WEBVTT

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[MUSIC PLAYING]

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EME OWOAJE: In
this section, we'll

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be looking at the
importance of politics.

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Who has power over the
data and the decisions?

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So in this slide, we
look at the requirements

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for data for decision making.

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There are social
requirements, there

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are financial and
technical requirements.

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I'll just highlight
some of the factors

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under the various sections
that we've mentioned.

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So in social, we look at
stakeholder engagement

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in data collection,
analysis, and dissemination.

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It is extremely important to
engage with the stakeholders.

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Because they must know why
you're collecting the data.

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They must know the
importance of the data.

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And this helps to ensure buy-in.

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Then it creates interest in
evidence-based decision making.

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And then, also, the
training of individuals

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engaged in data collection.

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That is extremely important.

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Because without training,
your data collection people

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will just go out and collect
whatever data they think is OK.

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Then the financial aspect,
to encourage local funding

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in data collection
analysis and dissemination,

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this is extremely
important as well.

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Because it fosters the notion
that, yes, this data is ours,

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and we have funded it.

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And we all know that when
people pay for things,

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they are more likely to
be interested in what

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they pay for rather than things
that are given to them free.

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Then there's also make
a case for investing

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in data collection to
donors engaged in the health

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systems of the country, then
integrating the various data

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sources to create a
sustainable data network, which

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is extremely important.

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Because all levels
of stakeholders

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should be involved
in that network

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so that they will have
that data to work with.

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Then there's a technical aspect.

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And this involves
employing the use

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of technological innovations to
ensure quality data collection,

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as we have with the AVADAR.

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So under the technical
section, firstly, we'll

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be looking at employing the use
of technological innovations

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to ensure quality data
collection as with the AVADAR,

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which represents [INAUDIBLE]
visual AFP detection

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and reporting, remembering
also that AFP is Acute Flaccid

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Paralysis.

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And then we will also look
at training and retraining

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on innovations used for data
collection and analysis,

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checking of data quality,
cross-validating, mechanisms

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for quality assurance.

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Then the technical
aspect also takes

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care of training
personnel at all levels

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on supervision vision of
data collection activities.

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Political, and cultural,
and ideological factors

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affecting data for
decision-making

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are also very important.

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Some of these include--

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culturally, people
in some regions

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may not be forthcoming regarding
the information on the number

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of children they have.

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Then on the side of
the health sector,

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managers and implementers
may inflate data figures

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to obtain financial rewards
for providing certain data.

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And other aspects
could be that there

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are many vertical
programs which result

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in generation of parallel data.

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There is also the factor
of political sensitivity

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and resistance to accepting
monitoring data and feedback

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on quality gaps.

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This occurs quite often.

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Data generated from
different donor organizations

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aren't usually shared.

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That's another problem
that is encountered

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in a number of countries.

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And, finally,
policymakers are not

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trained to understand
scientific data.

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There's a lot of political
sensitivity or resistance

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to accepting monitoring data
or feedback on quality gaps.

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One has to be a bit liberal
with numbers and budgets

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in planning such programs so
that the genuine implementers

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are not constrained by
artificially imposed ceilings.

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We have prepared
an activity for you

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to map these concepts to
implementation, research, IR

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competencies so that you can
think about how political,

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cultural, and
ideological factors

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affect data for decision-making.

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Please see the course
site for more details.

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So in this slide we'll be
looking at data collection

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and feedback mechanism.

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The data is usually collected
at the health facility

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level or the community
level and then

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it's transmitted to the local
governments and the partners

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at that level, then to the state
government and the partners

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at that level, and then to the
federal government and partners

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at that level.

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On the right hand
side of this slide,

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we are looking at the various
offices and organizations

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that are involved in
the data collection.

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So at the local
government level,

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we have the disease surveillance
and notification officer.

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We also have the
Monitoring and Evaluation--

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that's the M&E officer
and other ad hoc staff.

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At the state government level,
you have the state's Disease

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Surveillance and Notification
Officer, the DSNO, and then

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the state epidemiologist, you
have the state M&E officer,

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and you have the state primary
health care board officials.

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Then you also have WHO,
UNICEF, and other NGOs

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that are involved in the
polio eradication initiative.

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At the federal government level,
you have the federal ministry

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of health, you have the national
primary health care development

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agency, as well as the National
Centers for Disease Control

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and Prevention, and you
have international NGOs

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involved in the polio
eradication initiative.

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So this is one model of
how the data collection

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process is supposed to work.

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It is a political process.

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In this model, however,
it seems that the feedback

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of the health facilities and
the data collection points

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comes from the federal level.

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There's additional value of
feedback for the immediate

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[INAUDIBLE] next
supervisory level

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to cut short time to
improve data quality

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and take corrective actions.

