Data collection and analysis tools for food security and nutrition

particular the poor and people in vulnerable situations, including 
infants, to safe, nutritious and sufficient food all year round”, while 
Target 2.2. reads: “By 2030, end all forms of malnutrition, including 
achieving, by 2025, the internationally agreed targets on stunting and 
wasting in children under 5 years of age, and address the nutritional 
needs of adolescent girls, pregnant and lactating women and older 
persons”. See 
https://sdgs.un.org/goals
 for a full description of the 
SDGs, targets and indicators.
3 Throughout the report, the term agriculture refers to the broader 
set of activities that involve the use of natural resources (land, water, 
forests, fish) to produce foods.
4 Namely, to:
•
Highlight the benefits of using data and the opportunity costs of 
not using data for decisions
•
Illustrate initiatives that have encouraged evidence-based 
decisions in agriculture and food security across the public, 
private, and academic sectors as well as approaches that have 
not worked.
•
Identify specific high priority gaps in data production and analysis 
not covered by ongoing initiatives.
•
Identify the barriers impeding quality data collection, analysis, 
and use in decision-making.
•
Provide insights into how to ensure data collection and its 
utilization give voice to the people most affected by policies 
stemming from that data, including farmers and other food 
producers (CFS 2019/46/7, 2019, p. 9).
See CFS’s Multi-Year Programme of Work at 
https://www.fao.org/3/
na703en/na703en.pdf
.

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INTRODUCTION
most countries still do not conduct regular 
household and farm surveys, do not meet the 
minimum data requirements, lack sustainable 
data systems, and have insufficient capacity to 
analyse and use the data at their disposal (CFS 
2019/46/7, 2019, p. 8). 
Therefore, 
while many may live in places where 
data and information flow with unprecedented 
mass and speed, many countries still lack 
sustainable data systems and related 
capacities
.
Rather than recommending 
from the onset additional investment in data 
collection for food security and nutrition, we 
first propose in-depth ways of thinking about 
data collection and analysis tools
to ensure full 
and proper use and re-use of existing data
.
The CFS presented the following additional 
rationale for this report: 
 Addressing the gap in quality data is also 
essential to monitor progress and understand 
where the world stands in achieving its shared 
goals – the SDGs. Custodian UN specialized 
agencies were identified for each SDG indicator 
to ensure that robust, global statistics were 
provided to measure progress in achieving 
the 2030 Agenda. However, the success of the 
SDGs rests largely upon strengthening data 
collection and statistical capacity-development 
at national level, including capacity building 
that strengthens coordination among national 
statistics offices (CFS 2019/46/7, 2019, p. 8). 
As of this writing, there are still many countries 
in the world where training is required so that 
there are sufficient human resources to properly 
interpret, process and digest new data in the 
various forms in which they are continuously 
generated, stored and distributed. Of particular 
concern is that 
this is true also for the scientific 
community
, where the more traditional research 
tools are being challenged by emerging ones,
5
which have not yet sufficiently permeated 
academic curricula. This brings to the fore 
the 
need to invest in capacity development at all 
levels, starting even in primary school and 
continuing through specialized training of 
professionals working in public and private 
institutions dealing with data
.
This report has been designed to respond to the 
call of the CFS to Support the process of laying 
the groundwork for informed decision making, 
setting standards for improved data-driven 
policies around food security and nutrition, and 
strengthening effective monitoring, review and 
follow-up to deliver SDG 2 (CFS 2019/46/7, 2019, 
p. 8).
To begin laying this groundwork, the report 
was developed with an understanding that food 
security and nutrition (FSN) policymaking at 
global, national and local levels, involves the 
use of data, either new or existing, to reach 
effective, evidence-informed decisions, and 
that this involves a distributed process, where 
responsibilities are held by different individuals 
and institutions, at different levels.
The report is organized around six chapters: 
Chapter 1 
defines key concepts around the data 
collection and analysis tools that are presented 
throughout the report. It provides operational 
definitions of 
data