’s inspiring.
But we’re not there yet.
And brace yourself, executives,
because I’m going to be putting companies
on the hot seat to step up
and play the role that I know they can.
My experiences back in Rome prove
using data you can save lives.
OK, not that first attempt,
but eventually we got there.
Let me paint the picture for you.
Imagine that you have to plan
breakfast, lunch and dinner
for 500,000 people,
and you only have
a certain budget to do it,
say 6.5 million dollars per month.
Well, what should you do?
What’s the best way to handle it?
Should you buy rice, wheat, chickpea, oil?
How much?
It sounds simple. It’s not.
You have 30 possible foods,
and you have to pick five of them.
That’s already over 140,000
different combinations.
Then for each food that you pick,
you need to decide how much you’ll buy,
where you’re going to get it from,
where you’re going to store it,
how long it’s going to take to get there.
You need to look at all of the different
transportation routes as well.
And that’s already
over 900 million options.
If you considered each option
for a single second,
that would take you
over 28 years to get through.
900 million options.
So we created a tool
that allowed decisionmakers
to weed through all 900 million options
in just a matter of days.
It turned out to be incredibly successful.
In an operation in Iraq,
we saved 17 percent of the costs,
and this meant that you had the ability
to feed an additional 80,000 people.
It’s all thanks to the use of data
and modeling complex systems.
But we didn’t do it alone.
The unit that I worked with in Rome,
they were unique.
They believed in collaboration.
They brought in the academic world.
They brought in companies.
And if we really want to make big changes
in big problems like world hunger,
we need everybody to the table.
We need the data people
from humanitarian organizations
leading the way,
and orchestrating
just the right types of engagements
with academics, with governments.
And there’s one group that’s not being
leveraged in the way that it should be.
Did you guess it? Companies.
Companies have a major role to play
in fixing the big problems in our world.
I’ve been in the private sector
for two years now.
I’ve seen what companies can do,
and I’ve seen what companies aren’t doing,
and I think there’s three main ways
that we can fill that gap:
by donating data,
by donating decision scientists
and by donating technology
to gather new sources of data.
This is data philanthropy,
and it’s the future of corporate
social responsibility.
Bonus, it also makes good business sense.
Companies today,
they collect mountains of data,
so the first thing they can do
is start donating that data.
Some companies are already doing it.
Take, for example,
a major telecom company.
They opened up their data
in Senegal and the Ivory Coast
and researchers discovered
that if you look at the patterns
in the pings to the cell phone towers,
you can see where people are traveling.
And that can tell you things like
where malaria might spread,
and you can make predictions with it.
Or take for example
an innovative satellite company.
They opened up their data and donated it,
and with that data you could track
how droughts are impacting
food production.
With that you can actually trigger
aid funding before a crisis can happen.
This is a great start.
There’s important insights
just locked away in company data.
And yes, you need to be very careful.
You need to respect privacy concerns,
for example by anonymizing the data.
But even if the floodgates opened up,
and even if all companies
donated their data
to academics, to NGOs,
to humanitarian organizations,
it wouldn’t be enough
to harness that full impact of data
for humanitarian goals.
Why?
To unlock insights in data,
you need decision scientists.
Decision scientists are people like me.
They take the data, they clean it up,
transform it and put it
into a useful algorithm
that’s the best choice
to address the business need at hand.
In the world of humanitarian aid,
there are very few decision scientists.
Most of them work for companies.
So that’s the second thing
that companies need to do.
In addition to donating their data,
they need to donate
their decision scientists.
Now, companies will say, “Ah! Don’t take
our decision scientists from us.
We need every spare second of their time.”
But there’s a way.
If a company was going to donate
a block of a decision scientist’s time,
it would actually make more sense
to spread out that block of time
over a long period,
say for example five years.
This might only amount
to a couple of hours per month,
which a company would hardly miss,
but what it enables is really important:
long-term partnerships.
Long-term partnerships
allow you to build relationships,
to get to know the data,
to really understand it
and to start to understand
the needs and challenges
that the humanitarian
organization is facing.
In Rome, at the World Food Programme,
this took us five years to do,
five years.
That first three years, OK,
that was just what we couldn’t solve for.
Then there was two years after that
of refining and implementing the tool,
like in the operations in Iraq
and other countries.
I don’t think that’s
an unrealistic timeline
when it comes to using data
to make operational changes.
It’s an investment. It requires patience.
But the types of results
that can be produced are undeniable.
In our case, it was the ability
to feed tens of thousands more people.
So we have donating data,
we have donating decision scientists,
and there’s actually a third way
that companies can help:
donating technology
to capture new sources of data.
You see, there’s a lot of things
we just don’t have data on.
Right now, Syrian refugees
are flooding into Greece,
and the UN refugee agency,
they have their hands full.
The current system for tracking people
is paper and pencil,
and what that means is
that when a mother and her five children
walk into the camp,
headquarters is essentially
blind to this moment.
That’s all going to change
in the next few weeks,
thanks to private sector collaboration.
There’s going to be a new system based
on donated package tracking technology
from the logistics company
that I work for.
With this new system,
there will be a data trail,
so you know exactly the moment
when that mother and her children
walk into the camp.
And even more, you know
if she’s going to have supplies
this month and the next.
Information visibility drives efficiency.
For companies, using technology
to gather important data,
it’s like bread and butter.
They’ve been doing it for years,
and it’s led to major
operational efficiency improvements.
Just try to imagine
your favorite beverage company
trying to plan their inventory
and not knowing how many bottles
were on the shelves.
It’s absurd.
Data drives better decisions.
Now, if you’re representing a company,
and you’re pragmatic
and not just idealistic,
you might be saying to yourself,
“OK, this is all great, Mallory,
but why should I want to be involved?”
Well for one thing, beyond the good PR,
humanitarian aid
is a 24-billion-dollar sector,
and there’s over five billion people,
maybe your next customers,
that live in the developing world.
Further, companies that are engaging
in data philanthropy,
they’re finding new insights
locked away in their data.
Take, for example, a credit card company
that’s opened up a center
that functions as a hub for academics,
for NGOs and governments,
all working together.
They’re looking at information
in credit card swipes
and using that to find insights
about how households in India
live, work, earn and spend.
For the humanitarian world,
this provides information
about how you might
bring people out of poverty.
But for companies, it’s providing
insights about your customers
and potential customers in India.
It’s a win all around.
Now, for me, what I find
exciting about data philanthropy –
donating data, donating decision
scientists and donating technology –
it’s what it means
for young professionals like me
who are choosing to work at companies.
Studies show that
the next generation of the workforce
care about having their work
make a bigger impact.
We want to make a difference,
and so through data philanthropy,
companies can actually help engage
and retain their decision scientists.
And that’s a big deal for a profession
that’s in high demand.
Data philanthropy
makes good business sense,
and it also can help
revolutionize the humanitarian world.
If we coordinated
the planning and logistics
across all of the major facets
of a humanitarian operation,
we could feed, clothe and shelter
hundreds of thousands more people,
and companies need to step up
and play the role that I know they can
in bringing about this revolution.
You’ve probably heard of the saying
“food for thought.”
Well, this is literally thought for food.
It finally is the right idea
at the right time.
(Laughter)
Très magnifique.
Thank you.
(Applause)