Good morning.
Good afternoon.
Good evening, everyone.
So my name is Tatiana Eremenko.
I work at the Spanish National University of Distance Education,
which was the Spanish partner of this project,
and I will be chairing the first session of our conference
after Rachael's explanation I won't be talking
too much about the project.
I think,
as she said, one of the objectives of our project
was to understand how the data revolution that we all hear about
the implications it has for professionals
in the international development sector.
In terms of training, the use of new skills, the use of new data sources,
how maybe they've had to work with new collaborators.
Anyway, all these changes that this data revolution
might have resulted in.
And so some of these results from the expert
interviews that we conducted were already published just recently
in an article in the Journal of International Development.
But we really thought there were so much more elements to be discussed
that it should be part of the final conference today.
So it's really an honor to have five of the five speakers with us
who all work in the sector in different positions,
who have very different visions on what on these issues and
we'll start right away with
with their participation.
So the plan is the following.
Each of the speakers will talk for about
four, several minutes, and then either you will react to
the other speakers right away or will see if there are questions
and comments from the audience.
Maybe we'll take those right away and then do that.
So I will present each of the speakers and then
in the order of an appearance in the program, and then we'll have
the debate at the end.
So the first person to talk today will be Bill Anderson
who is an information architect
at the organization Development Initiatives in the UK.
He's interested in the political economy of data
and focused on the strengthening of national data
infrastructures, particularly in low income countries.
He has published two pieces
the data side of leaving no one behind and data disharmony.
How can donors better act on their commitments
that summarize his learnings over the past few years?
So Bill, the floor is yours.
Thank you. Thanks.
Thanks very much.
And thank you for having me today.
The term data revolution is seems to be going out of fashion at the moment.
I'm definitely on Twitter.
It sort of is disappearing, which is a pity
because I'm very proud to call myself a data revolutionary.
And I think that, you know, there is very much this is an ongoing
revolution and there's a lot of work still to be done.
As I said, I'm going to focus mainly although I'm working
in Malaysia on SDGs in Malaysia at the moment, most of my works
in low income countries
are sort of what I'm going to say is it's very much around
what my work in Africa and in in low income countries.
And it's slightly different, I think, for other countries
because, I mean, what is replacing the revolution now is a lot of talk
about innovation and about the fourth industrial revolution
and how we can leapfrog and
how even Africa supposedly can leapfrog ahead
and bypass
a number of the slower steps that have taken place to date.
Why I think that revolution is still so important
is that in low income countries, there's a lot of smoke and mirrors.
We all realize the importance of administrative data.
We all realize the importance of new data sources.
But in fact and to be controversial, I say
I think the SDGs have a bit of responsibility for this.
We are still stuck in low income countries
with household surveys, which are small samples
because like too expensive to be done any any larger samples.
Household of these surveys run the show and they might provide
useful estimates for global databases but in terms of the data
that is needed at a subnational level to meet the SDGs
or to meet development programs, they are not very helpful
and that is where we are stuck at the moment.
And there is a continuing sort of investment that is going on in this.
I was doing some work in Bangladesh
a couple of years ago, and one of one of the indicators
that we came across, so when you're looking at maternal
mortality is obviously a very key issue.
And because most low income countries do not have
decent information on on deaths and causes of deaths,
there is quite a complicated formula that is used to calculate
the maternal mortality rate, which includes a statistic
called skilled attendance at birth.
In Bangladesh,
there are six different systems collecting data
on skilled attendance for surveys and two administrative systems.
All of these are funded by different donors
and they are all producing widely different results
So on the one hand, you can say, Well, yeah, we've got a lot of data now,
but it is kind of like a waste of effort, a duplication
competition between donors and producing something where
we're still stuck in
this world of surveys primarily.
And that's really sustainable data investments are still a problem.
Who is to blame for this?
One could say in the piece we published recently,
sort of looks at the last 20 years of the commitments
made by international agencies and donor institutions.
They have been talking for 20 years
about the harmonization of aid.
They've been talking about respecting country ownership, respecting
countries systems and the commitments they're talking about making
now are the same commitments they were talking about 20 years ago.
