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The use of a data landscape exercise

The data landscape exercise can be used for a number of purposes:

1. To establish which data is available, accessible and of sufficient quality to respond to a relevant nutrition policy question

The data landscape exercise is conducted as one of the first activities when an NIPN is being set up in a country and prior to the identification of policy-relevant questions (see section 2.1).
The data landscape provides an initial picture of the available and accessible data and its quality. It helps the NIPN data experts to quickly ascertain which survey instruments have been used to collect nutrition-relevant indicators and which institutions or individuals to contact to access the data. The landscape exercise will also contribute to building and maintaining close connections with the multi-sectoral data providers and facilitate access to data in the future.
The initial data landscape exercise will be used during the process of formulating policy questions to provide first-hand basic information for the NIPN data experts to decide whether a formulated policy question can be answered or not (see section 2.3).
Yet, it is likely that some further investigation may be required to reach a final decision on whether policy questions can be answered as the data landscape exercise will never be able to cover 100% of all available data:

  • Policy questions can be very diverse and may require investigation of indicators that have not been included in the exercise.
  • To effectively assess the data quality, the dataset needs to be manipulated, which cannot be done during the data landscape exercise.
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2. To initiate a process to progressively update the data landscape

The data landscape exercise should be a dynamic process. The initial exercise provides an initial picture of the data landscape. As new surveys are conducted, the data landscape can be updated. While the NIPN approach is developing and maturing within a country and as more and more policy questions are being formulated and answered, the data landscape will expand and provide a more complete picture.

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3. To provide actionable recommendations to improve the nutrition information system

The data landscape may offer new insights into the structure and functioning of the nutrition information systems. In particular, it may:

  • Identify and highlight gaps in data availability and lack of capacity to collect nutrition-relevant indicators, which could lead to recommendations on how to fill those gaps.
  • Highlight lack of harmonisation of indicators collected by the various instruments or systems. For example, the sampling method or the geographical unit may vary between information systems. The data landscape exercise can identify these differences and advocate for harmonisation or clarify (in-)comparability of indicators.
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4. To provide input into the NIPN data management strategy

One of the aims of NIPN is to build a central repository of multi-sectoral datasets or to support an existing repository. The data landscape exercise will provide key information regarding the existing information systems, how they communicate with each other, and where to locate required indicators. Such information is important for the design of an appropriate NIPN central repository.

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