By Chika Hayashi, UNICEF

1. Data Curation and analysis: practices,
challenges, and opportunities for
better use of nutrition data
11 February 2020
SESSION 2

2. 11 February 2020
Adapted from Piwoz, E., Rawat, R, Fracassi, P, Kim, D. Strengthening the Nutrition Data Value Chain for Accountability and Action: Progress, gaps, and next steps

3. 11 February 2020
Data curation
Aggregate, structure and report data
Aggregate/
Collate
Structure Report
Collect and
combine primary
data sources from
nutrition and other
relevant sectors
Assess, organize,
arrange and relate
the different data
sources to allow for
a comprehensive
understanding of
the situation
Process the data
into information
and summarize
information at
regular intervals.

4. 11 February 2020
Data curation
Aggregate, structure and report data
Aggregate/Collate – at the country level
Collect and combine primary data sources from nutrition and other relevant sectors
HMIS
Interoperability
Nutrition data in surveys
SMART, National Nutrition
Surveys etc.
Data from agricultural
sector
Data from other nutrition
sensitive sectors- WASH,
social protection etc.
Open Access to Data
Quality Assured Data
Privacy RelevanceSecurity
Balancing act
Easily
Distributable
Easily
Integrated
Robust
Documentation

5. Using standard templates UNICEF conducts in-depth
review of data sources to validate quality
Criteria for review of nutrition data to ensure
consistency and comparability of data across countries
and over time
• Sampling methodology
• Survey coverage and target group
• Plausible estimates and trends
• Adherence to global indicator definition and
methods guidance
Data curation
Aggregate, structure and report data
Structure – at the global level
Assess, organize, arrange and relate the different data sources to allow for a comprehensive understanding of the situation

6. Harmonizing variable names and labels to facilitate global and regional
reporting
UNICEF produces nutrition-focused standardized datasets from microdata including:
So far over 700 surveys have been standardized
Data curation
Aggregate, structure and report data
Structure – at the global and country level
Organize, arrange and relate the different data sources to allow for a comprehensive understanding of the situation

7. 11 February 2020
Data curation
Aggregate, structure and report data
Structure – at the country level
Organize, arrange and relate the different data sources to allow for a comprehensive understanding of the situation
Common Variables Consistent Metadata
Where possible,
common variable
names should be
adopted e.g. Facility
ID number, Region,
Town
Consistent Code lists
Where possible,
variables should use
the same code lists
e.g. a consistent list of
towns, regions, facility
lists.
Where possible,
Entries or entire All
datasets should have
similar metadata e.g.
unit of analysis, data
provider, collection
mechanism and be
timestamped,

8. 11 February 2020
Data curation
Aggregate, structure and report data
Report
Process the data into information and summarize information at regular intervals.
At the country
level
At the global
level

9. 11 February 2020
Synthesize
data
Build tools
Build
models
Combine
information
and perform
analysis to get
an overview of
the situation.
Tools to visualize the
information to more
easily unearth insights
Analyze how to drive
change or understand
bottlenecks
Systems and models
to assist in
prioritization,
optimization and
prediction
Data Analysis
Synthesize data, build analytical tools & models to
derive insight

10. Data Analysis
Synthesize data, build analytical tools & models
UNICEF produces various nutrition databases as a global public good including
• Time- series data
• Data for various sub-populations
Low Birthweight,
Birth weighing
Infant and Young Child
Feeding
Child anthropometry Micronutrients
Analysis yields valuable information to help
• track trends over time
• identify the populations most at risk
Synthesize data – Global and Country
Combine information and perform analysis to get an overview of the situation.
Residence Socioeconomic Status Sex

11. Trajectory, levels and trends Is the progress across the board and are the pockets of inequity?
Comparison between countries Comparison within countries
Data Analysis
Synthesize data, build analytical tools & models
Build tools - at the global level
Tools to visualize the information to more easily unearth insights
Global Tracking Tool

12. Data Analysis
Synthesize data, build analytical tools & models
Build tools - at the country level – e.g. DHIS dashboard
Tools to visualize the information to more easily unearth insights

13. Data Analysis
Synthesize data, build analytical tools & models
Can help fill in data gaps
• Helps plug in gaps in between survey data – countries have the most up to date
information to make decisions
Build models – at the global level
Understand how to drive change or understand bottlenecks
Systems to assist in prioritization, optimization and prediction
What Questions are we trying to answer?

14. 26 February 2020
Build models – at the country level
Understand how to drive change or understand bottlenecks
Systems to assist in prioritization, optimization and prediction
Data Analysis
Synthesize data, build analytical tools & models
• Identify determinants of a nutritional problem – Root cause analysis
• Predict how nutritional status may change over time – implications for
resource allocation
Bottleneck Analysis
What Questions are we trying to answer?

15. Basic Challenges and
Recommendations
Data Curation
Restricted access to micro-
data/ issues with data
privacy
Invest in data management
systems with data sharing
policy at the country level
Model details are hard to
understand
Make detailed
documentation available
Make models easy to use
and invest in stakeholder
understanding of the
details, continued
interactive consultation
Lack of statistical capacity
Workshops and ongoing
support for analytical
capacity at the country
level; incentives for staff
retention;
Make codes and scripts
available as a public good
Inconsistent Code lists,
Variables etc.
Harmonized variables with
centrally managed
reference data (national
data standard)
Insufficient
Metadata
Robust metadata
with clearly labelled
versions and
timestamps
Underutilization of
data visualization tools
Make available
standard visualization
templates
Automate production
of dashboards
Data Analysis

16. Thank you!
More information on
http://add your website

17. Outline
• What is data curation?
• Understanding data curation in the
context of regional and global nutrition
monitoring.
• What is data analysis?
• Challenges and recommendations
11 February 2020