Are they going to change?
Well, we can ask again, but to me the more important thing is to say
that countries have to take ownership on this and it is countries.
And if anyone working in development
does not really respect the country, ownership
I think there's a lot of stuff
that has been spoken in the last year about localization.
And in fact a lot of that has been saying, oh,
what we need to do from from the developed world is to
ensure
that the way we implement stuff on the ground
or the way that things are managed on the ground is done at a local level.
But it's still our project.
We still own the process.
I can't stress enough
how I mean, how we have got such a long way still to travel
in terms of the transfer of ownership in these processes.
Just wanted to say
a couple of things about innovation
in Uganda.
There was a fantastic project.
I Global Pulse is a small U.N. agency.
It was a laboratory in Kampala and one in Indonesia.
And in Africa.
Local radio stations are still really important, as perhaps
the most important means by which people communicate.
And a pilot was set up where
a lot of local radio stations were recorded.
They use a lot of different languages.
Those are transcribed and natural language processing.
What was was passed through it.
And in that work, they predicted a cholera outbreak
two days before the health service did.
An amazing point example of innovation
and what innovation can do and the powers of air.
The problem with that story is that was a pilot and it went nowhere
because there was no means of operationalizing it.
Operationalizing it was then financed to scale it up.
More importantly, had the political engagement been done
with the National Statistics Office or the other ministries to say
for them to be on board to say, okay, we can now start accepting that data
and how it could then
be staffed to be used in some kind of form
even gradually, either gradually that might be
but a die to death
Well,
there's been a number of attempts to measure poverty.
So Facebook has a relative wealth index,
which I think the term defines a human being as someone with a smartphone.
And given that only half people in Africa have a smartphone,
it doesn't say much about how you count people.
There have been attempts to measure poverty
by looking at satellite photography of night lights.
There has been a project getting started originally
by Department International, developed in the UK
to look at said like photographs of roofs
and trying to identify poor people from the nature
of the roofs of their houses.
All these attempts at innovation
might be well intentioned, but the idea
that when we get to some of the basic points of development
and to see that proxies are the way in which we are going to use data
to data to define our priorities,
I think is a problem
The there was a fantastic book
written a couple years ago called Geek Heresy by Kentaro Toyama,
who was started off the Microsoft Research Lab in India some years back.
And he argued that
technology can only amplify what you've already got
And to me, if you're looking at the Leave No.
One Behind agenda to say that we are really going to use
data, you know, within development, you
to count people.
I mean, people have to count.
And for people to account, you really have to count people.
We cannot get too excited and jump the gun
and think that proxy is going to replace everything else.
There's one awful one.
One final comment and also
a step backwards.
It's happened in Africa.
Civil registration.
Birth registration is a huge problem.
There is very little information on the registration of births in Africa.
And there was a really good program set up five more than five years ago
by the African Union and a very complicated
acceleration program for CBS.
And what it was doing,
it was getting the Buy-In of governments of,
you know, of of ministries to build up this process.
What has happened in the last couple of years is in
has come aid for Africa which is a private sector led thing.
Great biometrics is the future.
We need a good biometric ID because that is the way
we're going to advance the economies of our countries. The
that process, the idea for Africa process,
I mean, there's an article recently by Pas de la Hausler, who was the
assistant general in South Africa, just pointing out and saying that how
by pushing in
that innovation process, it has set back
the administrative processes that are needed to make that work.
So I just wanted to get on the idea is there's
this interrelationship between innovation
and making things work on the ground.
To me is the most important issue facing us in the data revolution
today. Well, I.
Thank you, Bill, for this great opening to the session.
And your final point.
I'm sure
that their participants will have a
things to add.
So let's welcome Rafaela
Greco Taniguchi, who holds a Ph.D.
in public law with specialization in migration law and a master
in international development since 2008.
She's the team leader on human mobility
and the Belgian Development Agency And before this,
she served as an expert on migration and development,
mainly for the European Commission and the IOM.
The floor is yours, Rafaela.
Thank you very much, Tatiana, and thank.
You all for having me here.
I obviously don't feel qualified enough to discuss.
With you, but I.
Hope to bring some ideas on the table.