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This improves ownership
at the successive levels.

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So we'll be looking
at data and power.

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We have a quote for a
global-level policymaker.

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"At the country level,
whoever has the data

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can interpret the problem
and make the decision

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about how to tackle it, which
that means they actually

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have really a lot of power
over how money is spent

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and how money is requested.

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So the data is like the
gold of this program."

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In some times and places,
this wasn't a problem.

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' In other times and places,
there were controversies about

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data and data access between
governments and other UN

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agencies and even between
different levels of the same UN

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agency.

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Ideally, data should
be open to all

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and interpreted and
used for decision-making

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at state and local levels
rather than retaining all power

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and decisions at the
national, regional,

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and headquarter levels.

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Unless they are under pressure
from higher levels simply

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to show data that looks good
rather than data that is good--

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meaning it also shows problems--

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implementers at
successive levels

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should own their own data
and the credibility attached

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with it.

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Ideally, too, data at all
levels should be shared.

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It shouldn't just flow
from the lower levels

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to the higher levels,
but aggregated data

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should be open for use by
people at lower levels.

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But the point here is that
data sharing and use is always

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political.

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And so these things need to be
plugged into health programs

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from the outset with
the understanding

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that there will be
political and power

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reasons to keep datas secret.

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We have an example in this slide
that addresses controversies

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over data at the Nigerian
Emergency Operations Center,

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EOC.

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As mentioned in the previous
lecture, the EOC in Nigeria

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was an important site of
data for decision-making,

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but sharing data was,
according to some interviewees,

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a challenge for some.

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Agencies and
individuals, who had

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controlled certain
data for years,

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we're now asked to share.

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According to one interviewee,
when the EOC was started--

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for some people to have
it, that is the data,

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be completely open
to all the partners

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and be second-guessed in
terms of their interpretation

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of that data was so
threatening to them.

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So in this slide, we continue
with the controversies

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over data at the Nigerian EOC.

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We have a quote from another
global-level policymaker.

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"At one point in Nigeria, we
had to go up to the Minister

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and say, 'this is
your government data,

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but nobody has
access to it, why?'

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And the response he gave was,
'You,' and by 'you,' he meant

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all the polio partners
and external people.

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'You have been doing this in our
country for decades and yet I

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don't have any data managers in
my ministry who can actually do

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the kinds of analyses you do,
and you are supposed to be

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building our capacity,
what's wrong?'

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And it was just such a telling
moment because he was basically

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say, 'I would like
to own the data,

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but I can't and by
nature of your presence,

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you're kind of tying our hands
and we have to listen to what

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you're saying about
what this data says.'

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And that led to a data-sharing
guideline for the EOC,

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and it basically outlines which
server this data would sit on,

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who had access to it,
all of this stuff,

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and it led to a much more
equitable way of sherry

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the data and analyzing it."

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In this slide, we'll be
having an exercise looking

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at national level data sharing.

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So I'd like you to look
at the health program

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that you're familiar
with, perhaps polio

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eradication in your country,
and outline the following.

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The different actors.

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The data needs.

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Access to needs.

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Control of data.

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Outline how roles
and responsibilities

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could be defined to
promote collaboration

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rather than competition.

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We have another
example in this slide

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and we'll be looking at global
data sharing agreements.

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Global data sharing for polio
has seen many challenges,

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and data sharing agreements
have been critical.

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As we've mentioned, data
leadership is about power.

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So we have another quote
from an interviewee.

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"I think one of
the solutions are

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lessons here is in any
kind of global effort

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like this is put effort into
those data sharing agreements

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at the beginning rather
than running into tension

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and letting it
slow you down later

00:12:36.540 --> 00:12:39.640 align:middle line:90%
and just being open about this.

00:12:39.640 --> 00:12:42.460 align:middle line:84%
It needing to be an
explicit thing that's

00:12:42.460 --> 00:12:44.380 align:middle line:90%
built into the work.

00:12:44.380 --> 00:12:48.910 align:middle line:84%
I also think, and within that
the idea that the government

00:12:48.910 --> 00:12:51.460 align:middle line:90%
always needs to own the data.

00:12:51.460 --> 00:12:54.790 align:middle line:84%
The server that the
data sits on needs

00:12:54.790 --> 00:12:59.620 align:middle line:84%
to be government owned
and government managed.

00:12:59.620 --> 00:13:02.230 align:middle line:84%
The people who are managing
and analyzing the data

00:13:02.230 --> 00:13:05.830 align:middle line:84%
should ideally be governments
and without a kind

00:13:05.830 --> 00:13:10.420 align:middle line:84%
of explicit agreement on that,
again, from the beginning.

