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DATA COLLECTION AND ANALYSIS TOOLS FOR
FOOD SECURITY AND NUTRITION
data analysis can be opaque for three reasons:
because of deliberate decisions by the owner
or creator of the tool to protect intellectual
property, because of a lack of technical skills
and knowledge among the users of the tool
and those who are bound to be affected by
the decision, and because these systems
may actually be “intrinsically opaque due to
the characteristics
of many state-of-the-art
machine-learning methods” (Oliver, 2021 p.61).
DEFINING DATA GOVERNANCE
Data governance has been defined in different
ways. In this report, we define it as a:
Globally relevant set of principles, strategies,
policies, regulations and standards developed
by institutions to collect, manage, share and use
data.
By establishing
rules and standards, data
governance aims to enable the broadest possible
data sharing, so that data can be used effectively,
while ensuring the protection, integrity and
transparency of data systems. To be effective,
institutions responsible for developing data
governance frameworks, including international
organizations,
national governments, academia,
the private sector and civil society organizations
(CSOs), must act in a coordinated manner.
These institutions must reinforce collaboration
to establish and maintain data systems that can
inform the design of interventions and policies
needed to address FSN challenges. We address all
these issues in Chapter 5.
A CONCEPTUAL
FRAMEWORK TO INFORM
DATA
COLLECTION AND
ANALYSIS TOOLS FOR FOOD
SECURITY AND NUTRITION
One of the main objectives of this report is to
support more effective FSN decision-making,
by providing guidance on the most appropriate
ways to use and analyse data. In order to do this,
it is necessary to, first, frame the many factors
which influence FSN (and
therefore determine
which data are needed for decision-making) and,
second, emphasize the data-informed decision-
making process from a conceptual standpoint
Drawing on previous work by the HLPE-FSN
and others (Bronfenbrenner, 1979; HLPE, 2017,
2020), our conceptual framework illustrates
how multiple levels of factors influence the food
security and nutritional status of individuals and
aims to help guide data collection and analysis.
The conceptual framework can help define
pathways to build evidence for decision-making
through
setting research priorities, with the goal
of enhancing the FSN of individuals, households
and communities.
One of the challenges for FSN is the complexity
of the concept of food security. As noted by the
HLPE-FSN:
The concept of food security has evolved
to recognize the centrality of
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