And mostly after listening to Bill, I feel very much challenged
as a police former policy maker and no program maker
in the area of international development with a specific focus
on migration and human mobility as you said.
Tatiana.
So these are the lens
with which I come to the table to give small contributions.
So where thus where do data. And.
Data sources come into the picture
for a development practitioner like myself?
While trying to summarize, I identified three
key moments of my daily life and the life of a development.
Agency where.
Where we have witnessed changes and I am.
Not quite sure we were.
Prepared for those, nor that we are now.
But I think it's interesting to engage in a discussion with.
Your first
feasibility and project design.
So feasibility analysis.
And project design where.
We are asked the more and more by our
donors or administration.
I have been very quick in introducing myself.
I work for the Public Bilateral Cooperation.
Agency for a development. Of Belgium.
Therefore, my main donor and administration is the Ministry.
For Foreign Affairs and. International Cooperation.
We are asking them more and more to provide data
and use innovative data sources to court to build.
The innovation is really the keyword.
For the present and the. Future
when assessing situation,
carrying up feasibility studies and designing project and.
Programs. When I talk about.
Project and programs,
we are talking here about multiannual frameworks of bilateral cooperation
between Belgium and other partner countries.
So they are.
Obviously much obviously multidimensional, obviously
multisectoral, obviously multi partner.
So the complexity comes immediately into the play.
What are we. Talking about when we talk about data?
What are we talking about when we talk about innovative data?
And of course, we were used on conducting household.
Surveys with small samples to justify the.
Problem analysis that we would make and then the solutions that we would
to bring. On the table.
So our programs, this is the less and less accepted
by the donors and the administration, them
and we are, the more and more asked to count on big data and.
Data coming, for.
Instance, from
analysis of social networks.
Most of all, when we talk about.
Assessment.
About movements and about
diaspora relations.
Transnational relations.
Second, very important moment.
Where.
We need data or where we are pushed towards new ways of new relations.
Between practitioners and data.
Makers and analysts is the implementation of programs,
because during the implementation of programs, at several times we
if we think that the co-creation until five,
ten years back, we would have have cartography and mapping
exercises
in the different areas, we would implement project.
Now, all of this is old fashion, and we are the more
and more asked to connect with those who
produce data in a in a
in an attempt, as Bill also pointed, to transfer. The.
Ownership in data collection and support
local authorities and notably the National Institute.
And National Statistics Institute.
In our partner countries to be the more and more able to perform.
What. They are asked to.
But at the same time, and this is always the dilemma
in development projects, you are asked to intervene on this long term
knowledge and ownership transfer and.
Strengthening or building or development.
Of capacity and on the other end to produce results very quickly
and the famous quick wins, we are constantly reminded us
and therefore we are kept in this in this tension.
And in this. Dilemma. And
we end up
very often going back to the old fashioned.
Method of carrying out survey household surveys and.
Small sample studies in order to have information
for the implementation of the project.
And last bit of the programing
cycle and project cycle management
where they are extremely and the more and more important them and.
It is here where I would like to.
Focus is the assessment. Phase.
So monitoring and evaluation. Phase.
Impact. Assessment phases.
I will focus now in the last three, four years of project programing
and implementation
on migration and development in particular, because
through the injection of an enormous amount of money
since the creation of the EU Trust.
Fund to address the root causes of irregular migration and.
Instability in Africa, we are talking.
About €2 billion and even more if we count some.
Other contribution in a very.
Short period of time, four or five years. Maximum
This has been enormous in terms of economic.
Investment from the Commission and the Member States.
And therefore an enormous investment has also been done
in trying to monitor the way these funds were used
to assess and evaluate the results.
The outcomes.
Of the different projects, and to assess the impact of these.
Interventions for all.
Of these three, measuring moments and assessment moments
we have, as international community and European International Community
developed new tools and new ways of collecting
data, analyzing data and using data.
And here
I really
appreciate what.
Has been said by the advice.
Adding at the beginning,
how do we collect data where the where the data comes from?
How are they treated?
These are a question that they.
Have the impression we have a bit left.