00:13:10.420 --> 00:13:16.300 align:middle line:84%
It's been very hard to retrofit
that and kind of pass the data

00:13:16.300 --> 00:13:19.660 align:middle line:90%
back to the government."

00:13:19.660 --> 00:13:21.670 align:middle line:84%
In this slide,
we'll just briefly

00:13:21.670 --> 00:13:23.950 align:middle line:90%
look at data management.

00:13:23.950 --> 00:13:26.290 align:middle line:84%
Data management
includes all aspects

00:13:26.290 --> 00:13:30.970 align:middle line:84%
of data planning, handling,
analysis, documentation,

00:13:30.970 --> 00:13:31.930 align:middle line:90%
and storage.

00:13:31.930 --> 00:13:37.360 align:middle line:84%
It takes place in all stages
of the data lifecycle.

00:13:37.360 --> 00:13:39.230 align:middle line:84%
So on the right hand
side of the slide,

00:13:39.230 --> 00:13:44.470 align:middle line:84%
you can see the various
stages of data management.

00:13:44.470 --> 00:13:48.430 align:middle line:84%
So starting from creating
data to processing data,

00:13:48.430 --> 00:13:53.350 align:middle line:84%
analyzing the data, preserving
it, giving access to data,

00:13:53.350 --> 00:13:55.600 align:middle line:90%
and then reusing it.

00:13:55.600 --> 00:13:58.390 align:middle line:84%
So data management
is technical work

00:13:58.390 --> 00:14:02.160 align:middle line:84%
because it can also be
a political process.

00:14:02.160 --> 00:14:04.900 align:middle line:84%
Ownership issues
should be well-defined

00:14:04.900 --> 00:14:09.450 align:middle line:84%
and I believe we talked about
that in the earlier slides.

00:14:09.450 --> 00:14:14.010 align:middle line:84%
So what's the takeaway from this
module that we've gone through?

00:14:14.010 --> 00:14:17.190 align:middle line:84%
Build open data
into project culture

00:14:17.190 --> 00:14:21.000 align:middle line:84%
from the beginning
and the specifics.

00:14:21.000 --> 00:14:24.210 align:middle line:84%
In big programs implemented
through big partnerships

00:14:24.210 --> 00:14:26.820 align:middle line:84%
or in partnerships
with governments,

00:14:26.820 --> 00:14:31.260 align:middle line:84%
all data should be available
in the public domain

00:14:31.260 --> 00:14:33.640 align:middle line:90%
and open for discussion.

00:14:33.640 --> 00:14:36.720 align:middle line:84%
The other thing is that one
person in the polio program

00:14:36.720 --> 00:14:39.900 align:middle line:84%
explained the importance
of data sharing

00:14:39.900 --> 00:14:42.750 align:middle line:84%
by saying, "Better
interpretation comes

00:14:42.750 --> 00:14:47.090 align:middle line:84%
with collective wisdom
and experience."

00:14:47.090 --> 00:14:50.930 align:middle line:84%
So implementation science
in today's course themes

00:14:50.930 --> 00:14:52.490 align:middle line:90%
has evolved.

00:14:52.490 --> 00:14:56.420 align:middle line:84%
The importance of incentives--
how good is the data?

00:14:56.420 --> 00:14:59.630 align:middle line:84%
The promise and
limits of technology--

00:14:59.630 --> 00:15:02.930 align:middle line:84%
how and when can
technology improve data?

00:15:02.930 --> 00:15:04.890 align:middle line:84%
We've shown some
examples of that.

00:15:04.890 --> 00:15:07.320 align:middle line:90%
Then the portals of politics--

00:15:07.320 --> 00:15:11.210 align:middle line:84%
who has the power over
data and the decisions?

00:15:11.210 --> 00:15:12.900 align:middle line:90%
We've discussed that as well.

00:15:12.900 --> 00:15:17.300 align:middle line:84%
So we'd like to link back
to implementation science

00:15:17.300 --> 00:15:22.130 align:middle line:84%
and we'd like you to think of
the following for each topic.

00:15:22.130 --> 00:15:25.310 align:middle line:84%
And then we'd like you to
summarize the implementation

00:15:25.310 --> 00:15:28.670 align:middle line:84%
problems for data, for
decision-making, provide

00:15:28.670 --> 00:15:32.690 align:middle line:84%
examples of the root
causes of the problem,

00:15:32.690 --> 00:15:35.570 align:middle line:84%
and then also
implementation strategies

00:15:35.570 --> 00:15:41.380 align:middle line:84%
deployed to address the
problems based on the contexts.

00:15:41.380 --> 00:15:44.130 align:middle line:90%
[MUSIC PLAYING]

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