Implications for data analysis (1/4)

The objective of the analysis is defined by decision makers, not by the data analysts. A dialogue between data analysts and decision makers is important to specify the objective and clarify the question.

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Data analysts need to evaluate whether it is feasible to answer a specific question given the data availability and its quality. This has also implications in terms of the data analysis methods that may be particularly suited or not (see pages 6 to 9 in this section).
The idea behind the information platforms is to make better use of existing data. Therefore, questions that require the collection of new data cannot be answered by NIPN teams. It may however be possible to partially answer the question (by formulating sub-questions) or alternatively, to explore whether a third party (e.g. a research institute) would be able to conduct a specific study/research to collect these data.
Qualitative data, though not the primary focus of the information platforms, are an important source of information to better interpret results from quantitative data analysis.

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The results of the analysis must be produced in a timely fashion, meaning that the results are available in the window of opportunity of decision-making by policy makers.
For example, if decision makers need information to finalise the new Multisectoral Plan of Action for Nutrition by the end a given year, it is useless to produce a comprehensive analysis 3 months later. It is more important to produce a less comprehensive analysis which is ready in time to inform the decisions.
The data analyst should not be guided by the question “How much time is needed to produce the results?” but by “What results can realistically be produced within the proposed timeframe?”.

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