Aside and we have concentrated ourselves in hiring private companies
that would in parallel after implementation of the projects,
carry out extremely heavy and burdensome
data collection exercises
in order to have three monthly ideas and updates
of specific framework for monitoring the.
The implementation progress. Of the.
Very.
Many hundreds of projects that have been funded only through the EGF
for Africa.
Then other external
actors involved in annual evaluation and data collection.
Exercise.
Project by project.
On top of this overall evaluation of all the.
Projects. Assessed with data collection.
And I want to end with one exercise in particular that is I think.
Very relevant for your discussion, for our discussion now,
which is a counter-factual. Impact evaluation.
That has been launched.
Beginning of last year by the Commission, specifically on ten.
To Russia.
I'm sorry, could we maybe come back to this point, the country.
Absolutely.
So that we have time for everybody to present. But.
Absolutely. Thank you.
So let's move on to our next speaker, who is Inigo
Macias, who works at Oxfam Interim One in Spain.
And he works on issues related to development, inequality
and international cooperation in different public and private
organizations, both in Spain, and internationally.
And he has a master's
in development studies from the London School of Economics
and a bachelor's degree in Economics and Business Administration.
From the
university.
Inigo.
Let's hear what you have to say.
Thank you.
I hope you can hear me.
First thing, some things, things for limitation.
And also, we should all welcome this initiative
because I guess that most of us here in this in this event
we are always dealing with data and and the struggle in our minds
how to deal with that data.
And this is this is something that we are
we are all in the same team.
I just wanted to I'm going to I'm going to be very, very brief
because I think that
maybe it is going to be more most important
is going to be the conversation afterwards.
But I would like to raise
well, I would like to raise what would we understand
are the opportunities and challenges of this of this data revolution.
But first of all, let me let me start with the caveat.
I guess most of you know Oxfam.
Oxfam is a global NGO that's a confederation of different ingos.
And I work as a research coordinator here in Spain.
And I support all the initiatives that we as Oxfam,
we deal regarding campaign and influence in.
So I'm going to talk about building on this experience.
Right.
What did I mean, the opportunities?
I think that would be said.
This idea of
that, that technology can only amplify what we have.
I mean, this is something that we are seen every day
when it comes to analyzing the difference social phenomena
and where it is happening in the South, in the global
global south or in the global north.
And this is something we are seeing and we have just a recent example
because of COVID, the the probably the the
after the deaths of the people, the economic and social impact,
I mean, data has been the most important collateral
victim of COVID.
I mean, we have missed
a different wave of household services health services.
So we
we haven't been able to really track what has happened
in lots of countries.
So I mean, and we have seen that in the south
at the same time, we have we have important opportunities
because I mean, this
this data revolution is basically helping us to generate
different venues, new venues to generate knowledge,
whether it be research or any other way of generating knowledge.
And and also what we are seeing is that this data revolution is
is allowing us to analyze different social,
economic and political phenomena in a different way more directly.
I mean, we were used to use different I mean, using different techniques,
using instrumental variables,
but not being able to basically to analyze and see
what was going on with that phenomena and this data revolution
that is basically helping us to to monitor that.
And I give you a couple of examples.
I know during COVID, I mean,
we were working with some
people from the World Bank and through phone services,
through phone surveys,
we were able to check what was happening in in different countries
at the household level, what how COVID, how bad that
restriction policies, how the economic impact,
how that COVID was impacting different households in different countries.
So and then through these households,
these these phone surveys, we were able to somehow
see what the impact was Also, I mean, we've been able, for example,
to portray
how social mobility or how inequality is affecting social mobility.
We are able to see how people from different neighbor neighborhoods
in different countries.
I mean, they don't they don't they don't mix with each other.
What I mean, with this, I mean, we see people
from poorer neighborhoods, not going to the same places
as the families and households from rich neighborhoods.
So so and this is helping us to to portray different phenomena
that before before this sort of data revolution,
I mean, we were just able to capture the tip of the iceberg.
So these are these are important opportunities,
another important opportunity is that in some cases,
we are being able to monitor these different phenomena
in real time.
And and this is this has happened
in basically in lots of like rich northern countries.
It is more difficult to see that in developing countries.
