Data collection and analysis tools for food security and nutrition

partnership is the Integrated Food Security 
Phase Classification (IPC), an initiative that is 
funded by international collaborators but still 
enables national ownership (
SEE BOX 13
).
Other initiatives focusing at sustainable food 
systems include components to enable data 
collection for monitoring and evaluation (
SEE FOR 
EXAMPLE, BOX 33
).

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BOX 33:
THE GLOBAL AGRICULTURE AND FOOD SECURITY PROGRAM (GAFSP)
As an example of coordination and institutional arrangement for monitoring and evaluation, the GAFSP provides 
funding and technical assistance to support implementation of country-led initiatives, giving priority to those with 
evidence of stakeholder participation, including producer organizations (PO) and relevant civil society organizations 
(CSOs), from project design to implementation (GAFSP, n.d.).
More recently, not-for-profit social enterprises 
such as 
Statistics for Sustainable Development
 
(Stats4SD) have ventured into research, 
statistical support and capacity building for 
monitoring and evaluation (M&E) of development 
interventions with the aim to promote the 
better use of statistics for decision-making. 
The 
Intergovernmental Science-Policy Platform 
on Biodiversity and Ecosystem Services
(IPBES) 
is an independent intergovernmental body that 
aims to strengthen the science-policy interface 
for biodiversity and ecosystem services for 
long-term human well-being and sustainable 
development. IPBES, whose membership is 
open to all UN-member countries, has specific 
objectives on strengthening knowledge, 
facilitating data sharing and catalysing the 
generation of new knowledge. Specific attention 
is paid to indigenous and local knowledge 
systems.
GREATER ATTENTION TO DATA 
QUALITY ISSUES
Financial and institutional support from 
policymakers to collect good-quality data, 
adhering to the four foundational principles of 
findability, accessibility, interoperability, and 
reusability (FAIR), could be forthcoming if the 
benefits of collecting good quality data, as well as 
the cost of insufficient data quality, is internalized 
and well-communicated. This requires champions, 
in each institution involved, who will provide 
sufficient drive and traction for the initiation and 
sustenance of such data collection efforts.
Enforceable regulatory frameworks also provide 
guidance to facilitate better coordination 
between agencies and the involvement of 
stakeholders. This may also provide an incentive 
for governments to generate, analyse and utilise 
timely and relevant data and support open access 
in line with FAIR principles. The establishment 
of adequate legal and regulatory frameworks 
will facilitate international and cross-border 
collaboration as data collection is subject to 
local laws and regulations and may subtly vary 
between countries or even regions. In the absence 
of regulatory frameworks that codify the need 
for specific data, successful collaborations such 
as the EAF-Nansen Programme are also limited 
in their reach. The use of new methods such as 
machine learning could be associated with black 
box models where the algorithms may not be 
transparent or easily understandable. Appropriate 
regulatory frameworks will establish the 
requirement for documentation and transparency 
of these efforts to adequately understand and 
interpret the results generated, ensuring power 
balance and equality in the process.
A forum to build mutual understanding on FSN data 
and statistics, governance issues and a consensus 
on the principles and norms that should guide 
resource allocation among the stakeholders could 
be proposed as a step in facilitating standardisation 
and harmonisation efforts.
CHALLENGES TO DATA 
GOVERNANCE FROM DATA-
DRIVEN TECHNOLOGIES
Technological innovation opens the door to new 
data sources and increased data volume but 
may also divert attention from strengthening 
data collection procedures, as well as from 
identifying data governance capabilities and gaps. 
According to a recent study, “this underscores 

