What happened and what’s next for DPPD

Where we started

Work in the data for development space tends to focus on the aggregate — the collective behavior of individuals and groups — while discarding individual observations, including outliers.

Map of DPPD pilots
Overview of the seven DPPD pilots

What we learned

We learned quite a lot over the past two years and were able to document some of these learnings in a report and an academic paper:

  • DPPD is not universally applicable: Digital data is rarely available at the level of individuals, mainly due to the need to de-identify and aggregate digital observations to preserve privacy. This makes DPPD better suited to development challenges where aggregated units are of relevance, e.g. geographic units such as villages or urban blocks.
  • Niche know-how is needed: A combination of country-specific domain knowledge and domain-specific data knowledge is crucial in the very early stages of a DPPD project. The former is needed to understand the normative behaviors in certain geographies and the contextual and structural factors that enable the successful practices applied by positive deviants. The latter is required to identify relevant performance indicators as well as mapping out suitable data sources that could be used.
  • Adopt a holistic approach when understanding deviance: DPPD provides an opportunity to uncover factors beyond the individual positive deviant that could be modified and transferred, such as access to markets or the provision of government subsidies. This can inform the design of nuanced interventions and thereby increase their effectiveness and contextual fit.
  • Earth observation data is the low hanging fruit for DPPD: Earth observation data turned out to be the most viable non-traditional data source that can be employed in the DPPD method. It can be acquired at low cost, over long periods of time, and thanks to recent advances in remote sensing technologies, it is witnessing a growing availability at a high resolution, including coverage of lowest-income countries where other datasets are lacking.

What we achieved

We’re excited about the progress we made over the past two years. While there remain open questions to investigate to improve the method, we were able to demonstrate its viability to identify locally sourced solutions by combining non-traditional and traditional data sources.

The challenges we faced

We view the pilot projects as a crucial, successful first step. However, we did stumble a few times along the way. One obstacle was that we were not able to identify key differentiators of positively-deviant behavior in some of the pilot projects. Data quality could be to blame. For example, the control data in one of the pilots was relatively old (dating to 2012), and we found that it falsified our predictions (of positive deviance) when we realized its inaccuracy after going into the field.

The five stages of the DPPD method

What’s next

We have taken an important first step in demonstrating that digital data can indeed be used to identify positive deviants, and to some extent scale their practices. ​​We have also shown that digital data can be used not only for top-down identification and tackling of development challenges, but as a tool to identify and diffuse local practices and strategies.

A new setup

We are outlining a series of questions to improve and adapt the method and are moving into a new setup that will allow us to further this investigation. A network of researchers and practitioners applying the tools and learnings from the DPPD handbook will connect with and learn from one another through a dedicated community of practice.

Get involved

We hope you will continue to engage with us through the next stage of this endeavor. Here’s how you can get involved:

  • Apply the method: explore the application of DPPD in your work. Learn about it from our paper, see how others are doing it on our blog, use the handbook to apply it yourself, or message us for support.
  • Provide data access: we are interested in developing DPPD use cases using social media and mobile data. If you have access to such data and are interested in the method, please reach out to us.
  • Contribute with your expertise: are you an expert in data for development or community participatory approaches? Join the DPPD community or write to us to learn more.
  • Support us: Reach out to learn how to partner with us.

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Data Powered Positive Deviance DPPD

Data Powered Positive Deviance DPPD

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We are an international collective that is dedicated to utilizing big data to find effective locally developed solutions to complex problems.