But in the case of COVID, right now, we have some data sets that basically
following what was going on, the bank accounts, they help us to see
how COVID has impacting wages and things like that.
So, I mean, this is something that it is
I mean, if they are in place and also building on that,
when that huge gap that exists between the the global north
and the global South.
But that helps us.
I mean, in our case for Oxfam, that we are basically I mean,
we are able to to monitor these these phenomena in real time.
We are able to provide solutions, policy solutions.
We are able to show the people what's going on and then to
move these people towards whatever decision
and this is this is another important opportunity
that that that we see this this data revolution is
is helping out in terms of challenges I mean,
there are new.
New, new.
Thousands of new providers of information.
We don't really know anything.
Bill was talking about abouts household surveys,
but we don't really know
exactly how they do these new phone services, these new providers of data
and what's behind the methodology they are using to survey
and to get the phenomena.
And this is something that that we have so many different providers
and they are not as transparent as we are used to.
When it comes to household surveys.
And this is the main challenge.
And that leads us to two to two important implications.
This that sometimes I have been on my own, this false sense of confidence.
I mean, I have that data
and then I take this data as it's portraying the reality.
But I don't know the details,
and I am eager to use that data because I don't know the details.
What are the limitations, what they are currently measuring,
what I can say because I mean, we have the data, but we need
I mean, there is a huge gap between data and information.
We have lots of data.
But I mean, at least in my case, I need to transform
that data into information and knowledge.
And this is it is an important this is an important challenge for me.
And also something that that Rafaela said about most of these providers.
I mean, there is an increasing privatization of this data
and and and I do know
I mean, I really would like to have this sort of like real time
data of what's going on with the people when when COVID
considering the impact, the
economic and social impact of COVID in different households.
I really like to have that data publicly available.
And what we see is that it's basically managed by
banks and other private institutions.
And it is really, really difficult for us to access that that info that
that data sets from that data and to info
and then basically help to define what in this case, which would be
the most appropriate policies to deal with with these situations.
Okay. Can I leave it here? Thank you.
Thank you.
And thank you already for reacting to
what was said by our previous speakers.
It's really great.
I will present our next speaker, who is somebody who is the director
of the Demographic and Social Statistics Department
of the Senegalese National Agency for Statistics and Demography.
He's trained at the Institute
of Demographic Training and Research in Cameroon, has a Ph.D.
in demography, and he's in charge of one of the things he's organizing
the most recent population census in Senegal.
Mr. and I will be talking in French but you will
be able to hear translation.
More Xhosa.
Less LEP a whole whose it's not illusory.
She said to me,
because you mentioned
an 8000 executive panel.
We know
you're that best members
should have to support us.
But in the confessional at least, you see that the seven per year
the demography deficit, demographic issues
that are not the rest of the demography
disregard
representation which is they're
sort of a new impetus.
You know, literally they
statistic
a leper
who says that to build up to
La Fermeture it would be a literal disaster
pulling statistics to our social
protection measures to mass
General de la, that de la demographic
of community.
Remarkably, so the collective is that is
already population.
It's as you know sociodemographic economic
and is that more
lives up that also tend to let us off measure and that under the population
is that demographic, it is something
that we see there's a capsular condition.
If you diminish
that investment, the
okay and the value comparable to let me say
is that I've said that assessment.
A. Major crisis of quality
and is fixated on the populace
abusing and ethnicity
an example exemplar
la demographic no system step to excess or less
if you differentiate that it's just demographic yelling and an.
So you respond to both your
in business and mathematics
and sociology assure that I feel an anthropology
would be a better master
and a multidisciplinary
and a Louis that's more
like collect data like when you collect collector.
In fact you're doing this equality so it will see land at least and then
the last admission you enter a pretty effective method
for what it allows you to mass market multiple premises
so to use of before
the statistical is a demographic in the sense it says I get that but
asking in is indicative of zero love mortality
lamentably de la frequently
if you are familiar
and then well you can see the sentimental demographic
is a threat by lear abdication
sociology medical
those empty
busy bus services.
