The preparation of a data landscape report (2/3)

3. Description of the main information systems and datasets available

This is a description of the main information systems with nutrition-relevant information. It addresses questions such as:

  • How is data collection organised?
  • What systematic data quality control mechanisms are applied?
  • Which indicators are collected?
  • What are the sampling methods, exact dates of data collection, and population groups covered by the data?
  • What is the procedure to access the datasets?
  • Which outputs are produced on the basis of this data? For whom and how are they used?
    One data provider can manage one or more information systems (e.g. a National Statistical Office manages the Demographic and Health Survey, as well as SMART Surveys).

Typical information systems include: Education Management Information System, Health Information Routine Data (DHIS2), Water Management Information System, Household Income and Consumption Surveys, Health and Nutrition Expenditure Surveys, Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), SMART Nutrition Surveys, and Integrated Food Security Phase Classification Database (IPC)…

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4. Indicator matrix

Ideally, an Excel file is populated with information for all the indicators included in the data landscape (see below for an example of an indicator matrix).
The typical information collected on each indicator includes:

  • Name of the dataset(s) to which the indicator is attached
  • Definition of the indicator (variables used to generate the indicator)
  • Period of data collection
  • Year of data collection
  • Data collection sampling method (e.g. survey, routine)
  • Geographical coverage
  • Organisation(s) that collect and manage the data

Note that indicators from different datasets may not be directly comparable (Challenge n°3, this section, page 8).

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Example of indicator matrix
The indicator matrix in an Excel file (as in the example provided below based on the experience of Niger) is an ideal output of a data landscape exercise. It describes which datasets contain for instance an indicator identified to answer a policy-relevant question. However, as detailed in Challenge n°1 (this section, page 6), describing all indicators available in all datasets that are of interest to NIPN can be a massive piece of work, which is not always necessary. Côte d’Ivoire, for example, decided to describe the information systems and datasets of interest to NIPN without describing precisely all indicators available in these datasets, and did not develop an indicator matrix.
Download an example of indicator matrix in Excel format (based on the experience of Niger).
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