Data Empowerment
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Data Empowerment

How can we ensure that data powered positive deviance empowers people?

Illustration: Vincent Beck

By Andreas Pawelke & Richard Heeks

A new way to empower?

Data powered positive deviance (DPPD) is a method that relies on both traditional and non-traditional data to identify grassroots solutions to complex development problems, such as deforestation, food insecurity and gender-based violence.

The DPPD pilot team in Ecuador interviews a cattle farmer in Sucúa Cantón, Morona Santiago province to understand the practices that positive deviants use to conserve forest. Photo by Ricardo Araguillin.

Developed by a group of researchers and practitioners* DPPD seeks to identify “positive deviants” — individuals and groups who are able to achieve significantly better outcomes than their peers despite having similar resources and constraints — and scale the practices and strategies they employ.

DPPD and data empowerment — the process where people take control of their data to promote their own and their societies’ wellbeing — are based on similar beliefs when it comes to how data initiatives, in international development and beyond, should be designed and implemented.

DPPD is about acknowledging the value of locally-sourced solutions and the agency of positive deviants and their communities, and respecting the right for positive deviants to decide whether they want to share their practices or not. Similarly, treating people as active users of data instead of passive data providers is at the heart of data empowerment.

There is, however, no guarantee that the DPPD method will be applied in a way that empowers people. In fact, without careful planning, it may even exacerbate existing power inequalities. Modified from our five ways to ‘do’ data empowerment, here is a list of things you can do to integrate data empowerment practices into your DPPD project.

1. Do no harm

Before you do anything else, assess the potential risks and benefits of applying the DPPD method. Consider how you will use data responsibly to minimize the risks related to the use of sensitive data, especially when that data is personally identifiable.

If you think the use and analysis of certain data might disadvantage or even harm the positive deviants, or their communities and wider social systems, it is important to adjust the project accordingly or ultimately to not move forward with it. Just because the DPPD method may be suitable (the right method for a particular problem) and feasible (data, capabilities and resources are available) does not mean it should be applied.

To protect the privacy of individuals and communities involved, ask yourself the following questions:

  • Is the data source of a sensitive nature, e.g. does it contain personally identifiable information?
  • Are safeguards in place for secure data access and processing?
  • Has consent been given (directly or indirectly) by the data subjects to use their data?
  • To whom can the identity of positive deviants and non-positive deviants be revealed, if at all?

These questions serve as a starting point; the specific assessment and related questions will depend on your project scope and the data you plan to use for the analysis.

2. Understand and address power dynamics

DPPD projects require a broad range of expertise from different stakeholders to succeed. It’s important to involve these stakeholders early on in the process and understand how they relate to the problem at hand. You must be particularly mindful of the ways that DPPD projects could empower one group of people while reducing the power of another.

Take this hypothetical example: a development organization successfully identifies positive deviants and documents their practices using the DPPD method. But instead of working with the positive deviants to enable members of their communities to apply their practices and strategies, they simply document these practices and use them for their own programming. While there could be good reasons to do this in particular situations (e.g., positive deviants are not able to train others in applying their practices, scaling local solutions requires policy interventions beyond the individual level), doing so at scale might gradually lead to knowledge of these successful local practices (and the data that was used to identify them) to be concentrated with a small number of powerful actors.

What’s more, as you implement the method, be aware that existing power dynamics could impact why and how positive deviants are more successful than others. The analysis of deviant cases should therefore be “power-aware”. Ask the question of how power relations and the underlying mix of social, political and economic relationships impact the ability of positive deviants to succeed. This is likely not captured by the quantitative data used in the first steps of the DPPD method, but must be researched and understood during the interviews with potential positive deviants and their communities in Stage 3 of the process (Discover Underlying Factors) as shown in the DPPD method figure below.

The fives stages of the DPPD method

3. Treat people as active users, not passive providers

When conducting the field research in Stage 3, it’s best to use community-based participatory research methods, such as discovery and action dialogues, participatory sketching or community mapping. Identify opportunities to engage people in interpreting the data that were collected about them to encourage dialogue and debate around the challenges they face and possible means to address them.

As Positive Deviance pioneer Monique Sternin and colleagues put it:

“A way to combine the advantages of big data with the “traditional” PD approach is to consider the community from which the data are “harvested” as partners or even co-researchers in this endeavor. Participatory Action Research (PAR) format offers such an opportunity³. PAR differs from traditional research in that the research participants act as co-researchers who are involved in the entire decision-making process, from initial conception to data collection and analysis and the development of action oriented feasible recommendations.”

Once one or several positively-deviant practices have been identified for scaling (which is part of Stage 4), it is crucial for positive deviants and the community to be at the center of, and closely involved in, every step of the process. Ensure the active participation of those who have developed and own a given solution (the positive deviants) and those who stand to benefit from adopting a positively-deviant practice (non-positive deviant members), as well as those who might have influence over the design and implementation process.

4. Make the data you collect accessible to others

Make the data you’ve collected publicly accessible, if it can be done without running the risk of doing harm, as open data for others to use. Another option is to provide access only to those communities, stakeholders and individuals you’re engaging with as part of the DPPD project. Similar to the shareback sessions run by Data Zetu, this shifts community members from the passive role of producers of data to the active role of users.

DPPD & data empowerment

Data empowerment and data-powered positive deviance can be compatible and complementary in many ways. They share the fundamental belief in people’s agency, their right to have a say in how their data is used and the importance of involving those closest to a problem in identifying solutions. Nevertheless, it’s important to pay close attention to data empowerment principles and practices when running DPPD projects to avoid the danger that increased application of the method results in more extractive, top-down data initiatives.

Read our earlier blog posts if you want to know what we mean by data empowerment or visit the DPPD Medium blog to learn more about the DPPD method, the pilots and the team behind it. You can also get in touch with basma[at]datapoweredpd.org or andi[at]datapoweredpd.org to learn how to apply DPPD.

With thanks to Basma Albanna and Michael Cañares for their comments and suggestions.

*The authors were part of the team that developed and tested the DPPD method with teams in Ecuador, Mexico, Niger and Somalia.

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