I get lazy that will be demonized
due to a lot of these statistics still appear to be
no de la condition to be less in small
a condition to view or see
necessities
for beer and therefore they say specifically
for metro system
to seek out about sort of click assaults sociology.
No reason not to see a list of people saying this is that that I just have
to see the Institute of Sociology if they did cause you're
pretty sure
that the festival about the civil if anything
but put I suppose you should be listening
to music in.
Fact I don't meet yet the problem. The.
Choice.
The quitting show we.
Are not.
Who we are who belong to me what I am
problem. But.
In case you didn't go.
With that.
And kept listening to me
and I began to see barber college
a lady and develop my best technology
only to lose the present police the see electronic
and but
mostly as opposed to member that is about electronic sources
say that that is if you use our super satellite connectivity
then that would be the lesser after
the LA producer step to see
Latinos as is admitted here facility and then denies the
to see basically to the associated people this is a de la la
galaxy will be able to be immaculate or to demonstrate that if they
they believe if the loan system
utility importantly sources
will say that seek and applauded for
the fact that the system suggestion refer
to the appropriate program for families to be
the center of the pleasure program will be at the same level.
I sure certainly appreciate independent help and that Mr.
Plus hello si si
and at least that will be offered comment system
to see it for a living conditions associated
with an embassy in and
Saudi
Arabia insidious abusive.
Yes it's very easy you to see that it
gets your specific info from our embassies of
celebrities. I met with you.
They appreciate
that last night to see yet if it is I get a lot of these indicate
then I that what they were saying Apple is objective
yesterday and the bottom is
that yesterday I
la societa is speaking
about the legal requirement but I do believe
Bell will be the leader exists
as this is what insurance is
and I live in a
of a compound in Africa.
So if you will see the selling you permit
a Cisco several members of the I think you said I love to activity
but they have met yet it is
said that we seek a level met system to see buy
back says lot up and the court a little bit less room
to optimize the approach of yes you can ask the limited amount
so you believe so.
I don't
buy the a week crucial wasn't there
but bullet got it compressed
and Adesanya her and that will show you
who have just showed her shoulder happened on a discussion opposite
she's a.
Customer who markets in ways that will receive approval.
I see that
she and for the last speaker who's going to be Emily Perskie
who she's a statistician at the office when asked
I'm sorry,
who is a statistician
at the office for National Statistics in the UK
and she's in charge of setting up and managing a new branch
focused on using on skills to increase statistical capacity in partner
countries previously she worked at the Department
for International Development in the UK as well,
and her work focuses on increasing statistical capacity building
and in countries such as Tanzania, Kenya, Yemen.
So Emily and now in Egypt where she is
talking from the floor is yours
Thank you.
Thank you for having me. As part of this debate.
I'm similarly to Ben.
I've spent most of my time working in low income countries
around around the world.
I happened to be in Egypt at the moment
where we're running a statistical leadership training course.
But that's just where I happen to be at the moment.
So I work for a national statistics office.
Now, I've previously, as you said, worked
in the development organization.
So I kind of see this debate
from multiple sides, which is really, really interesting.
So as previous speakers have have,
I have alluded to the I have said that
the potential benefits of the data revolution cannot be overstated.
We have the potential to improve data
collection, data analysis to bring new insights.
But those of us working in development is massive,
and there are lots of examples of innovations
and I'm really pleased to hear from
some of some of the previous speakers about some of those.
I think
one of the things that I want to say is that it's the role
of the National Statistics Office
to bring together information from a range of sources
and try and use this to interpret which is which data
they can use for decision making and planning and monitoring.
Our organization was set up
during the Second World War by Winston Churchill
when he got fed up of hearing various information from a range of sources
and not being able to make sense of that.
And he said,
I want one institution which tells me, which is the data I can rely on.
And I think that is that is still the mandate of the Office
for National Statistics and National Statistics offices around the world.
I think, as Bill said previously,
for many decades now, a lot of national statistics
offices have become data collection agencies
rather than agencies pulling data from a range of sources
and trying to get to the truth underneath that.
And I think that there needs
to be a shift in the way that national statistics offices
think about their role and position themselves within within
the government. And
I think
there's a huge role to have for bringing together
the innovations and the traditional data sources
to really try and get that robust picture.
