The challenges and how to overcome them (1/4)
Engaging in a data landscape exercise is not without challenges. This section highlights four key challenges that country teams may face when designing and carrying out a data landscape exercise and it proposes pragmatic solutions for each of the challenges.
- Challenge n°1: The scope of the data landscape exercise
- Challenge n°2: Access to datasets for the data landscape exercise
- Challenge n°3: Harmonisation of indicators for the data landscape exercise
- Challenge n°4: Cost, time and resources for the data landscape exercise
Challenge n°1: The scope of the data landscape exercise
Although the data landscape exercise could be executed at central or decentralised level, the exercise is vast enough at central level. Therefore it is recommended to limit the focus at the central level. Countries with a highly decentralised system or with a specific interest in one district may want to expand the exercise to the decentralised level (topic not covered here). The domains for which data is required are the following:
- Nutrition outcomes
- Basic, underlying and immediate determinants of nutrition
- Nutrition-specific and nutrition-sensitive interventions / programmes
- Finance for nutrition
‘Finance for nutrition’ data is included here because analysing which investments or budget are allocated to which activities is the first element of the nutrition impact pathway (see section 2.3) and a crucial element in policy decision-making (see the SUN Budget Analysis for Nutrition).
The complete list of relevant datasets and indicators (this section, page 4) is potentially very vast and probably too big for the scope of a short-term exercise. It is recommended to narrow down the scope of this exercise to keep it feasible. However, each country will need to decide on the scope of their data landscape exercise as there is no “one-size-fits-all” solution.
Two options are described here:
- Option 1: Limit the exercise to the level of the datasets (exclude the indicator matrix)
- Option 2: Include datasets and indicators matrix in the data landscape exercise
(see below for more information on options 1 and 2).
Creating the indicators matrix is indeed time-consuming and resource-demanding, but can be of particular interest when:
- teams want to identify a list of key nutrition indicators to monitor (case of Niger);
- A list of key indicators for nutrition has been set in the main nutrition policy documents (case of Guatemala);
- Time and resources are available: the indicators matrix can be a practical tool to quickly ascertain where to find specific indicators to answer a specific nutrition policy-relevant question.