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5 
INSTITUTIONS AND GOVERNANCE FOR FSN DATA COLLECTION, ANALYSIS, AND USE
the need to better exploit complementarities 
between traditional and alternative data sources 
and methods, which will require both technical 
solutions as well as creative institutional 
arrangements that foster collaboration and value 
addition” (Carletto, 2021 p. 721).
Data-driven technologies may facilitate data 
collection, processing and sharing, as they may 
facilitate more effective collaboration in data and 
statistics. Digital technologies can also favour 
timeliness in data availability and can facilitate 
the performance of quality checks (World Bank, 
2021). However, these technologies may lead to 
higher asymmetries in data access, for example, 
when data are transferred from data contributors 
to data processing companies that control further 
access and use of these data (World Bank, 2021). 
In some cases, data collected in one country are 
processed in cloud-based facilities operated by 
other countries or private companies, creating 
dependencies and risks for data privacy and data 
access (World Bank, 2021).
Finally, while open data can facilitate access to 
data, it is not synonymous with universal data 
access. The ability to access open data is limited 
to those with access to digital infrastructures 
and digital technologies, and who possess the 
required technical skills.
SOLUTIONS TO ENHANCE 
FSN DATA GOVERNANCE
STREAMLINING TRANSNATIONAL 
AND NATIONAL DATA GOVERNANCE 
FOR FSN
The development of improved knowledge systems 
to inform more effective policy action in FSN 
requires special attention to governance issues. 
Furthermore, effective collaboration both at 
country and international levels is essential to 
address data governance challenges.
International standards for FSN data governance 
and data sharing should be further developed. 
Enhanced coordination of country efforts can lead 
to a more efficient way of collecting FSN data, 
avoiding fragmentation and duplication of data 
initiatives. There are international institutions 
already well-positioned to lead such initiatives 
and provide country-support. FAO can play an 
important role in facilitating the integration 
of datasets and support data sharing and 
data governance. Digital technologies create 
opportunities to establish data platforms that 
connect data providers and data users, while 
international organizations are essential to ensure 
that data generation meets quality standards and 
builds data trust.
Some global initiatives to develop international 
standards and enhance coordination are ongoing, 
but implementation at the country level is slow. 
New institutional arrangements are being 
promoted in some countries to facilitate the 
effective integration, sharing and reuse of FSN 
data. In the framework of transnational data 
standards and protocols, governments should 
develop data strategies including regulations 
for data protection, sharing and use as well as 
mechanisms to enhance collaboration on FSN 
data at national and subnational levels.
INCLUSIVE APPROACH TO DATA 
GOVERNANCE
Inclusive and multi-stakeholder approaches 
are critical for data governance and sharing. 
Governance mechanisms established through 
dialogue between stakeholders (data contributors, 
collectors, processors, providers and users), 
whether state or non-state, increase trust, which 
is a precondition for effective collaboration and, 
therefore, for implementing feasible governance 
solutions.
INCREASING TRANSPARENCY 
AND GOVERNANCE OF OFFICIAL 
STATISTICS FOR FSN
National statistical agencies generating datasets 
on FSN should pay special attention to:
•
harmonization of concepts and indicators;
•
coordination both with international and other 
national institutions producing data (e.g., national 
and international sources of food prices and 
markets) to ensure comparability of data;

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•
governance mechanisms to enhance data 
sharing and usability, while respecting the 
confidentiality of personal and sensitive data.
Although there are initiatives to coordinate data 
collection and governance, greater internal and 
international coordination is needed to avoid the 
proliferation of disconnected data initiatives that 
can lead to data gaps and duplication. Improved 
coordination may reduce the burden of collecting 
data by focusing on the essential datasets needed 
to promote FSN and integrating across data 
sources to overcome the limitations of individual 
data sources. Therefore, setting priorities and 
adopting agreed data protocols will help to further 
develop and maintain FSN data systems.
The FAIR and CARE data principles have the 
potential to address some of the governance 
challenges. The adoption of these principles 
should be promoted across the global research 
community.
However, more effort is needed in research 
areas that are currently under-covered. Funding 
agencies should prioritise research on optimal 
dietary targets and cost-effective policies to 
achieve them; monitoring and evaluation of health 
indicators and policy outcomes; engagement 
with communities and active public-private