So and a couple of examples of innovations that I wanted to focus on.
One from from international development and one from the UK.
And that I think really kind of illustrate this point.
So in the UK and when the 2008 financial downturn
hit, the official GDP figures were too slow too.
So to predict that the two shows that there was a recession
and that the country was moving into recession
and the National Bank was therefore too slow to take the decisions
that were needed to manage fiscal policy monetary policy.
And the onus came under quite significant criticism
for that, for saying there's a lot of data out there.
Why does it take so long to produce the quarterly GDP figures?
And since then there's been an investment
in innovation within OTAs and we're now looking at using proxies
from a range of sources to have early indicators
of the economy and the economic change.
And so we use data from
shipping
air data from the shipping industry,
we use web scrapes, data range to come up with a range of proxies
that can say very early in in the period
what how the economy is faring and that that will not in any way
replace the official measures of GDP based on the robust data
which come later on in slower time.
But it allows those policymakers to see
early if decisions need to be taken.
I think another example of this, which the kind of requirements
have the traditional and bring it together with the
with the new forms of data
and a project that I'm really inspired by is the three program,
which many of you I'm sure have heard about.
And so this came from the Gates Foundation
running vaccine distribution in many countries.
I'm finding that when they got to the remote areas,
they either had too many vaccines or not enough
because the population numbers were out of date
were not accurate enough to use for detailed planning.
So the three program pulls together data from a range of different
sources, uses cell phone signals, night lights, etc.
mashes that together with the official data
to get more UP-TO-DATE low level estimates of the population.
And these have been proved to be really efficient in
planning the vaccine rollouts and bed net rollouts and other things that
the Gates Foundation were particularly interested in at the moment.
These are done
by in an academic way by universities,
but they rely heavily on
the traditional data and the surveys that we talked about,
the smaller sample but robust, well funded surveys,
the censuses in order to to make that link between the proxy
and the solid evidence that you've got.
And now I think that needs to be done
more and more in conjunction with the National Statistics Office.
And so the way that the UK is using this, these early indicators of GDP
with we're still doing that
within the system and we're linking those early estimates
so if they if they're the new innovative forms of data
that can give you greater disaggregation,
they can give you quicker data, they can give you more timely data,
but they can't
replace the official data because they need to be benchmarks by something.
So I think, again, Bill mentioned the work
that's being done on using tin roofs and stories to estimate poverty,
and that data can give you much more frequent and lower level
estimates than than the household surveys can give you a property,
but only if you know the relationship between tin roofs and poverty.
And that relationship
is going to change
over time and have space
and so you need those benchmark surveys
to be able
to give you that relationship, which you can then based the model on.
I think one of the challenges
for the next period and right now is the fact that a lot
of that experimentation is being done by different organizations,
as previous speakers have said, it's hard to know which
which of the ones that are good quality, that have a solid methodology
that you can use.
How do you understand some of the sampling error biases
if you're using data from cell phones and only half the population
has cell phones, or even worse from social media,
which is biased towards particular population groups,
and so there's a lot of experimentation going on.
And I think I think the trick and the challenge going forward
is linking that those innovations and those experiments
with the official statistics
and trying to make sure that you connect that that the user
can understand how those things can complement and build together.
And we in the
and we have
we're really privileged that we have this fantastic data science campus
and doing a lot of that experimentation
and also
looking at experimentation by others and seeing how that's relevant
to the UK.
Not all national statistics offices have that resource
or are going to have that resource and if we did things more effectively
and efficiently, it wouldn't need to have that resource.
That is really important for all of us to think about how we can invest
in the right kind of innovation, in the right kind of experiment,
where we can identify where existing data
can help us to give a more granular picture,
a faster picture, in order to plan and take those decisions really well,
but also how that can be linked
and used more systematically and used to build overall effectiveness
of the system rather than pockets of experimentation.
Okay.
Well, thank you very much, Emily.
I think it links in an interesting way to what was just said before
from the viewpoint of Senegal, where you have similar.
Yeah.
Working on similar data sources but also thinking about
how to bring in innovation for of that.