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Data for development: 5 takeaways

Data is big. It is a trending topic. When ICCO – back in 2012 – started testing smartphone-based interviews in Medan, North Sumatra we did not foresee the impact it would have on our work.

Data for development: 5 takeaways

Discover 5 takeaways

Today ICCO uses  Akvo Flow in most of the 35 countries where we implement programs. While our first experiments in Medan focused on our food security programs, we broadened our scope to economic empowerment and emergency response programs. The same happened in other organizations, leading to a trend of being open and transparent about data.

Gathering, combining and analyzing data comes with opportunities and challenges. Here are 5 important takeaways from our experience in the past years:

Train your enumeratorsTrain your enumerators

Training of enumerators is a key aspect of gathering high-quality data, cleaning rubbish data and structuring answering options. An example is the Dietary Diversity Survey that we use. We ask women what they ate for breakfast, lunch and dinner (and in-between) and categorize their answers in food groups. The enumerator should interpret the answers and make sure we do not miss out answers, as the number of food groups tells us something about the nutritional value of meals eaten.

Invest in Statistical knowledge Invest in statistical knowledge

Together with Akvo we try to deepen our understanding of data. For example, we are experimenting with Multiple Correspondence Analyses.  We figure out which elements in our datasets are the most influential in particular outcomes. In this way we found out that being a member of a self-help group (and for how long) is a determining factor for most of the other answers given by the respondent. Similarly, we make more and more use of Geographical Information Systems to discover particular geographic patterns. In our Assam Livelihood program we found clear differences between villages; differences we would never have discovered by looking at a spreadsheet file.

Mistrust your data Mistrust your data

In Ethiopia we recently gathered an interesting dataset including food status, age and disability. We found – in some cases – striking data: some older aged people seemed to be more food secure than younger peers. We did not take this for granted – we went back to the communities and discussed the outcomes of the data with them in focus group discussions. And it turned out that some elderly people indeed had higher incomes and thus better opportunities to buy food.

Translate your data to revised programming Translate your data to revised programming

Data are no more than a supportive element in your programming. It helps you in getting better information about what works and what does not. It could guide you in making other decisions.

Be aware of new developmentsBe aware of new developments

Finally, you are not alone.  You should therefore be careful in reinventing the wheel. That’s why ICCO attended the International Open Data Conference in Madrid last year, and participates in the Partos and ACT Alliance data group. Besides we cooperate with both knowledge institutions (e.g. CDI) and technical parties (such as Nelen & Schuurmans and Vandersat).

Martijn Marijnis, Monitoring & Evaluation advisor

Further reading: https://www.icco-cooperation.org/en/blogpost/how-to-effectively-collect-dat-aon-food--nutrition-security

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